esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 2 Materials and Methods
### Model Linearization
#### 2.2.2 Coupled Tank Model Linearization  
The linearization of the CT nonlinear model provided with relation (5) can be performed using the Taylor series method [5, 6, 7]. Specifically, using the mentioned method around the stationary state, it can be written as follows:  
\[\begin{array}{l}\sqrt{H_{1}}\approx\sqrt{H_{1s}}+\frac{1}{2\sqrt{H_{1s}}}(H_{ 1}-H_{1s}),\\ \sqrt{H_{2}}\approx\sqrt{H_{2s}}+\frac{1}{2\sqrt{H_{2s}}}(H_{2}-H_{2s}),\\ \sqrt{H_{1}-H_{2}}\approx\sqrt{H_{1s}-H_{2s}}+\frac{1}{2\sqrt{H_{1s}-H_{2s}}}[ (H_{1}-H_{2})-(H_{1s}-H_{2s})].\end{array} \tag{7}\]  
Moreover, the following would be valid:  
\[\begin{array}{l}0=Q_{i1s}-\alpha_{1}\sqrt{H_{1s}}-\alpha_{3}\sqrt{H_{1s}-H_{ 2s}},\\ 0=Q_{i2s}-\alpha_{2}\sqrt{H_{2s}}+\alpha_{3}\sqrt{H_{1s}-H_{2s}}.\end{array} \tag{8}\]  
After inserting (7) into (5), and by subtracting (8) from the obtained expression, it is be written as follows:  
\[\begin{array}{l}A_{1}\frac{dH_{1}}{dt}=(Q_{i1}-Q_{i1s})-\frac{\alpha_{1}}{2 \sqrt{H_{1s}}}(H_{1}-H_{1s})-\frac{\alpha_{3}}{2\sqrt{H_{1s}-H_{2s}}}[(H_{1}- H_{2})-(H_{1s}-H_{2s})],\\ A_{2}\frac{dH_{2}}{dt}=(Q_{i2}-Q_{i2s})-\frac{\alpha_{2}}{2\sqrt{H_{2s}}}(H_{2}- H_{2s})+\frac{\alpha_{3}}{2\sqrt{H_{1s}-H_{2s}}}[(H_{1}-H_{2})-(H_{1s}-H_{2s})]. \end{array} \tag{9}\]  
By introducing a substitution,  
\[\begin{array}{l}q_{i1}=Q_{i1}-Q_{i1s},q_{i2}=Q_{i2}-Q_{i2s},\\ h_{1}=H_{1}-H_{1s},\,h_{2}=H_{2}-H_{2s},\\ h_{1}-h_{2}=(H_{1}-H_{2})-(H_{1s}-H_{2s}),\end{array} \tag{10}\]  
It can be written as follows:  
\[\begin{array}{l}A_{1}\frac{dh_{1}}{dt}=q_{i1}-\frac{\alpha_{1}}{2\sqrt{H_{1s }}}h_{1}-\frac{\alpha_{3}}{2\sqrt{H_{1s}-H_{2s}}}(h_{1}-h_{2}),\\ A_{2}\frac{dh_{2}}{dt}=q_{i2}-\frac{\alpha_{3}}{2\sqrt{H_{2s}}}h_{2}+\frac{ \alpha_{3}}{2\sqrt{H_{1s}-H_{2s}}}(h_{1}-h_{2}).\end{array} \tag{11}\]  
Figure 3: SISO nonlinear tank system.  
Introducing the substitution (12),  
\[\begin{array}{l}T_{11}=\frac{A_{1}}{\frac{A_{2}}{2\sqrt{H_{15}}+ \frac{A_{3}}{2\sqrt{H_{15}}-H_{25}}}},\ T_{21}=\frac{A_{2}}{\frac{A_{3}}{2\sqrt {H_{25}}+\frac{A_{3}}{2\sqrt{H_{15}}-H_{25}}}},\\ k_{1}=\frac{-\frac{A_{1}}{2\sqrt{H_{15}}}+\frac{1}{2\sqrt{H_{15}}-H_{25}}}{k_{2 }},\ k_{2}=\frac{2}{\frac{A_{2}}{2\sqrt{H_{25}}+\frac{A_{3}}{2\sqrt{H_{15}}-H_{ 25}}}},\\ k_{12}=\frac{\frac{A_{3}}{2\sqrt{H_{15}}-H_{25}}}{\frac{2\sqrt{H_{15}}+\frac{A_ {3}}{2\sqrt{H_{15}}-H_{25}}}},\ k_{21}=\frac{\frac{A_{3}}{2\sqrt{H_{15}}-H_{25} }}{\frac{2\sqrt{H_{15}}+\frac{A_{3}}{2\sqrt{H_{15}}-H_{25}}}},\end{array} \tag{12}\]  
relation (11) in the Laplace domain is written as follows:  
\[\begin{array}{l}h_{1}(T_{11}s+1)=q_{i1}k_{1}+k_{12}h_{2},\\ h_{2}(T_{21}s+1)=q_{i2}k_{2}+k_{21}h_{1},\end{array} \tag{13}\]  
which is a linearized MIMO CT model.
## 3 The Multi-Material Topology Optimization Problem
### The Topology Optimization Problem  
For the sake of simplicity, the design variable matrix introduced in Equation (1) will henceforth be reformatted into a column vector, denoted by \(\mathbf{p}\), defined as  
\[\mathbf{p}=[\mathbf{p}_{1},\mathbf{p}_{2},\ldots,\mathbf{p}_{NM}]^{T}, \tag{24}\]  
where each block \(\mathbf{p}_{i}\) corresponds to the design variables associated with the \(i\)-th material phase. The typical \(\mathbf{G}\)-based optimal design problem may then be formulated as follows:  
\[\left\{\begin{array}{ll}\min_{\mathbf{p}}\mathcal{F}(\mathbf{p})&=\ \ ||\mathbf{G}(\mathbf{p})||\\ \\ \text{s.t.}&\mathbf{G}(\mathbf{p})=\mathbf{C}\mathbf{K}^{-1}(\mathbf{p})\mathbf{B}\\ \\ &V_{i}(\mathbf{p}_{i})\leq V_{\text{max}i},\ \ i=1,\ldots,NM\\ \\ &\mathbf{p}_{\text{min}}\leq\mathbf{p}\leq\mathbf{p}_{\text{max}}\end{array}\right., \tag{25}\]  
where:  
* (25)\({}_{1}\) defines the objective function \(\mathcal{F}(\mathbf{p})\) to be minimized;
* (25)\({}_{2}\) provides the explicit definition of the input/output transfer matrix, highlighting its dependence on the design variable vector \(\mathbf{p}\);
* (25)\({}_{3}\) imposes a standard volume constraint on each individual material. In the present formulation, all materials are subject to the same volume constraint, although non-uniform constraints may also be considered;
* the inequalities on the design variables in (25)\({}_{4}\) are to be interpreted component-wise. As is customary, each design variable is bounded between zero (void) and one (full material).  
The optimization problem in (25) is solved numerically using the Method of Moving Asymptotes (MMA) [23]. To this end, the user must supply the current values of the objective function and the constraints, together with their sensitivities with respect to  
Figure 1: System Representation in Standard Input/Output Form.  
the design variables. When the objective function to be minimized is a matrix norm that depends on the singular values of the transfer matrix, a semi-analytical computation of the sensitivities is required. Specifically, using the chain rule, one must compute the derivatives of both the transfer matrix and its singular values with respect to each design variable, denoted generically by \(p_{k}\). Further details on this procedure are provided below.
# Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles
## 5 Example Analysis
### Results of the Algorithm
#### 5.2.2 Distribution of Objective Function Values  
The optimal scheduling of the virtual power plant incorporating electric vehicles is solved by the non-dominated ranking genetic algorithm, and the distribution of the economic objective function values is shown in Figure 12, the distribution of the environmental objective function values is shown in Figure 13, and the distribution of the peak-shaving objective function values is shown in Figure 14.
## 3 Strength Characterization
### Analysis of the Importance of Influencing Factors  
The results of the test were analyzed using the variance method to determine the primary and secondary levels of importance of the individual influencing factors, and the results are presented in Table 9.  
The analysis of the variance table suggests that the F-values of the influencing factors in descending order are as follows: curing age (C) > cement content (B) > slurry concentration (A). The significant effects of Factor B and Factor C indicate that the curing age and cement content are the main factors influencing the strength of the filler, the most significant of which is the curing age; the effect of Factor A is not significant, which means that concentration is a minor factor affecting the strength of the filler.
## 4 Results and Discussions
### Parametric Assessment Results  
The resulting parametric results for carbon emissions and production cost variations are shown in Figure 8a,b. The results indicate that transportation distance (S4) and the power of the shredding machine used to process cardboard and textile fibres (S5) are the most significant variables influencing total carbon emissions. Electricity consumption for fibre processing and transportation capacity has little to no impact on total carbon emissions. At the scale of the current case study, optimizing the S4 and S5 parameters could reduce total carbon emissions by approximately 1%.  
Production cost variations for the indicators considered are highlighted in Figure 8b. Similar to the carbon emission results, transportation distance (S4) and shredding machine power (S5) are the most influential factors affecting total production costs. Therefore, optimizing the textile fibre processing and reducing transportation distance can further decrease both carbon emissions and production costs of the sustainable mixes.  
Further optimization studies are recommended for large-scale implementation of Tex-crete, focusing on the need for additional processing infrastructure and addressing potential regulatory barriers prior to widespread industry implementation.  
Figure 8: Parametric assessment results for carbon emissions and production costs.
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
###### Abstract  
We evaluated the uncertainty associated with secondary pollutants formation due to different chemical mechanisms in photochemical modelling. The CMAQ modelling system was utilized in conjunction with CB6R3, SAPRC07, and RACM2 chemical mechanisms and compared the concentrations of various chemical species, including ozone (O3) and particulate matter (PM2.5). Using datasets from ambient monitoring stations, we assessed the performance of each of the mechanism in summer and winter. The concentrations of various chemical species predicted by the three mechanisms varied significantly. The differences are more evident in summer than in winter for most of the species, except for hydrogen peroxide (H2O2), methyl hydroperoxide (MEPX), and Secondary Organic Aerosol--Anthropogenic. We observed that the summer daytime O3 predictions showed reasonable peaks at the three air quality monitoring sites, but the nighttime values underpredicted for all three mechanisms. In the winter, all three mechanisms tend to under-predict O3. Differences in the mean O3 values (bias) at the different sites, for the different seasons, are consistent with corrections made to previous modelling studies that modified KZMIN. PM2.5 predictions with RACM2 were slightly better. The dominant PM2.5 species in summer were sulfate and SOA-Bio, which may be attributed to non-mobile sources in the region, while NO3 became dominant in winter due to more favorable conditions for forming this species, including lower temperatures and an elevated NH4 to SO4 ratio. We concluded that the differences in O3 and PM2.5 predictions between the three mechanisms are significant, implying that when developing strategic and management actions are based on modelling, the most appropriate mechanism should be considered.  
CMAQ; chemical mechanisms; O3; PM2.5 +
Footnote †: journal: Appl. Sci. 2022, 12, 8576. [[https://doi.org/10.3390/app12178576](https://doi.org/10.3390/app12178576)]([https://doi.org/10.3390/app12178576](https://doi.org/10.3390/app12178576))
## 2 Material and Methods
### Photovoltaic (PV) Set Definitions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Characteristics** & **Efficiency** & **Specification** \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [25]} & Full-bridge inverter & & Converter. CC-CA \\  & Single-stage inverter & & Input: 80 Vdc \\  & C-CA isolated & 90.0\% & Output: 220 Vac \\  & Micro-inverter & & Potency: 500 W \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [28]} & Multistage inverter (7 stages) & & Converter. CC-CA \\  & Non-isolated & 94.9\% & Output: 110 Vac \\  & Micro-inverter & & Potency: 500 W \\  & Micro-inverter & & Switching: 15.3 kHz \\ \hline \multirow{4}{*}{[PERSON] et al. [27]} & Full-bridge inverter & & Converter. CC-DC \\  & Isolated & 95.0\% & Output: 200 Vdc \\  & Micro-inverter & & Potency: 200 W \\  & & & Switching: 100 kHz \\ \hline \multirow{4}{*}{[PERSON] et al. [28]} & Buck-boost converter & & Converter. DC-DC \\  & Non-isolated & 95.5\% & Output: 380 Vdc \\  & Central inverter & & Potency: 5000 W \\  & & & Switching: 25 kHz \\ \hline \multirow{4}{*}{[PERSON] e [PERSON] [29]} & Fly back & & Converter. DC-DC \\  & Isolated & 96.0\% & Output: 380 Vdc \\  & Micro-inverter & & Potency: 180 W \\  & & & Switching: 50 kHz \\ \hline \multirow{4}{*}{[PERSON] et al. [30]} & Half-bridge inverter & & Converter. DC-AC \\  & Non-isolated & 96.2\% & Output: 220 Vac \\  & Micro-inverter & & Potency: 149.5 W \\  & & & Switching: 20 kHz \\ \hline \multirow{4}{*}{[PERSON] et al. [31]} & Half-bridge inverter & & Converter. DC-DC \\  & Non-isolated & 96.5\% & Output: 80 Vdc \\  & Micro-inverter & & Potency: 500 W \\  & & & Switching: 100 kHz \\ \hline \multirow{4}{*}{[PERSON] et al. [32]} & Resonator converter & & Converter. DC-DC \\  & Isolated & 97.5\% & Input: 40.80 Vdc \\  & Micro-inverter & & Potency: 370 W \\  & & & Switching: 50 kHz \\ \hline \multirow{4}{*}{[PERSON] et al. [30]} & Half-bridge inverter & & Converter. DC-AC \\  & Non-isolated & 96.2\% & Output: 220 Vac \\  & Micro-inverter & & Potency: 149.5 W \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of solar inverters: technology vs. efficiency. graphic, (ii) constructive parameter and installation mistakes. Table 3 presents the impact of each factor on power generation. These factors are relevant for selecting the most suitable set of PV panels, inverters, and other devices for a solar microgrid. Additionally, it is essential to establish how this system will be installed and the maintenance guidelines to be provided to the end user to extract the maximum performance throughout the entire life cycle of the PV system.
## 3 Results and Discussion  
### Future Climate Change  
#### 3.1.1 Change Characteristics of Precipitation  
Based on the precipitation prediction results, the average precipitation in the study area is projected to be 218.9 mm for the short-term period (2021-2040) and 226.9 mm for the long-term period (2041-2060). These values are 16.7 mm and 24.7 mm higher, respectively, than the average precipitation in the current period (2001-2020), indicating an increase in annual precipitation in the study area. Figure 4 illustrates the monthly average precipitation forecast for different periods. The monthly average precipitation follows a similar trend throughout the year, with the highest amount occurring in August. This suggests that future climate changes have minimal influence on the annual distribution of precipitation. Analyzing the monthly precipitation, there is an increase in precipitation during February, April, June-August, and October-November. The largest increase is observed in July, with a short-term increase of 6.1 mm and a long-term increase of 8.9 mm. Precipitation in January, March, May, September, and December decreases, though the decrease is not significant in most months.
# Phytoplankton as CO\({}_{2}\) Sinks: Redirecting the Carbon Cycle
## 4 Mitigating Fossil Energy Dependence by Enhancing Photosynthesis
### Fossil Fuel Reserves and Potential Evolution  
Coal reserves at the end of 2020 (sum of anthracite, bituminous, sub-bituminous and lignite) are estimated at 1170 Gt, equivalent to an average of 8.35 \(\times\) 10\({}^{6}\) TWh of primary energy (28 MJ/kg). Gas reserves are estimated at 144 Gt, equivalent to 2.3 \(\times\) 10\({}^{6}\) TWh of energy (55 MJ/kg). Finally, oil reserves are estimated at 245 Gt, equivalent to 3 \(\times\) 10\({}^{6}\) TWh of energy (45 MJ/kg). From these figures, the total fossil fuel reserves can be estimated at 13.7 \(\times\) 10\({}^{6}\) TWh [76]. Taking into account the current consumption of energy from fossil fuels (1.36 \(\times\) 10\({}^{5}\) TWh) and applying an annual increase of 1 \(\times\) 10\({}^{3}\) TWh [76], these reserves (as a single block) will be exhausted in approximately 75 years (Figure 8).  
During this time-lapse, 4330 Gt of CO\({}_{2}\) will be emitted into the atmosphere, of which 58% will be theoretically absorbed by natural mechanisms (Figure 1). Therefore, in 2100, approximately 1820 Gt of CO\({}_{2}\) will remain in the atmosphere, increasing the concentration of this gas to over 650 ppm. The effects of this rapid increase in CO\({}_{2}\) concentration at the global level are still uncertain. However, it seems evident that an increase in global temperature will be one of the most direct consequences of this anthropogenic perturbation. Based on the current trend, it is predicted that at the time of fossil fuel depletion, the planet's average temperature will be above 4.0 \({}^{\circ}\)C compared to that observed in 1800 [77]. Other potential consequences could include recurring and intense heat waves, extreme weather, changes in precipitation patterns, rising sea levels, changes in crop productivity, loss of biodiversity, and impacts on human health and the economy. So far, most of the international measures being implemented to front this challenge are focused almost exclusively on reducing CO\({}_{2}\) emissions, both from the burning of fossil fuels as well as the anthropic use of the land, and the implementation and promotion of clean energy sources such as solar, wind, tides, and even nuclear fusion. However, these measures have not been developed fast enough to stop this process [78].  
Figure 8: Simulation of the possible evolution of fossil fuel reserves (red dashed line) and the atmospheric concentration of CO\({}_{2}\) maintaining the annual consumption and emission rate increment (black line) throughout the next century. The pink, green, and blue dashed lines suggest the impact of several amounts of biological capture of atmospheric carbon on the CO\({}_{2}\) accumulation.
## 3 Flexible Merging Decision Methods
### Incomplete Information Static Game Models
#### 3.1.2 Payoff Function  
The payoff function decides whether the result of the game is reasonable or not. The payoff function consists of four parts, namely timeliness payoff \(R_{time}\), safety payoff \(R_{safe}\), comfort payoff \(R_{comf}\), and economics payoff \(R_{eco}\).  
(1) Timeliness payoff \(R_{time}\)  
\(R_{time}\) is measured by the difference between the vehicle's desired speed and its current momentary speed--the greater the difference, the higher the gain. The \(R_{time}\) formula is shown in Equation (2):  
\[R_{time}=\frac{(v_{max}-v_{min})+(\overline{v}-v)}{2(v_{max}-v_{min})} \tag{2}\]  
where \(v_{max}\) is the maximum speed and \(v_{min}\) is the minimum speed.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Allow** & **Not Allow** \\ \hline Merge & CAVs go first & Risk of collision \\ Not Merge & Deadlock & MDVs go first \\ \hline \end{tabular}
\end{table}
Table 1: Game outcomes.  
(2) Safety payoff \(R_{safe}\)  
During merging, vehicles need to maintain a safe distance from the gaming opponent. Therefore, \(R_{safe}\) is measured by the displacement difference between the two vehicles, and the greater the distance, the safer it is. The distance \(D\) and the minimum distance \(D_{min}\) between two vehicles are shown in Equations (3) and (4), respectively:  
\[D(t)=\left|L_{CAV}-L_{MDV}-\int_{t_{i}}^{t_{i}+th}v_{CAV}(t)-v_{MDV}(t)dt\right| \tag{3}\]  
\[D_{min}=\underset{t}{\text{min}}D(t) \tag{4}\]  
where \(L_{CAV}\) and \(L_{MDV}\) indicate the distance of CAV and MDV to the end of the acceleration lane, respectively; \(v_{CAV}(t)\) and \(v_{HDV}(t)\) are speed functions of CAV and MDV, which are decided by strategy \(S\); \(t_{i}\) is the current decision time; and \(th\) is the estimated lane-changing time interval.  
\(R_{safe}\) is equal to  
\[R_{safe}=\left\{\begin{aligned} -1,& D_{min}<d_{1}\\ \frac{D_{min}-d_{1}}{d_{2}-d_{1}},& D_{min}\in[d_{1},d_{2}]\\ 1,& D_{min}>d_{2}\end{aligned}\right. \tag{5}\]  
where \(d_{1}\) and \(d_{2}\) are the minimum and maximum safety passing distances.  
(3) Comfort payoff \(R_{comf}\)  
The calculation of \(R_{comf}\) consists of two parts, \(R_{comf}=R_{comf}^{acc}+R_{comf}^{speed}\), which are the discomfort caused by the change in acceleration and the change in speed, respectively, as shown in Equations (6) and (7):  
\[R_{comf}^{acc}=\left\{\begin{aligned} &\underset{\frac{s_{t}-s_{t}-s_{t}-s_{t} }{d_{1}}}{\frac{s_{t}-s_{t}-s_{t}}{d_{1}}},& a_{t}\geq a_{t- \Delta t}\\ &\frac{\frac{s_{t}-s_{t}\Delta t}{d_{1}}-\underset{\frac{s_{t}-s_ {t}}  
In summary, the overall payoff is  
\[R=w_{1}f(R_{time})+w_{2}f\Big{(}R_{safe}\Big{)}+w_{3}f\Big{(}R_{comf}\Big{)}+w_{4} f(R_{cco}) \tag{10}\]  
where \(w_{1},w_{2},w_{3},w_{4}\) are the weights of \(R_{time}\), \(R_{safe}\), \(R_{comf}\), and \(R_{cco}\). \(f(*)\) is the normalization process, mapping \(R\) between 0 and \(1;f(*)=\frac{s-w_{min}}{s_{max}-s_{min}}\).
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 6 Evaluation
### Experimental Settings  
Because of the large number of description files and the number of queries, we chose AES as the encryption algorithm for the description files. In addition, we used HMAC-SM3 with 256 bits key to instantiate the pseudo-random hash function \(prf_{k}()\). For the Hash function used to validate data on the blockchain, we used SHA-256, the same as Ethereum.  
In our experiments, we used Java language to implement all programs. The client-side was an Inter i7-6700 HQ 2.6 GHz computer with 16 GB RAM running Windows 10. The fog node was simulated by using the Linux CentosOS 7. As for the blockchain, we used the FISCO BCOS [27] consortium chain as the backbone of the blockchain and the system environment was also CentOS7. The compiler was the WeBASE IDE and the language was Solidity 0.4.24 (0.4.24 is upward compatible up to version 0.5).
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 3 Modeling and Parameter Identification
### Parameters Identification
#### 3.3.1 \(t_{mc}\)  
The most significant difference between the stepper motor model in this paper and those in previous papers lies in Equation (1), which allows us to ignore those hard parameters inside the stepper motor, such as phase current, back EMF and magnetic flux. Only one parameter, \(t_{mc}\), needs to be considered. The rotation velocity of the magnetic field of stator of stepper motor cannot be measured directly and is obtained indirectly through measurements of phase current of stepper motor. The command rotation velocity can be easily obtained by measuring the pulse input to the stepper motor. By comparing these two velocities, \(t_{mc}\) is estimated to be about 1 ms.
## 5 Conclusions and Future Work  
In this paper, a novel cumulative QoE model is proposed. This model successfully and effectively incorporates the impacts of human-related influence factors to predict the cumulative perceived video quality. In different scenarios, the proposed model achieved impressive performance, outperforming the reference model. Additionally, it was shown that the introduced memory weight accurately mimicked human memory during a streaming session, especially when unpleasant events repeatedly occurred. Besides, the user's interest in video content was found as a potential influence factor in predicting QoE. However, the correlation between DoI and subjective overall QoE was not so high due to the small number of subjects involved in our experiment. For future work, the influence of DoI will be further investigated. In addition, the proposed cumulative QoE model will also be evaluated in multiple databases to understand how well the model will perform across diverse scenarios of video streaming.  
Conceptualization, T.N.D., C.M.T. and P.X.T.; methodology, T.N.D., C.M.T. and P.X.T.; writing--original draft preparation, T.N.D. and C.M.T.; writing--review and editing, P.X.T. and E.K.; and supervision, E.K. and P.X.T.  
This research received no external funding.  
The authors declare no conflict of interest.