esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Construction of Auxiliary Reference Signal  
In output-constrained control based on barrier Lyapunov functions, the constraint parameter should be set large enough. For example, one designs a controller according to the barrier Lyapunov function as follows:  
\[V=\frac{e^{2}}{2(b_{e}^{2}-e^{2})}, \tag{4}\]  
where \(b_{e}\) is the design parameter for implementing system constraint. During controller design, \(b_{e}\) must be set properly to meet \(b_{e}>|e(0)|\). Since \(|e(0)|\) is different in each operation, it is a bit troublesome to set the appropriate value of \(b_{e}\) during controller design. To overcome this problem, an auxiliary signal is formed as follows:  
\[x_{d}^{*}(t)=\left\{\begin{array}{ll}(x(0)-x_{d}(0))\cos^{3}(\frac{\pi t}{2t _{d}})v+x_{d}(t),&t<t_{d}\\ x_{d}(t),&t\geq t_{d}\end{array}\right. \tag{5}\]  
where  
\[\
u=\left\{\begin{array}{ll}1,&t<t_{d},\\ 0,&t\geq t_{d},\end{array}\right. \tag{6}\]  
\(t_{d}>0\) is a design parameter. According to the above construction, we can see that both \(x_{d}^{*}(0)=x(0)\), \(x_{d}^{*}(t_{d})=x_{d}(t_{d})\), \(\dot{x}_{d}^{*}(t_{d})=\dot{x}_{d}(t_{d})\) and \(\dot{x}_{d}^{*}(t_{d})=\dot{x}_{d}(t_{d})\) hold. The construction of such auxiliary signal is the basis of achieving deferred output constraint. For an AUV, when the absolute value of the initial error is large, if the controller is designed according to the conventional method, the control input in the initial stage of operation may be very large, even exceeding the maximum power that the AUV can provide. In the process of controller design, using \(x_{d}^{*}(t)\) instead of \(x_{d}(t)\) as the reference signal is helpful to solve this problem.  
**Remark 3**.: _The purpose of constructing an auxiliary signal lies in the following two points: (i) it brings convenience to deferred constraint control design; (ii) it can make the control input smaller and smoother in the early stage of system operation._
## 5 Evaluation and Discussion
### Sentiment Analysis Procedure  
We employed sentiment analysis to extract insights into user satisfaction and discern general sentiments from the feedback. This involved utilizing a sentiment analysis model to classify feedback sentiment as positive, negative, or neutral, aiding in identifying areas for improvement and directing future chatbot enhancements.
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 3 Results
### Modified FID Evaluation  
The experiments were performed on both case studies, dendrology, and agricultural fields, with the TopoSinGAN and the baseline SinGAN models trained on each input image  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SinGAN**} & \multicolumn{2}{c}{**WGAN**} & \multicolumn{2}{c}{**TopoGAN**} & \multicolumn{2}{c}{**TopoSinGAN**} \\  & _Mean_ & _STD_ & _Mean_ & _STD_ & _Mean_ & _STD_ \\ \hline CREMI & 15.46 & 2.86 & 15.76 & 2.77 & 11.05 & 2.80 & **8.15** & **2.01** \\ \hline \hline \end{tabular}
\end{table}
Table 2: NTC evaluation of TopoSinGAN in comparison with other models on the CREMI dataset.  
Figure 6: The outputs of the SinGAN and TopoSinGAN models trained on a single real image for agricultural fields and dendrology experiments. The red frame highlights the TopoSinGAN results, emphasizing the improved topological accuracy in comparison with SinGAN.  
Figure 7: Sample outputs generated by TopoSinGAN and other GAN models. The single input used for the SinGAN and TopoSinGAN training is displayed on the left, while WGAN and TopoGAN were trained using the CREMI dataset. The red frame highlights the outputs of the TopoSinGAN model, showcasing its topological consistency relative to other models.  
for 10 runs, generating 1000 synthetic images per experiment. The measured average FID values were recorded in Table 3.  
As shown in Table 3, the average FID for the agricultural fields image, TopoSinGAN achieved an average FID value of 0.1914, compared to 0.2485 for the baseline SinGAN. The agricultural fields' image is characterized by significant variations in color, texture, and geometric patterns, leading to inherently higher FID values. The lower FID score for TopoSinGAN suggests that the topologically improved model produced more realistic images compared to SinGAN. Specifically, TopoSinGAN's ability to preserve topological structures while maintaining visual diversity led to a reduction of 0.0571 in FID, demonstrating its effectiveness in generating images with enhanced topological accuracy while retaining natural variation.  
The average FID for the dendrology experiment with TopoSinGAN was 0.0013, while the baseline SinGAN produced a similar value of 0.0014. Given the inherent similarity in the structure and geometry of xylem cells in the dendrology image, small FID values are expected. The similarity between the two models indicates that TopoSinGAN maintains the structural fidelity of the generated xylem images.
## 4 Algorithm
### Demand Dequeue Rules  
Due to the differences in server performance, the speed at which they process access demands varies. The system constraint requires that each access demand be fully processed within a single round, without spanning across rounds or being split. Therefore, in round \(t\), the system will traverse the front of the queue \(Que_{i}\) and calculate the number of access demands \(D_{i}(t)\) that can be processed within this round, provided that the queue is selected for scheduling.  
When the algorithm selects queue \(Que_{i}\), based on the maximum access demand capacity \(\psi_{i}\) that can be processed in a single round and the self-generated capacity \(C\) of the demand at the front of the queue, the number of access demands that can be processed in the current round when each queue is selected can be calculated as follows:  
\[D_{i}(t)=\max\{n\in\mathbb{N}^{+}\Big{|}\sum_{k=1}^{n}C_{k}\leq\psi_{i}\}, \quad i\in\{low,medium,high\} \tag{22}\]  
When the selected queue \(Que_{i}\) is chosen to respond, the access demands at the front of the queue need to be processed in a first-come, first-served manner, with a specified number of demands being dequeued sequentially and the parameter \(N_{i}\) being updated. Each access demand that is executed in response needs to accumulate delay in the parameter \(L_{i}\), and the specific calculation formula is as follows:  
\[L_{i}=\sum_{1}^{D_{i}(t)}T^{out}-T^{in} \tag{23}\]  
In each round, all queues perform the enqueue operation. However, differences exist in the response capabilities of each queue. This study aims to comprehensively consider queue congestion and access demand latency across the entire system, selecting one queue per round for response. This not only ensures the access rate but also effectively mitigates system latency. The specific dequeue process is shown in Algorithm 2.  
```
Parameters:Dequeued quantity \(D_{i}(t)\), number of responded access demands \(N_{i}\), cumulative delay caused by responded access demands \(L_{i}\)
1: Initialize \(L_{i}=0\) for all queues;
2: Initialize \(N_{i}=0\) for all queues;
3:for each round do
4: Select a queue \(Que_{i}\) based on system;
5: Initialize \(count=0\);
6:while\(count<D_{i}(t)\) and \(Que_{i}\) is not empty do
7: Dequeue a demand Dem from the head of \(Que_{i}\);
8: Set dequeue round: \(T^{out}=T^{now}\);
9: Compute response delay: \(L_{i}\gets L_{i}+(T^{out}-T^{in})\);
10: Update the number of responded access demands: \(N_{i}\gets N_{i}+1\);
11:\(count\gets count+1\);
12: Update remaining available capacity of \(Que_{i}\): \(R_{i}-=C\)
13:endwhile
14:endfor
```  
**Algorithm 2** Demand Dequeue Rules
# A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control
## 3 X3D Quadrotor Controller Design
### PID Control System  
PID controllers are the most fundamental feedback controllers that are frequently utilized in many industrial applications [22]. A PID controller calculates an error value that  
Figure 4: Closed-loop Control System for X3D Quadrotor.  
Figure 3: X3D Quadrotor Nonlinear Simulink model.  
distinguishes between the desired set point and the measured process value. By changing the process control inputs, the controller tries to decrease the error. To use the PID controller for achieving ideal values for a better control system, a complete mathematical model of the plant is required to determine the three parameters (proportional gain \(K_{P}\), integral gain \(K_{I}\), and derivative gain \(K_{D}\)) [23]. Six PID controllers for attitude/altitude stabilization and translation trajectories are presented in this research. The attitude angles are controlled by three PID controllers (\(\phi,\theta\)_and_\(\psi\)). The altitude (\(z\)-axis) of the X3D quadrotor is controlled by one PID controller, while the longitudinal and lateral positions (\(x\)-axis and \(y\)-axis) are controlled by two PID controllers. Hence, the attitude is determined by the positions. The Euler angles' preliminary conditions are set to (0, 0, and 0) to begin the experiment. The angular velocity and thrust for the X3D quadrotor are then generated by combining all PID controllers with the combinational control. Figure 5 depicts the PID controller feedback loops for an X3D quadrotor [24]. Equation (13) illustrates the discrete-time transfer function for each PID controller.  
\[PID=\left(K_{P}+Z^{-1}K_{D}+\frac{1}{Z^{-1}}K_{I}\right) \tag{13}\]  
In Equations (14) and (15), the equations for PID attitude control are provided.  
\[e_{atttitude}=\Theta_{reference}-\Theta_{measured}\cdot EBD(\Theta) \tag{14}\]  
\[\Theta_{desire}=e_{atttitude}.PID \tag{15}\]  
Similar to PID for attitude control, PID for height control (\(z-axis\)) and PID for translation position control (\(x-axis\)_and_\(y-axis\)) are calculated. The height (\(z-axis\)) and intended angular velocity are used as inputs in the combination control step, and following saturation, thrust and the actual angular velocity are transmitted to the X3D quadrotor's nonlinear model, as shown in Equations (16)-(18). The classical discrete PID is implemented in all the controller blocks, and the parametric gain values are listed in Table 3.  
\[\omega_{desire}=(\Theta_{desire}-\Theta_{measured}).EBH(\Theta) \tag{16}\]  
\[U_{[X,\ Y,\psi]}=sat\{\omega_{desire}\} \tag{17}\]  
\[U_{Z}=sat\{Z_{desire}\} \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parametric Gain**} & \multicolumn{3}{c}{**Controllers**} \\ \cline{2-4}  & **PID Altitude (Z)** & **PID (X, Y)** & **PID (\(\Phi\), \(\theta\), \(\psi\))** \\ \hline \(K_{P}\) & 1.5 & 2 & 1 \\ \hline \(K_{I}\) & 0 & 0 & 0 \\ \hline \(K_{D}\) & 0.5 & 1 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parametric Gain values of each PID Controllers.  
Figure 5: X3D Quadrotor PID Controllers.
## 2 Materials and Methods
### Thermoelectric Generator Module  
A thermoelectric generator module is an electrical series and a thermal parallel arrangement of n-type and p-type thermoelectric materials in the form of legs. The top and bottom of the legs are joined by conductors and are thermally insulated from the top and bottom by an insulator, i.e., alumina. Heat applied to the top side and the bottom side was kept at a low temperature; due to thermoelectric effects, voltage is produced across the n-type and p-type terminals, and by connecting the load across the terminals, output power and the conversion efficiency could be determined. Figure 1 shows a typical segmented thermoelectric generator module.  
Recently, TEG modules are being designed and simulated in 3D using Comsol-Multiphysics (COMSOL Inc., Burlington, Stockholm, Sweden) or ANSYS engineering simulation and 3D design software. Usually, researchers begin the design and simulation of a unicouuple TEG and later the design is scaled up to a module composed of a number of n-type and p-type legs. [PERSON] et al. carried out a comprehensive design and simulation of a segmented module [44]. However, a PbTeS TE material was used in the design. As lead (Pb) is a toxic element, it needs to be replaced, or another, less toxic, compound should be investigated. Here, skutterudite TE materials are investigated. By understanding the mathematical equations involved in the TEG, modelling and optimization can be achieved. Considering all of the possible thermal transfer losses, thermal and electrical contact resistances are negligible at a steady state, in which the heat is absorbed on the hot side and the heat rejected on the cold side.  
\[Q_{h}=n[\alpha IT_{H}-\frac{1}{2}l^{2}R+K(T_{H}-T_{c})] \tag{1}\]  
\[Q_{C}=n[\alpha IT_{c}+\frac{1}{2}l^{2}R+K(T_{H}-T_{c})] \tag{2}\]  
\[R=L(\frac{1}{\sigma_{pA_{p}}}+\frac{1}{\sigma_{nA_{n}}}) \tag{3}\]  
\[\alpha=\alpha_{p}+\alpha_{n} \tag{4}\]  
\[\kappa=\frac{1}{L}(\kappa_{p}A_{p}+\kappa_{n}A_{n}) \tag{5}\]  
\[I=\frac{\alpha(T_{H}-T_{c})}{R+R_{L}} \tag{6}\]  
\[P_{o}=Q_{h}-Q_{C}=l^{2}R_{L} \tag{7}\]  
\(Q_{h}\) is the power absorbed at the hot junction, \(Q_{C}\) is the power released at the cold junction, \(R\), \(\alpha\), and \(\kappa\) are the internal resistance, Seebeck coefficient, and thermal conductivity of a unicouple, respectively. \(I\) is the electric current passing through the device, \(P_{o}\) is the output power, and \(R_{L}\) is the load resistance. \(A\) is the cross sectional area of the leg. Usually, n-type and p-type legs have equal lengths \(L\), and \(n\) is the number of couples in a module. \(T_{H}\) is the hot side temperature and \(T_{c}\) is the cold side temperature.
# Predicting Runoff from the Weigan River under Climate Change
## 3 Results
### Evaluation of Model Simulation Results  
In this study, the period 1961-1964 was selected as the model warm-up period, the period 1965-1989 as the model calibration period, and the period 1990-2014 as the model validation period. The monthly runoff of the study area in the last 50 years was simulated to recreate its hydrological process in order to construct a SWAT model applicable to the upper watershed of the Weigan River outflow. It should be noted that the measured runoff from the Heizi Reservoir (II) hydrological station since August 1991 is affected by the regulating effect of the Kizil Reservoir, and the dam site section and the Heizi Reservoir (II) hydrological station are close to each other, with a distance of only about 1 km. Therefore, in this study, when simulating and verifying the SWAT model upstream of the Weigan River outlet, the inflow flow of the Kizil Reservoir was used to replace the flow data measured at the Heizi Reservoir (II) hydrological station in the same period. The best simulation results of the model could be obtained after the model was adjusted and run.  
From the simulation results of the multi-year monthly average flow (Figure 5), it can be seen that the difference between the simulated runoff value and the measured runoff value is relatively small, and the two runoff series' curves are well-fitted. This indicates that the constructed SWAT model for the upper reaches of the Weigan River outflow can relatively accurately describe the annual runoff process in this study area. And from the evaluation of the model's simulation results of monthly runoff for each time period (Table 2), it can be seen that the NSE coefficient is \(>\)0.75, the RSR is \(<\)0.50, the PBIAS is \(<\)\(\pm\)10%, and their simulation results are excellent. This indicates that the SWAT model has good applicability in the study area and can be used to predict future runoff evolution trends in the next step.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time Period** & \begin{tabular}{c} **Actual** \\ **Runoff/108** \\ **m\({}^{3}\) a\({}^{-1}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Simulate** \\ **Runoff/108** \\ **m\({}^{3}\) a\({}^{-1}\)** \\ \end{tabular} & **NSE** & **RSR** & **PBIAS/\%** & **R\({}^{2}\)** &
\begin{tabular}{c} **Simulation** \\ **Results** \\ \end{tabular} \\ \hline Calibration Period & & & & & & & \\ (I January 1965–31 & 24.19 & 22.77 & 0.75 & 0.50 & 5.91 & 0.82 & Good \\ December 1989) & & & & & & & \\ Validation Period & & & & & & & \\ (I January 1990–31 & 26.70 & 24.84 & 0.83 & 0.41 & 6.98 & 0.86 & Good \\ December 2014) & & & & & & & \\ Whole Period & & & & & & & \\ (I January 1965–31 & 25.47 & 23.82 & 0.80 & 0.45 & 6.47 & 0.84 & Good \\ December 2014) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of monthly runoff simulation results by time period based on the \(r\)-MME data.  
Figure 5: SWAT model’s monthly average flow simulation results for the 1965–2014 historical period.
### Calculation of DC Circuit Capacitance  
The previous calculations were made keeping the capacitor capacity infinite. In a real VFD, which will perform reactive power compensation function in addition to the main functions, real capacity calculations are required. The voltage \(U_{c}\) in the capacitor of the VFD DC circuit can vary only in the small range of \(\Delta U_{c}=1\div 3\%\). The equations below are based on the fact that the time interval during which the VFD inverter and the induction motor discharges the capacitor is about \(\Delta t=0.007\) s.  
\[\Delta Q=C\Delta U_{c} \tag{22}\]  
\[\Delta Q=i\times\Delta t=\frac{P}{\sqrt{2}}\Delta t \tag{23}\]  
Comparing the equations gives:  
\[C=\frac{P\times\Delta t}{\sqrt{2}\times U\times\Delta U_{c}} \tag{24}\]  
The following equation combines the internal resistance of the PDL and the capacitance of the VFD DC circuit. The capacitor must not be fully discharged until the PDL voltage is higher than the VFD DC circuit voltage, thus creating an inequality:  
\[\tau=RC\geq t_{\text{Charge}} \tag{25}\]  
The pulse displacement angle \(\alpha\) of the VFD input current varies within \(20\div 30^{\circ}\), so \(t_{\text{Charge}}=2\alpha=\)3.3 ms. Assuming a variation of \(\Delta U_{c}\) of \(1\div 3\%\), it is calculated that the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Test** & **\(\omega L\), \(\Omega\)** & \(\alpha_{r}\)\(  
capacity of a 1 kW VFD DC circuit should be between 5500 and 1830 \(\mu\)F. Calculating the time constant \(T_{RC}\) at the PDL resistance \(R=1\)\(\Omega\), it is obtained that it varies within 0.0018 s \(\div\) 0.0055 s. This time constant \(T_{RC}\) value is close to the required value.  
The calculated VFD DC circuit capacitance values are indicative and will be specified in further experimental studies.
# A Spaceborne Mounting Method for Fixing a Cubic Fabry-Perot Cavity in Ultra-Stable Lasers
## 4 Conclusions  
In summary, a spaceborne mounting method for fixing a 100 \(\times\) 100 \(\times\) 100 mm cubic FP cavity to USLs was proposed. A system, including an FP cavity in front of and behind the FP-cavity optical system and vacuum pump, was built based on the proposed method. Aerospace vibration tests of the whole system were carried out. To validate whether there was an offset or not during the vibration tests, three criteria were also proposed. The differences in the results for the whole system before and after the aerospace vibration tests were negligible for the three criteria. Thus, the proposed mounting method could be a good way to fix cubic FP cavities to USLs for space applications.  
Conceptualization, L.M., Y.X. and L.C.; methodology, F.M. and C.X.; validation, J.D. and L.M.; formal analysis, P.Z.; investigation, P.Z.; resources, T.L., J.J., S.Z. and J.W.; data curation, P.Z. and X.Y.; writing--original draft preparation, P.Z.; writing--review and editing, L.M.; visualization, W.B.; supervision, J.J., H.Q. and J.W.; project administration, W.B.; funding acquisition, L.M., L.C. and H.Q. 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, grant number 2020 YFC2201300, and the National Natural Science Foundation of China, grant numbers 12103014 and 11903041.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] and [PERSON] for assembling the FP cavity system at the Aerospace Information Research Institute, Chinese Academy of Sciences.  
The authors declare no conflict of interest.
# Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods
## 2 Wastewater from the Paint Industry  
### Paint  
Approximately 70% of the paint industry wastewater is discharged untreated into natural river basins [4]. All paint types contain chromophores, which are responsible for the color of the paint. However, paints differ in terms of chemical structure, process of dyeing, solubility, and the field of application. Most paints consist of similar components, a mixture of pigments, which are suspended solids, in a vehicle (liquid medium), a volatile solvent, a binder, which is a polymeric material, extenders, and suitable additives [9; 10; 11] as shown in Figure 2.  
Figure 1: The characteristics of industrial wastewater.  
The pigments are natural or synthetic compounds and can be classified by the production methods, their chemical structure, or their applications [13; 14]. The pigment is a natural or artificial powdered color substance (organic or inorganic) dissolved in water, alcohol, or oils that colors the substrate. The neutral, most often white mineral pigments are made from barite, chalk, kaolin, or gypsum. Organic pigments are either natural, from plant or animal sources (e. g. saffron flowers, Brazilian purple, carmine), or produced synthetically. Synthetic organic pigments, such as dioxazine, beta naphthol, azo and phthalocyanine, are hydrocarbon compounds made from petroleum, base, and chemicals, primarily by exposure to high temperatures or pressure. Many synthetic organic pigments, especially the azo pigments, are derived from water soluble dyes. Dyes on their own bind chemically to materials in a way that prevents them from being edited after they are applied. Pigment grain size can vary from very small, up to 1 \(\upmu\)m, to large, with over 10 \(\upmu\)m. For example, zinc oxide, zinc sulfide, lithopone, and titanium dioxide are the components of basic white pigments, black pigment contains elemental carbon. The color of red pigments is made up of minerals such as iron oxide, cadmium, cuprous oxide, and various synthetic organic pigments [15; 16].  
The binding of the pigments is carried out by the binder or vehicle, but also oils, proteins, waxes, resins, or polymers, dissolved in a diluent that dries to a hard film are used [17]. Diluents applied in the paint industry are either organic solvents or water, depending on the consumer needs.  
For saving, binder extenders are used, which are larger pigment particles. These particles improve the adhesion and strengthen the film. The physicochemical properties of the paints are adjusted by using different fillers and additives, such as chalk, gypsum, driers, stabilizers, bactericidals, fungicides, etc. [12].