esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 4 Experiments
### Software Requirements Analysis in Nuclear and Automotive Domain
#### 4.1.2 Requirements for Cruise Control Functions of Automobiles
Program parameters:  
SpeedMin: 30.0 Km/h;  
SpeedMax: 150.0 Km/h;  
SpeedInc: 2.5 Km/h;  
Kp: 8.113;  
Ki: 0.5;  
ThrottleSatMax: 45.0 percent;  
PedalsMin: 3.0 percent. 8. Functional requirement:  
8.1, Cruise Control behavior  
CC_HLR_CCB_01:  
When the driver starts the car, the CC shall be off. The output CruiseState should be set to OFF  
CC_HLR_CCB_02:  
The CC shall be set on when the driver pushes the ON button. CC_HLR_CCB_03:  
The CC shall automatically go off when the OFF button is pressed. CC_HLR_CCB_04:  
If the car speed is at the speed limit and the accelerator pedal is not pressed, the CC shall be on and regulate the car speed. The output CruiseState shall be set to ON. CC_HLR_CCB_05:  
The CC system shall be automatically disabled when the accelerator pedal is pressed, or the car speed is outside the speed limit. The output CruiseState shall be set to STDBY. CC_HLR_CCB_06:  
The system shall return to the ON state when both the accelerator pedal is not pressed, and the car speed is inside the speed limit. CC_HLR_CCB_07:  
The CC shall be immediately interrupted when the brake is pressed. The output CruiseState shall be set to INT. CC_HLR_CCB_08:  
The system shall resume either to the ON or STDBY states, depending on the Accelerator pedal and the Speed value when the resume button is pressed. The last set cruise speed shall be re-used. 8.2, Car driving control  
CC_HLR_CC_01:  
When the CC is off, the car speed shall be driven using the accelerator pedal. CC_HLR_CDC_02:  
When the CC is on, the car speed shall be automatically regulated. CC_HLR_CDC_03:  
The regulation shall be performed using a proportional and integral algorithm, with Kp and Ki factors. CC_HLR_CDC_04:  
The regulation algorithm shall be protected against the overshoot of its integral part: the integral action shall be reset when the CC is going on, and frozen when the throttle output is saturated. CC_HLR_CDC_05:  
The throttle command shall be saturated at ThrottleSatMax when automatically regulating, in order to limit the car acceleration for the comfort. 8.3, Cruise Speed management  
CC_HLR_CSM_01:  
The Cruise Speed shall be managed only when the cruise control is enabled, meaning ON, STDBY, or INT states. CC_HLR_CSM_02:  
The Cruise Speed shall be set to the current speed when the set button is pressed. CC_HLR_CSM_03:  
The Cruise Speed shall be increased by SpeedInc km/h when the QUICKACCEL button is pressed. Only if this new value of the Cruise Speed is still lower than the maximal speed SpeedMax km/h.
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 3 Power Performance Monitoring Model
### Model Construction  
The 28 base variables, along with their corresponding 28 \(\times\) 7 derived variables mentioned in Section 2.1, serve as the initial inputs. Following feature extraction and KPCA-based dimensionality reduction, nine principal components are identified as the input variables for evolution. The wind turbine active power regression prediction model is trained utilizing these nine evolutionary inputs with the AdaBoost method.  
A 2.0 MW wind turbine, which has been operational since its connection to the grid in December 2020, serves as a representative sample. According to the operation records and field observations, it consistently achieves superior power generation levels with high reliability. Using the operating data of the sample wind turbine from January 2021 to December 2021 as the training set and the data from January 2022 to June 2022 as the test set, the prediction results of the active power intercepted during 00:00-6:00 on 1 January 2022 are presented in Figure 5.  
The prediction error based on the proposed method is significantly lower than the classic LinearRegression model based on the results across all test set predictions. Moreover, with the \(R\)-squared = 0.966 for the AdaBoost regression model and \(R\)-squared = 0.929 for the LinearRegression model, the AdaBoost model provides a superior fit to the data. The \(p\)-value of the residual F-test is 2.82 \(\times\) 10\({}^{-30}\) (<0.05), which proves the statistical significance and practical applicability of the model.  
The performance change in wind turbines is further quantified by evaluating the daily power generation performance score \(K_{T}\), which is computed using the calculation method presented in Equation (12). A higher score signifies the superior generation performance of the wind turbine.  
\[K_{T}=\frac{1}{N}{\sum_{n=1}^{N}\left(P_{pre}-P_{real}\right)/P_{real}\times 1 00\%} \tag{12}\]  
The predicted results are processed in two stages, resulting in the following outcomes for power generation performance monitoring in Figure 6. It is noted that the power performance typically fluctuates within a range of \(\pm\)2%.  
Figure 5: Comparison of prediction results between AdaBoost and LinearRegression.  
Figure 6: Trends in power generation performance of the test set.
## 3 Results  
### Simulation Study  
In order to derive the noise reduction effect with the most reasonable image characteristics, the \(\sigma\)-value of the BM3D algorithm was increased in the interval of 0.01 to 0.99 and applied to the simulated brain T2 WI with three noise intensities of 0.05, 0.10, and 0.15 set. Among the images to which the BM3D algorithm was applied, \(\sigma\)-values of 0.01, 0.05, 0.10, 0.15, 0.20, 0.50, and 0.99 were set to extract the results to analyze the change in image quality from the image with 0.05 Rician noise added, as shown in Figure 4.  
Consequently, we confirmed that as the \(\sigma\)-value increased, the noise of the image decreased. Specifically, it was observed that blurring was also intensified at values of 0.20 or higher. Figure 5 shows the results of the CNR and CV measurements of the images obtained through the \(\sigma\)-value optimization experiment. When the \(\sigma\)-values were 0.08, 0.13, and 0.16 according to the noise intensity, which is shown by the values blue circled in Figures 5 and 6, the CNR was measured as approximately 190.03, 142.49, and 95.65 for each noise intensity, respectively. In addition, the calculated CV values were 0.0058, 0.0079, and 0.0095, respectively.  
Additionally, the SSIM and RMSE were measured to quantitatively determine the difference between the reference and resulting images of optimizing the \(\sigma\)-value of the BM3D algorithm. As shown in Figure 6, when the \(\sigma\)-values for each noise intensity were 0.08, 0.13, and 0.16, the SSIM values were approximately 0.6989, 0.6621, and 0.6287, respectively. The RMSE values were calculated to be approximately 0.1513, 0.1670, and 1931 for the same \(\sigma\)-values, respectively. We estimated the optimized \(\sigma\)-value as 0.12 based on the average value derived from the quantitative evaluation results of the simulation.  
Subsequently, a comparison was conducted between the BM3D algorithm with the \(\sigma\)-value of 0.12, the optimization value derived from the simulation study, and the conventional noise reduction algorithm. The application results of each algorithm were evaluated by calculating quantitative evaluation factors. Figure 7 shows the results of applying the Gaussian, median, Wiener filters, and optimized BM3D algorithms to the image with 0.05 Rician noise added.  
Figure 4: The representative results of applying the BM3D algorithm with a \(\sigma\)-value of (**a**) 0.01, (**b**) 0.05, (**c**) 0.10, (**d**) 0.15, (**e**) 0.20, (**f**) 0.50, and (**g**) 0.99 in the simulated brain T2-weighted image of noise 0.05 with regions of interest.  
From the CNR and CV measurements used to observe the change in the noise intensity, a significant improvement was confirmed in the optimized the BM3D algorithm, as shown in Figure 8. In addition, the similarity with the reference image was the highest when the optimized BM3D algorithm was applied, as shown in Table 1 and Figure 9.
# Spatial Variations in Microfiber Transport in a Transnational River Basin
## 2 Materials and Methods
### Study Area
#### 2.1.2 Wastewater Management along the Tisza and Its Catchment  
The catchment area of the Tisza River is shared between five countries: Ukraine (8.1%), Slovakia (10.2%), Romania (45.4%), Hungary (29.9%), and Serbia (6.4%) [53]. The quality of wastewater discharge and treatment, and the degree of waste management varies between countries, which affects the amount of municipal plastic entering the water system.  
Wastewater pipeline systems (WWPS) are only built in the settlements on the periphery of the Tisza catchment. For example, in Ukrainian (Transcarpathian) cities only 68% of the households are connected to WWPSs, but in the small towns it is 58%, and in the villages is only 1.5% [54]. The situation is similar in Romania, where on average 41% of the households are connected to WWPSs, but in rural areas, it is only 5-15% [55]. The situation is better in Slovakia, with an average of 62% [56]. In Hungary, 56% of the settlements along the Tisza are supplied by WWPS, which is much lower than the national average (83%) [57].  
The WWPS can be a false indicator of environmental status, as it is also important, whether the collected wastewater is treated or not, and what is the degree of the treatment [58]. Unfortunately, the wastewater treatment plants (WWTP) are not sufficiently built in the countries of the catchment, and sometimes more wastewater is generated than what can be treated. The fact that the regions along the Tisza in Hungary occupy almost half of the country's territory, where 39% of the population lives, but less that than a third of the country's total wastewater production is treated reflects the underdeveloped severage treatment capacity of the region [59]. Thus, wastewater is often discharged into the environment untreated. The proportion of untreated and discharged wastewater in Hungary is 2-2.8% (11-15 million m\({}^{3}\)/y). According to a Hungarian MP study, the wastewater contains 466 items/L, whereas in the sewage sludge there are 33-44 thousand items/kg, and 90% of the MPs are fibers [60]. Approximately 12% of the MPs of the raw wastewater gets into the effluent-treated wastewater, similarly to other countries [44; 58].
## 3 Results
### Selection of Optimal Block Arrangement and Combination Scheme
The test sample contained face images involving scarves as an example, and the situation after the reorganization is shown in Figure 7. The residual calculation of the arrangement of scheme a is shown in Figure 8, and Figure 9 shows the minimum residuals of face images involving scarves with different permutations. Figure 6: Residuals of face images involving sunglasses with different permutations [authors’ own processing]. Figure 7: Arrangement and combination scheme of a certain test sub-image (involving a scarf) in the third group: (**a**) reserved block 1, 2, 3; (**b**) reserved block 1, 2, 4; (**c**) reserved block 1, 3, 4; (**d**) reserved block 2, 3, 4; (**e**) reserved block 1, 2, 5; (**f**) reserved block 1, 3, 5; (**g**) reserved block 2, 3, 5; (**h**) reserved block 1, 4, 5; (**i**) reserved block 2, 4, 5; (**j**) reserved block 3, 4, 5 [Reprinted with permission from Elsevier [20]]. Copyright 2013, Neurocomputing]. As shown in Figure 7, the scar part was removed in scheme a. It can be seen from the residual values of each scheme in Figure 9 that the residual value of scheme a (reserved block positions one, two, and three) was the smallest. This means that the reconstituted training samples in Figure 3a were built using the optimal linear model to predict the class of the test image in Figure 7a. Therefore, the prediction result of scheme a was selected at this time. Similarly, the optimal arrangement and combination of one, two, four, and five sub-images were also similar to the above examples.
## 3 Results and Discussions
### Permanent Deformation
#### 3.1.1 Temperature Impact  
Figure 6 depicts the dependence of cumulative axial strain on the number of loading cycles, as influenced by various temperature conditions. Under conditions of an initial stress ratio \(\eta_{0}\) of 0.5, \(\rho_{0}\) set at 0.9 MPa, a moisture content of 14%, and a temperature of \(-7\)\({}^{\circ}\)C, Figure 6a represents the diverse profiles of cumulative axial strain plotted against the increasing number of loading cycles, for distinct dynamic stress amplitude levels. Under dynamic stress amplitudes of 1.2 MPa and 2.3 MPa, the cumulative axial strain progressively intensifies with an increase in loading cycles, ultimately reaching a discernible plateau in its growth rate. The respective \(\varepsilon_{\text{acc,20000}}\) values, corresponding to these conditions, are 0.84% and 2.84%. The curves could be categorized as the first type, which is similar to line 1 [43]. When \(\sigma_{\text{d}}\) reaches 2.8 MPa and 3.2 MPa, the cumulative axial strain escalates at a faster pace, achieving \(\varepsilon_{\text{acc,20000}}\) values of 5.23% and 8.07%, respectively. The subsequent trend of the cumulative axial strain curve, as the number of loading cycles increases, may indicate creep damage, aligning these curves with the second type, akin to line 2 [43]. At a dynamic stress amplitude of 4.5 MPa, the cumulative axial strain undergoes a rapid increase with each loading cycle. The 2835 th loading iteration marked the conclusion of the test, as it met the termination threshold of 20% of the peak vertical strain. The recorded curves align with the third category, displaying similarities to the behavior patterns of line 3 [43]. Similar classification of the curves could be performed in Figure 6b-e, etc. corresponding to the temperature conditions of \(-9\)\({}^{\circ}\)C, \(-11\)\({}^{\circ}\)C, \(-13\)\({}^{\circ}\)C vs. \(-3\)\({}^{\circ}\)C, etc., and would not elaborate further here.
# Multivalent Aptamer Approach: Designs, Strategies, and Applications
## 2 Multivalent aptamer Structures
### Linear Multivalent Aptamers  
Conjugating aptamers onto linear scaffolds--either flexible or rigid--allows aptamers to be arranged into linear configurations. The linear scaffolds can be made of polymers [53] or nucleic acids, such as ssDNA [54; 55], double stranded DNA (dsDNA) [56; 57; 58], and DNA nanotubes [59; 60]. The physical properties of each scaffold type confer specific advantages to their resultant linear constructs. For flexible scaffolds such as ssDNA and polymers, the scaffold chains can easily adapt to the surface topography of targets, while the multivalent aptamers work synergistically, thus enhancing binding avidities [53]. Linear multivalent aptamers with flexible scaffolds are commonly used for therapeutic [4; 61] or detection purposes [23; 53; 55]. Because of these long and flexible aptamer chains, the linear multivalent aptamers can reach tens of micrometers into the sample solutions, providing sufficient interactions with targets for more enhanced target capturing [55]. In addition, these flexible multivalent aptamer chains have been shown to wrap around their targets, resulting in even higher target-capturing efficacies [62]. In contrast, rigid scaffolds endow the aptamers with more precise arrangements and orientations. Typical rigid scaffolds include DNA nanowires and nanotubes; these rigid scaffolds are also commonly used as drug-loading sites for therapeutic agents [28; 63].
## 2 Materials and Methods
### Temperature Profiles and Capillary Pressure Drops
The temperature distribution in the example above is subject to the isothermal wall boundary condition. In fact, neither an isothermal nor an adiabatic wall exists [4; 16]. In the next example, it was assumed that there is a finite amount of heat lost through the die wall. The heat transfer coefficient h at the polymer-mold interface is known to depend on a host of physical quantities (e.g., cavity pressure, surface roughness, etc.) [42], and the reported values in injection molding are in the range of \(500-5000\) W/m\({}^{2}\cdot\) K [43; 44; 45]. For simplicity, a constant heat transfer coefficient \(h=1500\) W/m\({}^{2}\cdot\) K was used throughout. When the temperature-dependent viscosity and the more realistic thermal boundary condition are simultaneously taken into account in the simulation (Simulation C, dashed lines), we see in Figure 2b that the corresponding temperature rise becomes more pronounced near the wall, where the dissipated energy is the maximum. Due to the contact resistance between the melt and the die, the temperature rise is more considerable than that of the isothermal wall (e.g., 20 \({}^{\circ}\)C vs. 10 \({}^{\circ}\)C at \(\dot{\gamma}_{\rm a}=5000\) s\({}^{-1}\)). In Simulation C, the maximum temperature rise at the tube exit (\(z=30\) mm) can reach 24 \({}^{\circ}\)C, which explains why large surface temperature rises of extrudates were reported in the literature [16; 19; 23]. As the finite heat transfer boundary condition is physically more realistic, hereafter it is assumed in nonisothermal capillary flow simulations. For capillary flow involving flow through a contraction of a certain entry angle, the total pressure drop \(\Delta P_{\rm total}\) (from the barrel to the capillary exit) mainly consists of two components and may be written as [35; 37]  
\[\Delta P_{\rm total}\approx\Delta P_{\rm ent}+\Delta P_{\rm cap} \tag{7}\]  
where \(\Delta P_{\rm ent}\) is the entrance pressure drop, which is mainly due to the elongational flow at the entrance, and \(\Delta P_{\rm cap}\) is the pressure drop over the length \(L\) of the capillary, where the flow is shear-dominated; the pressure loss in the exit region of the capillary is presently ignored. Since the analytical power-law model is derived for a straight tube of radius \(R\)[1; 3], for comparison with theory, we had to present our experimental and simulation results in terms of \(\Delta P_{\rm cap}\) (\(\approx\Delta P_{\rm total}-\Delta P_{\rm ent}\)). In Figure 2d, the agreement between the measured \(\Delta P_{\rm cap}\) and the analytical power-law model is simply fortuitous, whereas the discrepancy between experiment and the simulation (both Simulations B and C) is very likely due to the neglect of viscous heating correction of the shear viscosity for large temperature rises, in particular at high shear rates. In fact, the trend in the observed deviation in Figure 2d is universal for all the samples investigated in this study, and highlights the necessity of viscous heating correction to the shear viscosity.
## 4 Stability Analysis
Then,  
\[\dot{\mathrm{V}}(\delta)=\frac{\partial\mathrm{V}(\delta)}{\partial\delta}\dot{ \delta}=2\delta^{\mathrm{T}}\mathrm{P}\delta=2\delta^{\mathrm{T}}\mathrm{P} \left[\omega_{\mathrm{o}}\mathrm{M}\delta+\mathrm{N}\frac{z_{f}-\dot{z}_{f}}{{ \omega_{\mathrm{o}}}^{2}}\right], \tag{24}\]  
and then  
\[\dot{\mathrm{V}}(\delta)=\omega_{\mathrm{o}}\delta^{\mathrm{T}} \mathrm{PM}\delta+\omega_{\mathrm{o}}\delta^{\mathrm{T}}\mathrm{M}^{\mathrm{T}} \mathrm{P}\delta+2\delta^{\mathrm{T}}\mathrm{PN}\frac{z_{f}-\dot{z}_{f}}{{ \omega_{\mathrm{o}}}^{2}}=\omega_{\mathrm{o}}\delta^{\mathrm{T}}\left(\mathrm{PM }+\mathrm{M}^{\mathrm{T}}\mathrm{P}\right)\delta+2\delta^{\mathrm{T}}\mathrm{PN }\frac{z_{f}-\dot{z}_{f}}{{\omega_{\mathrm{o}}}^{2}} \tag{25}\] \[=-\omega_{\mathrm{o}}\delta^{\mathrm{T}}\delta+2\delta^{\mathrm{T}} \mathrm{PN}\frac{z_{f}-\dot{z}_{f}}{{\omega_{\mathrm{o}}}^{2}}=-\omega_{ \mathrm{o}}\|\delta\|_{\mathrm{F}}\mathrm{}^{2}+2\delta^{\mathrm{T}}\mathrm{PN }\frac{z_{f}-\dot{z}_{f}}{{\omega_{\mathrm{o}}}^{2}}\]  
Since \(\mathrm{z}_{f}\) is globally Lipschitz, for all \(\mathrm{z}_{f},\dot{z}_{f}\), there exists a constant such that \(\left|\mathrm{z}_{f}-\dot{z}_{f}\right|\leq I\|\mathrm{x}-\mathrm{z}\|_{\mathrm{F}}\). Thus, we can obtain:  
\[2\delta^{\mathrm{T}}\mathrm{PN}\frac{\mathrm{z}_{f}-\dot{z}_{f}}{{\omega_{ \mathrm{o}}}^{2}}\leq 2\delta^{\mathrm{T}}\mathrm{PN}\frac{\|\mathrm{x}-\mathrm{z}\|_{ \mathrm{F}}}{{\omega_{\mathrm{o}}}^{2}}, \tag{26}\]  
When \(\omega_{\mathrm{o}}\geq 1\),  
\[\frac{\|\mathrm{x}-\mathrm{z}\|_{\mathrm{F}}}{{\omega_{\mathrm{o}}}^{2}}=\frac{ \|\mathrm{z}_{\mathrm{e}}\|_{\mathrm{F}}}{{\omega_{\mathrm{o}}}^{2}}=\frac{\| \sqrt{{\delta_{1}}^{2}+{\delta_{2}}^{2}{\omega_{\mathrm{o}}}^{2}+{Which means  
\[\dot{\mathrm{V}}(\delta)\leq-\omega_{\mathrm{o}}\|\delta\|_{\mathrm{F}}{}^{2}+c\| \delta\|_{\mathrm{F}}{}^{2}=-(\omega_{\mathrm{o}}-c)\|\delta\|_{\mathrm{F}}{}^{2}, \tag{29}\]  
Thus, if \(\omega_{\mathrm{o}}>c\), then the Lyapunov stability equation \(\dot{\mathrm{V}}(\delta)<0\). According to Definition 1, the system is semi-globally uniformly exponentially stable. \(\square\)
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 5 Conclusions  
In this work, the measurement results for an industrial example have been provided to validate the simulation results obtained with the Darwin model, which shows a good agreement in the range of intermediate frequencies, particularly around the resonant frequency. Additionally, for a complex and multi-domain application, the different quasistatic models were compared by computing their impedances, as well as the different electromagnetic fields, to investigate their limits.  
At low frequencies, when the impedance exhibits a resistive behavior, the EQS model is sufficient. Moreover, when the inductive effects are non-neglected, the MQS and Darwin models can be invoked, but the MQS model is preferred, since its computational cost is lower than the Darwin model. However, in the intermediate frequencies, particularly around the resonant frequency, the Darwin model should be adopted, since the MQS model cannot handle the coupled capacitive-inductive effects.  
Furthermore, the EQS model can be used as an indicator to learn the frequency at which the capacitive effects become significant, where the Darwin model should be applied.  
Figure 15: Modulus of impedance and the phase computed with respect to frequencies.  
**Author Contributions:** All authors are involved to some extent in the different parts of the paper: Conceptualization, H.T., Z.T., T.H., Y.L.M. and J.-P.D.; Data curation, H.T.; Formal analysis, H.T., Z.T., T.H. and Y.L.M.; Funding acquisition, Y.L.M. and J.-P.D.; Investigation, H.T., Z.T. and T.H.; Methodology, Z.T., T.H. and Y.L.M.; Resources, F.S. and J.-P.D.; Software, H.T.; Supervision, Z.T., T.H., Y.L.M. and J.-P.D.; Validation, Z.T., T.H. and Y.L.M.; Visualization, H.T., Z.T. and T.H.; Writing--original draft, H.T.; Writing--review & editing, Z.T. and T.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been financially supported by the European Regional Development Fund (ERDF) NP0017752 and LAMEL laboratory (joint laboratory between the University of Lille, ENSAM, and the EDF company).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interests.