esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer Vision System: Measuring Displacement and Bending Angle of Ionic Polymer-Metal Composites
## 4 Results and Discussion  
Several runs of the coding with IPMC bending video proved it was difficult to only isolate the contours of the sample and the reference circle. In Figure 17, circled in red, are the detected small contours, consisting of image noise and small dust particles in the air and on the camera lens. Contours near the reference circle are tiny ink splashes from the printer--hard to notice with the naked eye. Several small contours detected on the sample were due to the non-uniform colour distribution of the platinum-coated Nafion surface. Some of these can be eliminated by changing the threshold value, but this results in the contours of the sample and the reference circle not being perfectly detected.  
The first iteration of the code saw the sample and reference circle being distinguished from each other as having minimum and maximum contour areas, respectively. Having other contours present besides the two required would render the code futile. Accounting for this, the improved code sorted contours by their areas. In this setup, the reference circle with a diameter of 50 mm was configured to always have both more contour area than the sample and the maximum contour area in the frame. The sample will have the second largest contour area. Contours were sorted by their areas using the _sorted()_ function, setting the key as contour area. By default, this function sorts the areas in ascending order, from smallest to largest contour area. Areas were sorted in descending order by adding _reverse_=_True_ in the function [37]. Reference circle contour was then set to the largest contour area, whereas the sample was set to the second largest. Displacement results from the improved vision system and the manual calculation method are shown in Figure 18.  
Figure 17: Detection of multiple contours.  
Visually inspecting the displacements values obtained from both methods, the results generally follow the same trend with no significant deviations between one another. Deviations in results between the manual method and the proposed vision system can statistically be calculated using mean absolute error (MAE) and root mean squared error (RMSE). Calculated values of MAE and RMSE are 0.068080668 and 0.088160652, respectively. A similar trend can be seen in bending angle values, as shown in Figure 19.  
The calculated values of MAE and RMSE for bending angle are 0.081544205 and 0.103880163, respectively. Low values of MAE and RMSE signify agreeable results between both methods. It can be concluded that the vision system proposed can accurately approximate the displacement and bending angle values of Nafion-based ionic polymer-metal composites.
# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 2 Wind Turbine and Control Algorithm
### Control Algorithm  
The control algorithm for the targeted wind turbine was designed considering the independent generator torque control of the PCS, applying only pitch control and control transition logic. In previous studies targeting the same wind turbine, a power control strategy commonly used in large wind turbine control systems was adopted, where both torque control and pitch control were handled by the PLC, and a mode switch was used for control mode transitions [19; 28]. However, this study involved the design of a controller for an environment where independent generator torque control is performed by the PCS. Additionally, to improve any potential degradation in the operational performance that may arise during the application of the PCS's independent generator torque control, an algorithm was proposed and designed to replace the previously used mode switch. Verification of the designed controller was conducted through simulations using the Bladed (version 4.12) program, and the control algorithm was implemented as an external controller in Dynamic Link Library (DLL) format. Figure 2 shows the overall schematic of the power control algorithm considering the PCS torque control, and the details related to the design are described in the following section.
## Nomenclature  
\begin{tabular}{l l l l} UUV & unmanned underwater vehicle & \(a\) & wave amplitude \\ DPIV & Digital Particle Image Velocimetry & \(c\) & chord length \\ VOF & volume of fluid & \(\lambda\) & wavelength \\ NWT & numerical wave tank & \(St\) & Strouhal number \\ CFD & computational fluid dynamics & \(xay\) & geodetic coordinate system \\ RNG & renormalization group & \(x_{1}o_{1}y_{1}\) & local coordinate system \\ \(h_{l}\) & submergence depth & \(V_{0}\) & inflow velocity \\ \(h_{w}\) & water depth & \(k\) & wave number \\ \(H\) & wave height & \(y_{0}\) & heavy amplitude \\ \(g\) & gravitational acceleration & \(\theta_{0}\) & maximum pitching angle \\ \(\varphi_{0}\) & phase difference between deformation and the heaving & \(\varphi\) & phase difference between heaving and \\  & motion & \(\varphi\) & pitching motions \\ \(\theta(t)\) & angular displacement & \(\delta_{y}\) & deforming offset \\ \(\psi\) & phase difference between foil motion and wave motion & \(\delta_{c}\) & deforming amplitude \\ \(f\) & motion frequency & \(M\) & moment \\ \(s\), \(\varepsilon\) & controlling coefficients for deformation & \(F_{y}\) & lateral force \\ \(y_{1}(t)\) & vertical displacement & \(C_{l}\) & lateral force coefficient \\ \(F_{x}\) & thrust force & \(\rho\) & water density \\ \(C_{t}\) & thrust force coefficient & \(P_{o}\) & output power \\ \(C_{m}\) & moment coefficient & \(C_{po}\) & output power coefficient \\ \(P_{i}\) & input power & \(C_{T}\) & average thrust force coefficient \\ \(C_{pi}\) & input power coefficient & \(C_{pw}\) & average power coefficient \\ \(\eta\) & propulsive efficiency & \(c_{w}\) & wave celerity \\ \(c_{g}\) & group celerity & \(\eta_{r}\) & percentage of wave energy recovery \\ \(P_{w}\) & average power of a regular wave with unit width & \(C_{pm}\) & output power coefficient under calm \\ \(C_{pr}\) & wave power recovering coefficient & \(u^{\prime}_{r},u^{\prime}_{r}\) & fluctuating velocity components \\ \(C_{pw}\) & output power coefficient with the presence of waves & \(u_{r},u_{r}\) & mean velocity components \\ \(\dfrac{u_{r},u_{j}}{u^{\prime}_{i}u^{\prime}_{j}}\) & transient velocity components & \(S_{i}\) & source item \\ \(\dfrac{u_{r}^{i}u^{\prime}_{i}}{u^{\prime}_{j}}\) & mean value of \(u_{i}^{\prime}u^{\prime}_{i}\) & \(h_{u}\) & height of the air zone \\ \(p_{r}\) & transient pressure & \(\omega_{w}\) & circular frequency of the wave motion \\ \(\mu\) & kinematic viscosity coefficient & \(\gamma\) & controlling coefficient for damping \\ \(\lambda_{d}\) & artificial damping coefficient & \(T\) & motion period of foil \\ \(\omega_{f}\) & angular frequency of foil motion & \(U_{i}\) & velocity component \\ \end{tabular}
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 4 Resemble Design
### Delay Bound Calculation for Real-Time Transmissions  
This subsection defines the end-to-end delay (e2 eDelay) of a message and describes the calculation of the end-to-end delay bound of a flow in RESEMBLE.  
The message end-to-end delay is defined as the difference between the message generation time at the source and the message reception time at the destination, measured at the Network Layer. The e2 eDelay is the sum of two components, i.e., the message queuing time and the message transmission time, at each hop in the path, from the source to the destination. In this analysis, we assume that the message transmission time is equal to the timeslot duration (\(T_{ts}\)), as the latter is configured to allow for the complete message transmission. As a result, in the worst-case e2 eDelay analysis presented here, the e2 eDelay bound, i.e., the upper bound of the delay that a message of the flow may experience, is given by the following equation  
\[e2 eDelay\_bound_{i}=h\times T_{ts}+\sum_{k=1}^{h}T_{q_{i}}^{(k)} \tag{2}\]  
where \(h\) is the number of hops a message of the \(i\)-th flow has to traverse to reach the destination, \(T_{ts}\) is the timeslot duration and \(T_{q_{i}}^{(k)}\) is the maximum queuing delay of a message belonging to the \(i\)-th flow at the \(k\)-th hop.  
To determine the worst case \(T_{q_{i}}^{(k)}\) at each node traversed by a message of the \(i\)-th flow, first, both the resource availability and the worst case for the resource request are calculated. In RESEMBLE, the resource availability for the \(k\)-th node is given by the number of timeslots that the node \(n_{k}\) can use to transmit, while the resource request is the number of timeslots needed to transmit the packets generated or forwarded by the node \(n_{k}\). Based on these definitions, the analysis can be performed by applying the worst-case response time (WCRT) analysis that was proposed in [33] and extended with the \"busy period\" approach in [34; 35; 36]. The analysis has already been applied to TDMA communications in [37; 38].  
At the Network Layer, RESEMBLE adopts the flooding mechanism; therefore, the messages are transmitted over multiple paths. For this reason, the \(e2 eDelay\_bound\) is calculated considering all the possible paths for a flow from the source to the destination.
## 1 Introduction
To address this challenge, stochastic techniques such as particle swarm optimization (PSO) are employed to increase the likelihood of locating the global optimum [16]. Implementing PSO in the FCM algorithm has been successful in various areas, including image processing and engineering [17; 18; 19; 20]. This paper introduces a PSO-FCM algorithm for the reconstruction of the MQAM modulation constellation diagram. This algorithm combines the FCM and PSO algorithms into a three-step process: initial cluster center computation using the subtractive clustering (SC) algorithm based on the signal-to-noise ratio (SN-SC); subsequent utilization of the PSO-FCM algorithm, leveraging the initially obtained cluster centers; and the combined benefits of FCM and PSO for improved cluster center accuracy. The PSO-FCM algorithm avoids getting stuck in local minima by utilizing the PSO global search method to find candidate solutions. The modulation type of the signal constellation can be found by finding the ratio of the largest and smallest cluster center radius. The subsequent sections of this paper are organized as follows: Section 2 outlines the signal model employed; Section 3 provides a detailed explanation of the methodology used in this study, which includes the signal-to-noise ratio-based subtractive clustering (SN-SC) algorithm and the particle swarm optimization with fuzzy c-means (PSO-FCM) algorithm; Section 4 illustrates the simulation results; and Section 5 concludes the work.
## 3 Problem Statement and Principal Approach
### Asymmetric Error Probabilities  
In [23; 25; 30; 42], asymmetric error probabilities of the flash channel are reported, i.e., the probabilities of 1-to-0 errors and 0-to-1 errors are not equal. In our measurements, we found basically two causes for this asymmetry. Firstly, for some charge levels, the asymmetric shape of the threshold voltage distributions causes a certain asymmetry for the bit errors. Secondly, an offset of the reference voltage leads to asymmetric probabilities of 1-to-0 errors and 0-to-1 errors. Later on, we will exploit the asymmetric error probabilities due to the offsets for the adaptation of the read references.  
The asymmetric error probabilities are indicated in Figure 4 which shows the number of errors for 1-to-0 errors and 0-to-1 errors. This figure considers reference \(r_{3}\) at an EOL scenario. The error probabilities are balanced for the optimal threshold voltage which is indicated by the value \(\Delta V_{th}=0\), whereas the asymmetric error counts for other voltages can be exploited for the threshold adaptation.  
As shown in Figure 2, the higher distributions are highly asymmetric for early-life cases. Figure 5 shows the error count for 1-to-0 errors, 0-to-1 errors, and the overall error count for reference \(r_{7}\) for a fresh memory block. The error probabilities are asymmetric at the optimal threshold voltage indicated by the value \(\Delta V_{th}=0\), which needs to be considered for voltage adaptation.
# Flavour Generation during Lactic Acid Fermentation of Brassica Vegetables--Literature Review
## 3 Strategies Used for Aroma Analysis  
In every food product, the number of volatiles is usually high, however, the number of aroma active compounds present in the concentration above their odour threshold is limited. The most common strategy dedicated to analyzing the dominant odour active substances--so-called key odorants--is the sensoric approach, which involves the application of gas chromatography-olfactometry (GC-O) [15]. GC-O combines the separation step (GC), with the human nose playing the role of the detector. Among the extraction techniques used for the sensoric approach is solvent assisted flavour extraction (SAFE), introduced by [PERSON] et al. [16], which is considered the gold standard. SAFE is considered to be a method that allows for effective volatile isolation at low temperatures, avoiding potential flavour modification or artefact formation at high temperatures [17]. In a similar manner, for taste active components, liquid chromatography fractioning and later a taste test by the sensory panel is applied. The flavour dilution factor (FD) is the dilution step in which the respective odorant is detected for the last time. A higher FD factor means a higher odour potency of the analyte [18]. However, the FD factor does not display the importance of the single aroma compounds appropriately. That is because the volatility of each odorant determines its available amount in the headspace, whereas during the GC-O, the entire amount of each compound is vaporized in the injection port, and the amount of analyte reaching the odorant receptors is larger. To solve this issue, an odour activity value (OAV) was suggested, which is expressed as the concentration divided by the odour threshold [19]. Analogically in taste analysis, a procedure aiming to describe the importance of the taste-active component in flavour creation is the taste dilution analysis (TDA). However, in taste, the analysis is more straightforward, since the effect of the matrix does not play a significant role. As concluded by [PERSON] and [PERSON] [20], the aroma profile might be reconstituted based on the results of quantitative data. The authors presented it based on the analysis of apricot, which was closely mimicked by the mixture of 18 identified volatiles of identical concentrations to those present in the apricot.  
To better understand the flavour, after recognition of aroma-active compounds, their origin needs to be determined. This step involves multiple biochemical analyses. The application of radiolabelled compounds is one of the most common steps in providing information regarding potential biochemical pathways leading to flavour formation. The next steps aim to provide information regarding the enzymes involved in these pathways by their isolation and evaluation of the activity with dedicated precursors.
# Pattern-Based Test Suite Reduction Method for Smart Contracts
## 4 Test Case Design and Implementation  
### Test Cases  
The test package was designed and implemented to perform two types of performance tests: verification of the smart contract evaluation time depending on the number of verification rules and verification of the evaluation time of the test suite prepared according to the proposed method. It was assumed that the evaluation times of smart contracts containing from one to eight verification rules would be compared. The number of test cases in the test suite for a smart contract with \(k\) verification rules \(T_{k}^{suite}\) can be expressed by the following Equation (5):  
\[T_{k}^{suite}=k+1 \tag{5}\]  
The number of test cases for the \(k\in<1,8>\) verification rules, in the \(k+1\) method, is presented in Figure 3.  
To compare the transaction evaluation time by a smart contract with the number of verification rules \(k\in<1,8>\), test suites with the number of test cases \(T_{k}^{suite}\in<2,9>\) must be prepared.  
The number of test cases in a test suite is substantially larger for full coverage (Figure 4).  
In the context of regression testing, it is important to obtain the execution time of a test suite for a smart contract. The number of test cases in the complete test set \(T_{k}^{set}\) for performance analysis of a test suite can be expressed as a sum of test cases in each test suite for verification rule amount (\(T_{i}^{suite},i\in<1,k>\)). This can be defined by the following Equation (6):  
\[T_{k}^{set}=\sum_{i=1}^{k}T_{i}^{suite}=\sum_{i=1}^{k}(i+1) \tag{6}\]  
In the example considered in the paper, the 44 test cases should be prepared (for \(k=8\)). Figure 5 presents the number of test cases in test suites for different numbers of verification rules in the \(k+1\) method. As the number of verification rules increases, the number of test  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Verification Rules** & **Test Cases in PM** & **Test Cases in FC** & **Number of Test Cases Eliminated** & \(\mathbf{r_{p}^{sc}}\) \\ \hline
1 & 2 & 2 & 0 & 0.00\% \\
2 & 3 & 4 & 1 & 25.00\% \\
3 & 4 & 8 & 4 & 50.00\% \\
4 & 5 & 16 & 11 & 68.75\% \\
5 & 6 & 32 & 26 & 81.25\% \\
6 & 7 & 64 & 57 & 89.06\% \\
7 & 8 & 128 & 120 & 93.75\% \\
8 & 9 & 256 & 247 & 96.48\% \\
9 & 10 & 512 & 502 & 98.05\% \\
10 & 11 & 1024 & 1013 & 98.93\% \\
11 & 12 & 2048 & 2036 & 99.41\% \\
12 & 13 & 4096 & 4083 & 99.68\% \\
13 & 14 & 8192 & 8178 & 99.83\% \\
14 & 15 & 16,384 & 16,369 & 99.91\% \\
15 & 16 & 32,768 & 32,752 & 99.95\% \\
16 & 17 & 65,536 & 65,519 & 99.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical values of test cases for PM and FC.  
For comparison, Figure 6 presents the number of test cases in test suites for different numbers of verification rules for the full coverage approach (function \(y=1.2023\times e^{0.27718\text{x}}\), model fit quality measure \(R^{2}\) = 0.9946).  
It can be noticed that the number of test cases is significantly higher for full coverage and grows exponentially with the increase in the number of verification rules.
## 5 Results and Discussions  
In order to compare the results for different tests, it is more appropriate to use relative temperatures rather than absolute temperatures since the ambient temperature varies from one test to another and sometimes even during the same test. For each studied convection cases, we are going to use three \(\Delta T\) to analyze better all of the results [25].  
* \(\Delta T_{rise,max}\): the maximum temperature increasing of all of the SCs. \[\Delta T_{rise,max}=Max(\Delta T_{i,\;rise})\] (8) where \(\Delta T_{rise}\) is the maximum temperature growth for each supercapacitor. \[\Delta T_{rise}=T_{max}-T_{initial}\] (9)
* \(\Delta T_{max}\): gives us information about the difference between the highest and the lowest temperature rise. This parameter is important since it should remain less than 5 \({}^{\circ}\)C [9] to prevent the rapid aging of SCs. \[\Delta T_{max}=\Delta T_{rise,max}-\Delta T_{rise,\;min}\] (10) where \(\Delta T_{rise,min}\) is the lowest maximum temperature augmentation of all of the SCs \[\Delta T_{rise,\;min}=Min(\Delta T_{i,\;rise})\] (11)  
Figure 7: Detail of the used polyhedral numerical grid in a longitudinal section cross the computed domain.  
\(\Delta T_{rise,avg}\): the average of the temperature rises of all of the SCs, and can be calculated with: \[\Delta T_{rise,avg}=\frac{\sum_{i=1}^{n}\Delta T_{i,rise}}{n}\] (12)  
In addition, we are going to plot the deviation of \(\sigma_{\Delta Trise}\). When the deviation value is high, it indicates that the differences between the temperatures of the SC are high. Its value should be low to preserve more uniformity.  
\[\sigma_{\Delta T_{rise}}=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\bigl{(}\Delta T_{i, rise}-\Delta T_{rise,avg}\bigr{)}^{2}} \tag{13}\]  
In order to illustrate the importance of the cooling system, it is necessary to study first of all of the natural convection cases.
## 4 Conclusions  
This paper introduces an approach for revising atmospheric transmittance for high-precision and wide-dynamic IR radiometry. The effect of the atmosphere on IR radiometry has been analyzed, and a model of high-speed calibration considering integration times suitable for the outfield employed; therefore, the calibration equation at random integration times was able to be deduced immediately [21; 22].  
Then, based on ambient temperature and constant reference, an approach to calculating the atmospheric transmittance and the path radiance was proposed. The transmittance was acquired at 2000 and 3000 \(\upmu\)s to eliminate stochastic error. Finally, calibration and radiometry experiments were performed based on a MWIR system with \(\Phi\)600 mm diameter to evaluate whether the proposed method is effective for IR radiometry for even greater precision. The experimental results at 2000 \(\upmu\)s and 3000 \(\upmu\)s illustrate that the revised method yields high precision: (1) this method remedies the atmospheric transmittance and the path radiance accuracy in horizontal direction and low angle of pitch for high-precision in the outfield; (2) instead of expensive atmospheric observation devices, like laser radar and solar radiometers, it reduces the cost and the complexity of calculations; and (3) the proposed method is able to radiometers, it reduce the atmospheric transmittance and the path radiance in real-time, which makes the result direct, effective, and fit for the outfield. It has to be pointed out that the radiometric results are relevant to the uniformity and invariability of the reference. However, these problems could be solved if a suitable reference is picked up.  
[PERSON] and [PERSON] conceived the presented idea. [PERSON] and [PERSON] developed the theoretical formalism. [PERSON] and [PERSON] collected and analysed the data. [PERSON] discussed the result and wrote the paper. [PERSON] supervised the entire work.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**2000 \(\upmu\)s**} & \multicolumn{4}{c}{**3000 \(\upmu\)s**} \\ \hline \(T^{\mathrm{o}}\)C & **Error of Software (\%)** & **Error of Reference (\%)** & **T\({}^{\mathrm{o}}\)C** & **Error of Software (\%)** & **Error of Reference (\%)** \\ \hline
50 & 13.81 & 0.27 & 50 & 10.73 & 0.26 \\
60 & 9.57 & 4.83 & 60 & 7.84 & 4.89 \\
70 & 7.77 & 0.84 & 70 & 6.08 & 1.23 \\
80 & 5.14 & 0.78 & 80 & 5.31 & 0.73 \\
90 & 5.19 & 0.58 & 90 & 5.83 & 1.02 \\
100 & 4.