esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach
## 4 Results and Discussion
### Analysis of Optimal Parameter for Orthogonal Polynomial
The performance in the salt and pepper noisy environment follows a similar performance to that observed with Gaussian noise. The model achieves a maximum recognition accuracy of 84.842 at \(p=0.4\) and a noise density of 0.01. As the noise density increases, the recognition accuracy decreases, reaching 81.957 at \(p=0.4\) and a noise density of 0.05. The scores for salt and pepper noise also reflect a gradual decrease, with values of 84.219 at \(p=0.4\) for a noise density of 0.02 and 83.032 at the same parameter for a noise density of 0.03. The results of speckle noise illustrate the challenges associated with maintaining recognition accuracy in the presence of noise. The model achieves a recognition accuracy of 84.729 at \(p=0.4\) and a noise variance of 0.2. However, as the noise variance increases, the recognition accuracy declines, with a score of 81.618 at \(p=0.4\) and a noise variance of 1. To this end, the results indicate that while the proposed technique performs well in clean environments, its slightly affected in the presence of noise, especially for OP parameter \(p=\)0.4. The findings highlight that the recognition system based on the proposed technique is able to tackle the problem of noisy environments.
## 1 Introduction
These methods are limited by the mechanical rotational speed of the pitch angle; even if the optimal control signal response is obtained, it is not possible to obtain the optimal pitch angle position. This paper proposes an innovative PID controller with automatic adjustment of its gains by means of an FLC. Unlike the proposed methods, the membership functions of the FLC are determined based on two criteria. The first criterion is to monitor the error of the controlled variable, which is the generator shaft speed and the magnitude of the change in this error. The second criterion is related to drastic changes in wind speed. An anemometer is installed at a certain distance that allows the controller to anticipate the pitch angle change before receiving the wind impact on the rotor. This ensures an anticipated response in the position of the angle of incidence and avoids an excessive rotational speed of the turbine, which increases the efficiency and safety of the system. In addition, the methodology proposes a statistical analysis of the historical wind behavior to determine the values of stable and gust wind speed. With this information, the global optimal values of the PID controller gains are determined. Therefore, this work contributes scientifically with a methodology for the development of a PID-FLC control algorithm for the pitch angle in a SHAWT. This algorithm solves the problem of controlling a nonlinear system due to wind randomness. The research question or hypothesis is to determine that it is possible to increase the performance of a SHAWT using a PID-FLC controller, compared to a classical PID controller. In this way, this research helps SHAWT users to improve their system, increasing their energy production and reducing maintenance costs. The article is structured by initially presenting the methodology of the algorithm development. The methodology proposes to set the operating limits of the wind turbine using its mathematical model, perform a statistical analysis of the wind to determine its variability, calculate the distance between the installation site of the anemometer and the turbine, validate the location of the anemometer using a dynamic analysis of the wind trajectory, determine the domain of the PID gain values, and assign the FLC rules. The validation of the proposed PID-FLC control algorithm is performed by simulation. A series of staggered pulses with different amplitude values are considered input variables. Each pulse represents different wind speed conditions, which test the response and reliability of the algorithm. Finally, the results of the implementation of the algorithm on a three-bladed SHAWT with a swept area of 130 m\({}^{2}\) and a 14 kW permanent magnet synchronous generator (PMSG) are presented. A comparison between the conventional and the proposed PID controller is performed to quantify the differences in their performance and to test the hypothesis.
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 4 Experiments and Analysis
### Multi-Task Model Test  
We tested the IQA and distortion type classification of the multi-task model proposed by Section 3.5. Based on the MSIQA model, we trained the multi-task model. The test results in the case of very few training epochs (1-3) are shown in Table 6. The joint tasks complement each other by sharing information, and even improve the performance of IQA. At the same time, we have made statistics on the classification accuracy of each distortion type, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Database** & \multicolumn{2}{c}{**LIVE**} & \multicolumn{2}{c}{**TID2013**} \\ \cline{2-6}  & **Metrics** & **SRCC** & **PLCC** & **SRCC** & **PLCC** \\ \hline \multirow{3}{*}{FR} & PSNR & 0.876 & 0.872 & 0.687 & 0.706 \\  & SSIM & 0.948 & 0.945 & 0.775 & 0.790 \\  & FSIMc & 0.963 & 0.960 & 0.851 & 0.877 \\ \hline \multirow{6}{*}{NR} & BLINDS-II & 0.912 & 0.916 & 0.536 & 0.628 \\  & BRISQUE & 0.939 & 0.942 & 0.573 & 0.651 \\  & CORNIA & 0.942 & 0.943 & 0.549 & 0.613 \\  & Kang & 0.956 & 0.953 & - & - \\  & BIECON & 0.961 & 0.962 & - & - \\  & Bosse & 0.960 & 0.972 & 0.835 & 0.855 \\  & DIQA & 0.975 & 0.977 & 0.825 & 0.850 \\  & Hallucinated & **0.982** & 0.982 & **0.879** & **0.880** \\  & QualNet & 0.980 & **0.984** & **0.890** & **0.901** \\  & MSIQA & **0.983** & **0.988** & 0.877 & **0.880** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the SRCC and PLCC with different methods on LIVE and TID2013 database.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type** & **PSNR** & **SSIM** & **FSIM** & **BRISQUE** & **CORNIA** & **BIECON** & **DIQA** & **MSIQA** \\ \hline JP2K & 0.895 & 0.961 & 0.972 & 0.914 & 0.921 & 0.965 & 0.961 & **0.983** \\ JPEG & 0.881 & 0.972 & 0.979 & 0.965 & 0.938 & **0.987** & 0.976 & **0.984** \\ WN & 0.985 & 0.969 & 0.971 & 0.977 & 0.957 & 0.970 & **0.988** & **0.998** \\ GB & 0.782 & 0.952 & **0.968** & 0.951 & 0.957 & 0.945 & 0.962 & **0.980** \\ FF & 0.891 & **0.956** & 0.950 & 0.877 & 0.906 & 0931 & 0.912 & **0.972** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the SRCC with different methods on LIVE for different distortion types.
# Unified Analytic Melt-Shear Model in the Limit of Quantum Melting
## 3 Results
### Quantum Melting Added to the Unified Analytic Cold EOS-Shear Modulus Model  
Now we can add quantum melting to the unified analytic cold equation of state (EOS)-shear modulus model developed in Refs. [5; 9; 18] in which the shear modulus part of the unified melt-shear model discussed above is combined with the cold EOS. This is done based on the following relation for cold bulk and shear moduli (subscript \"\(c\)\" refers to \(T=0\); \(G_{c}\) is identical to \(G(\rho,0)\) in Equation (13)) [9; 18]:  
\[G_{c}=\frac{G_{0}}{B_{0}}\bigg{(}B_{c}-\frac{2}{3}\,t\,P_{c}\bigg{)}, \tag{45}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\frac{\rho^{*}}{A^{4}\,Z^{6}}\) (\(\frac{\rm{g}}{{\rm cm}^{3}}\)) & \(\frac{\rho_{T_{m\max}}}{A^{4}\,Z^{6}}\) (\(\frac{\rm{g}}{{\rm cm}^{3}}\)) & \(\frac{\rho_{G\max}}{A^{4}\,Z^{6}}\) (\(\frac{\rm{g}}{{\rm cm}^{3}}\)) & \(\frac{T_{m\max}}{A\,Z^{4}}\) (K) & \(\frac{G_{\max}}{A^{4}\,Z^{10}}\) (GPa) \\ \hline \((4.7\pm 0.6)\cdot 10^{3}\) & \((2.9\pm 0.4)\cdot 10^{3}\) & \((4.1\pm 0.5)\cdot 10^{3}\) & \((1.3\pm 0.2)\cdot 10^{4}\) & \((7.1\pm 1.9)\cdot 10^{6}\) \\ \hline \end{tabular}
\end{table}
Table 1: Five parameters associated with the unified analytic melt-shear thermoelasticity model in the limit of quantum melting.  
where \(B_{c}\equiv\rho\frac{dP_{c}}{d\rho}\) is the cold bulk modulus. In Equation (45), \(G_{0}\equiv G_{c}(\rho_{0})\) and \(B_{0}\equiv B_{c}(\rho_{0})\), where \(\rho_{0}\) is some (fixed) density. It then follows from Equations (8) and (45) that  
\[\gamma_{G}(\rho)=\frac{1}{2}\,\frac{d\ln(B_{c}-\frac{2}{3}\,t\,P_{c})}{d\ln\rho} -\frac{1}{6}=\frac{\frac{B_{c}^{\prime}}{2}-\frac{1}{6}-\frac{t}{3}(1-\frac{P _{c}}{3R_{c}})-\frac{P_{c}}{3}\frac{dt}{dP_{c}}}{1-\frac{2t}{3}\frac{P_{c}}{B_ {c}}},\quad B_{c}^{\prime}\equiv\frac{dB_{c}}{dP_{c}}, \tag{46}\]  
which is the modified Slater-Dugdale-MacDonald-Vashchenko-Zubarev formula of ref. [18]. Note that the Gruneisen \(\gamma\) in (46) is identical to that in Equation (10) [9; 18]. In what follows we discuss a practical example of the unified analytic cold EOS-shear modulus model with the inclusion of quantum melting.
## Appendix B SN Grammar
### Composite Iterator and Composite Decision  
It is common, in the natural languages, that, at the end of a paragraph, mention is made that the content of the text will be repeated based on a condition, or, on the contrary, that the text described in the paragraph is conditioned by something. For this reason, SN has inbuilt support for adding conditionals and iterators to the composite instructions, in order that these constructions indicate that what was written will be repeated or executed if the specified criterion is met. The rule that describes the iteration that occurs within a composite instruction is presented below:nested_iterator ::='repeat' iterator_condition  
The following rule is presented for working with conditionals within a composite instruction:  
nested_decision ::= 'execute' 'when' condition  
An example is presented below (the ; indicates that the subsequent instruction continues on the same line, although, due to questions of space, it is divided into two):  
a Number. add 1 to the Number; repeat 10 times;  and execute when the Number is equal to 0.  
The approximate equivalent in Java for the previous instruction is shown below (_add 1 to the Number; repeat 10 times; and execute when the Number is equal to 0._):  
int theNumber = 0; if(theNumber == 0) {  for(int i = 0; i < 10; i++) {  theNumber += 1;}}  
As can be seen, the conditional is found in the most external position, meaning that it will encompass all of the instructions, which, previously, have run from left to right. On conversion into code, it is analyzed and placed at the beginning of the set of instructions, which also occurs with the iterator. This can be seen, for example, in the following instruction:  
a Number. add 1 to the Number; repeat 10 times; subtract 8 from the Number;  and execute when the Number is equal to 0.  
Its equivalent in Java is the following:  
int theNumber = 0; if(theNumber == 0) {  for(int i = 0; i < 10; i++) {  theNumber += 1;}  theNumber -= 8;}  
As can be seen, while the conditional includes all the instructions, the iterator only includes that which precedes it.
## 4 Privacy-Preserving Optimization Based on Advanced CH Selection (P2O-ACH)  
The privacy-preserving optimization-based advanced CH selection (P2O-ACH) method is designed to improve the overall performance of the network. The major category which affects the network performance is lack of security and increased energy utilization due to the usage of a huge number of devices in the network. The proposed P2O-ACH method is mainly designed to overcome these issues, such as security and energy utility. The proposed method is subdivided into two sections, namely ROA-based CH selection and the privacy-preserving mobility model. ROA is a rider optimization-based algorithm used for the optimization-based CH selection approach, which concentrates both the energy consumption reduction and reducing the congestion in the network. The privacy-preserving mobility model is used to provide security to the network mainly during the process of new member blending and leaving the cluster in the network. The diagrammatical representation of the proposed P2O-ACH method is given below in Figure 1.  
The proposed P2O-ACH methods concentrated on improving both the energy efficiency and privacy of VANETs. For the purpose, the enhanced ROA-based CH selection and privacy-preserving mobility model are introduced. In the enhanced ROA algorithm, optimization issues are solved, and with the help of the principle of ROA the process of CH is performed so that the CH is highly optimal, which reflects in the reduction of energy consumption, delay, and overhead during communication. On the other hand, the privacy-preserving mobility model is employed, which provides security during the process of node joining and migration process. This is the proposed methodology which is done in this research.
## Appendix A Engine Test-Bench Measurement--Data and Interpretation  
As mentioned in the conclusion, the quality of AI data processing is conditioned by the foremost test-bench measurement accuracies and result uncertainties (Table A1).  
The influence of EBP upon the other engine parameters is presented below.  
The exhaust gas temperature increases with load and, at a constant load, increases with speed. At a full load, the difference of temperature with the reference of 4.9 kPa has range of 20-100 \({}^{\circ}\)C when speed is increased from 1000 to 2200 rpm.  
The brake-specific fuel consumption (BSFC) is defined as the ratio of hourly fuel consumption to effective power. It varies with speed, load, and EBP. As a general tendency, it decreases with rising load and increases at higher EBP levels. Our experimental results revealed that for 1000 rpm, the BSFC increase with EBP was 0.3-2.42%, while for 2200 rpm, it correspondingly rose within 0.3-7.32%, thus representing a significant rise.  
The boost pressure decreased with EBP, with the smallest relative reductions (2.86-5.71%) being measured for 1000 rpm and the highest relative reductions (3.33-30.83%) being measured for 2200 rpm.  
The smoke opacity was proven to significantly increase with EBP on the whole range of speeds and loads as a consequence of reduced air excess ratio and air mass flowrate.  
When EBP increased from 500 to 3000 mmWC, the nonlinear rise of Hartridge smoke unit varied from 6 to 20 HSU, equivalent to the values of light absorption coefficient \(k\) within 0.2-0.5 m\({}^{-1}\) and not exceeding the limits imposed by Economic Commission for Europe ECE 24 regulation on visible emissions.
## 5 Comparative Study of PPDA Protocols
Finally, the PHA can support many aggregation functions and the CPDA can also support the most basic aggregation functions, such as _Sum_ and _Average_. The technique used to achieve privacy-preserving data aggregation for WSNs is the core of our classification model for PPDA protocols. So, based on the Table 1, we describe some important remarks on perturbation, shuffling, privacy homomorphism and hybrid techniques. First, the perturbation technique of homogeneous protocols (PT1) and shuffling technique can protect data privacy from both neighboring sensor nodes and outsiders. But, these two techniques consume a significant amount of energy due to many data transmissions and large computational overhead. On the other hand, the perturbation technique of heterogeneous protocols (PT2), privacy homomorphism and hybrid technique preserve data privacy only from outsiders. Therefore, they are energy efficient because they generate the less number of data traffics in the network. Secondly, PT1, PT2 and hybrid technique provide the data-loss-resiliency feature whereas shuffling technique does not provide the data-loss-resiliency. On the other hand, privacy homomorphism depends on each protocol for offering the data-loss-resiliency. Thirdly, the privacy homomorphism requires less main memory space than PT1, PT2, shuffling, hybrid technique. Specially, PT2 requires a large amount of main memory space. Fourthly, both PT1 and PT2 techniques have larger payload size than those of shuffling and hybrid techniques. Fifthly, only the privacy homomorphism has low transmission delay whereas the rest of techniques have medium or high delay. Sixthly, both PT2 and hybrid technique supports data integrity whereas PT1 and privacy homomorphism does not support it. On the other hand, the shuffling technique is specific to each protocol to support data integrity. Seventhly, the hybrid technique provides many aggregation functions whereas PT1, PT2, shuffling and privacy homomorphism provide the most basic aggregation functions. At last, shuffling, privacy homomorphism and hybrid technique use smaller messages in size than PT1 and PT2. Conclusively, we can see from the viewpoint of both privacy preservation and energy consumption that the perturbation technique of the homogeneous protocols is the best one.
# Multi-Physics Analysis of a Magnetorheological Valve Train with Experimental Validation
## 5 Results and Discussion
### Variable Valve Timing and Lift  
The final experiment conducted in this study was on variable timing and lift. Various timings and lifts were added in combination. The experiments were conducted with valve opening timings at angles of 145\({}^{\circ}\), 175\({}^{\circ}\), and 200\({}^{\circ}\), with valve lifts of 85% and 70%. The maximum current was applied until the valve-opening angle was reached; then, the magnitude of the applied current was reduced to achieve the required lift percentage. Figure 20 shows the results of the experiments conducted with 85% lift and various timings. The control was good in the timing part, and the 85% lift was achieved accurately.  
Figure 21 shows the experimental results with the same varied timing for the valve opening angles of 145\({}^{\circ}\), 175\({}^{\circ}\), and 200\({}^{\circ}\) as well as the lift adjustment of 70%. The required VVT was also achieved efficiently for this lift, and the control was rather good. The designed lift was reached.  
Figure 19: Variable valve timing test results at 100% lift.  
Figure 20: Variable timing and lift test results (85% lift).  
However, for late valve opening, the valve profiles did not coincide with their possible maximum points. As explained, this was caused by magnetic hysteresis. However, this valve train was established to achieve variable profiles, not to achieve a standard profile. By controlling the magnitude of the input current, the shape of the profile can be controlled. All the presented results revealed that the proposed valve train can deliver the desired valve profiles, namely VVT, VVL, VVT and VVL. Consequently, the proposed MR valve train is valid and valuable.  
Moreover, the response time of this valve train is 90 ms (\(\sim\)11 Hz), which means the demagnetization problem is a major issue after 660 rpm. However, this problem can be overcome by using hybrid (positive and negative) current input instead of DC supply. Therefore, further research is needed on the hysteresis effect on MR valve performance and control input signal to achieve the best performance.
## 5 Conclusions  
The test data set that was significantly different demonstrated the limitations of the nonlinear model that used only one parameter (the number of weeks laying) and did not adjust to changes that also resulted in MAPE and RMSPE values--9.134% and 14.809%, respectively.  
Although the calculated errors (MAPE and RMSPE) of the ML models were within the range of 5% to 10%, it was observed that they could better adjust to production changes than the tested nonlinear regression model.  
As the ML models also used environmental data as inputs, sudden changes in those factors (e.g., temperature, CO2, NH3) affected productivity, which could be predicted in a timely manner.  
The results showed that the ML models (LSTM, RF, and XGBoost, with a sliding window size of two) were able to forecast the drop-in production rate (2 nd production cycle) at a satisfactory level.  
The results suggested that the proposed solutions may also be applicable within farms that have limited production data sets and do not have large volumes of historical egg production data.  
Depending on the available historical data for model training, the farm could also employ a multi-model approach, where different models could be run according to the farmer's needs (forecasting length). Furthermore, this also keeps an option to apply the nonlinear model in situations where no environmental or other productivity-affecting data are recorded. In this case, the nonlinear model could be used either as a separate solution or complementary evidence to follow the production curve dynamics.  
Conceptualization, I.A., L.P., N.B. and A.K.; methodology, N.B. and A.K.; software, L.P.; validation, all authors; data curation, M.A.; writing--original draft preparation, N.B. and A.K.; writing--review and editing, I.A. and L.P.; visualization, A.K.; supervision, I.A. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the Specific Objective 1.1.1 \"Improve research and innovation capacity and the ability of Latvian research institutions to attract external funding, by investing in human capital and infrastructure\", 1.1.1.1. measure \"Support for applied research\", project No. 1.1.1.1/19/A/145 \"HENCO2: Cloud based IT platform designed to improve poultry productivity and reduce greenhouse gas emissions\"; and the European Social Fund, project No. 8.2.2.0/20/I/001 \"LLU Transition to a new funding model of doctoral studies\".  
The data used to support the findings of this study have not been made available because the data provider (owners of the farm, where the experiments were conducted and environmental monitoring took place) is a profitable organization that did not give permission to publish the data as part of this study.  
The authors declare no conflict of interest.
## 3 Methodology
### Retrackers  
#### 3.2.1 Threshold Retracker  
The threshold retracker is a simple retracker based on an estimation of the epoch (the leading edge position) as a percentage of the maximum peak ([PERSON] et al., 2016; [PERSON] et al., 2016). In principle, it works well when the maximum peak originates from a nadir water body. The epoch is calculated as the first sample or range bin that is above a percentage of the waveform peak. To provide a higher precision on the estimated epoch, a linear interpolation between the range bins adjacent to the threshold crossing is used as suggested in ([PERSON] et al., 2016):  
\[epoch=n_{th}-1+\left(A_{peak}\cdot\eta_{th}-y(n_{th}-1)\right)/(y(n_{th})-y(n_ {th}-1)) \tag{1}\]  
where \(n_{th}\) is the first range bin of the waveform whose power, \(y(n_{th})\) is right above the threshold (\(A_{peak}\cdot\eta_{th}\)), \(A_{peak}\) is the amplitude of the peak of the waveform, and \(\eta_{th}\) is defined as a threshold percentage (50% in our case). The term \(\left(A_{peak}\cdot\eta_{th}-y(n_{th}-1)\right)/(y(n_{th})-y(n_{th}-1))\) in Equation (1) corresponds to the linear proportionality coefficient.