esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Model
### Balanced Failure Point  
For the eccentric compression member, there is always a point in the M-N curve that makes the steel bars and the compressive concrete reach ultimate strength simultaneously. This point is the so-called balanced failure point, which is also the boundary point between large and small eccentric compression member. At this point, the eccentric compression member achieves the maximum bending moment that it can bear. Thus, the balanced failure point is also termed as the peak point. For the corroded eccentric compression members, how to determine the relative height of the compression zone corresponding to the balanced failure point is critical.  
The relative height of the compression zone is relative to the elastic modulus and yield strength of reinforcement. The elastic modulus changes little during the corrosion process, and the yield strength decreases after the corrosion of reinforcing bars. Corrosion of reinforcing bars results in deterioration of the bond performance between reinforcing bars and concrete, which destroys the strain compatibility between reinforcing bars and concrete. A modified model for calculating the relative height considering the corrosion is established in [36], which is given as  
\[\xi_{\text{bc}}=x_{\text{bc}}/h_{0}=\frac{\beta_{2}}{1+\frac{\Phi(\eta_{s})f_ {\text{red}}}{\epsilon_{\text{cu}}\,\xi_{\text{s}}}}, \tag{31}\]  
\[\Phi(\eta_{s})=0.27152\eta_{s}+1, \tag{32}\]  
where \(x_{\text{bc}}\) represents the height of the compression zone; \(h_{0}\) is the effective depth of section, \(h_{0}=H-a_{s}^{\prime}\); \(\Phi(\eta_{s})\) represents the strain incompatibility coefficient; \(f_{\text{red}}\) is the nominal yield strength of the corroded steel bar; \(\epsilon_{\text{cu}}\) is the ultimate strain of concrete, \(\epsilon_{\text{cu}}=0.0033\) in this study; and \(\beta_{2}\) is coefficient with \(\beta_{2}=0.74\,\sim 0.80\) when the strength grade of concrete changes from C80 to C50.  
Figure 5: Distribution of stress and strain in the pure bending section. \(c_{cu}\): the height of compression zone in concrete; \(x\): the equivalent rectangle height of the compression zone in concrete; \(\epsilon_{cu}\): the ultimate compression strain of concrete; \(a_{s}^{\prime}\): the distance between the resultant force point of reinforcement and compression edge of the section; \(\varepsilon_{si}\): the ultimate tensile strain of the steel bar.  
The axial force and bending moment of the balanced failure point can be obtained by  
\[N_{B}=\alpha_{1}x_{\rm bc}f_{cc}b+A_{\rm f,red}(\beta-\gamma)f_{y}, \tag{33}\]  
\[M_{B}=A_{\rm f,red}(\gamma+\beta)f_{y}\bigg{(}\frac{H}{2}-a_{s}^{\prime}\bigg{)} +\alpha_{1}x_{\rm bc}f_{cc}b\bigg{(}\frac{H}{2}-\frac{x_{\rm bc}}{2}\bigg{)}. \tag{34}\]
## 4 Interval Parameter Identification Procedure
### Searching the Eventual Result  
In contrast to the deterministic framework, the traditional optimization algorithms may not be able to deal with the uncertain system better. To improve the accuracy and efficiency of the parameter identification, the multi-level optimization-based strategy was constructed, which mainly included two steps: rough search and fine search.  
Note from Figure 7, the procedure is executed in the order of rough search and fine search. In the rough search, the initial interval of unknown parameters is defined according to the value calculated above. By introducing the constriction factor, the process of rough search can obtain a group of identification results rapidly. If the result can not meet the requirement of precision, the procedure will automatically move to the stage of the fine search. In this stage, numerous samples will be randomly generated near the above identification results to search the optimal results. The detailed process is shown in Figure 7.  
For the calculation of the system predicted interval, various methods have been tried, for instance, interval arithmetic, series expansion, perturbation, optimization, among others. However, these methods cannot show active inclusiveness for solving complex nonlinear problems. Therefore, Monte Carlo is adopted as a simple and effective method. By sampling a large number of parameter intervals, the interval of the objective function can be calculated rapidly. The proposed multi-level optimization-based strategy can identify the unknown parameter of uncertain systems efficiently and accurately.
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 4 Overlapped Circles Localization
### Radii Detection  
The next step after finding the center is to calculate the radius of each circle. In fact, the radius cannot be simply selected by the minimum distance from the center of the circle to the edge, because the minimum distance may reach the edge of another circle. Therefore, in this study, the minimum  
Figure 6: Three dimensional map of the distance function.  
rotation vector method is used to find the radius, it is a fast search algorithm, the search range includes local and global search, so the results can be obtained accurately and quickly. The flow chart is shown in Figure 7.  
After six vectors are obtained, the shortest vector length is the radius to be found. The basic steps are as follows:  
1. set the number of vectors = 1, at the maximum value, take the unit vector in any direction;
2. to judge whether the vector end reaches the edge, if so, turn to Step 3; if not, the direction no change, length + 1, repeat this step;
3. to judge the vector number = 6, if so, store them and turn to Step 5, otherwise turn to step 4;
4. store the vector and rotate it 60 degree, reset length = 1, number +1, return to Step 2;
5. the minimum value of these six vectors is the radius.
# A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems
## 3 Fuzzy DEMATEL for Object-Oriented Metrics
### Research Methodology  
This research is based on Fuzzy DEMATEL, because of its suitability for evaluating expert answers. In fact, evaluating the importance of a metric is very subjective depending on each expert experience. Thus, it cannot be assessed by crisp values. The concept of fuzzy sets [21] combined with DEMATEL methods perfectly handles the vagueness of expert answers. To deal with this imprecise decision-making problem, we adopt the triangular fuzzy number identified by [PERSON] and [PERSON] [22]. This fuzzy representation is defined by a set of values (Lower, Medium, Upper). The influence of each metric is measured over a five-level fuzzy scale, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Description** & **Representation** \\ \hline NC & Number of classes & C1 \\ \hline PS & Package size & C2 \\ \hline NOA & Number of attributes & C3 \\ \hline NOM & Number of methods & C4 \\ \hline NOD & Number of descendent classes (Inheritance) & C5 \\ \hline NODD & Number of direct descendent classes (Direct inheritance) & C6 \\ \hline NMSC & Number of messages send from a class to itself (Internal-messages) & C7 \\ \hline NOC & Number of messages sent for other classes & C8 \\ \hline NCC & Number of classes affected by the measured class & C9 \\ \hline ATFD & Access To foreign data & C10 \\ \hline NOP & Number of parameters & C11 \\ \hline NIC & Number of interconnected classes/Number of classes affected by the measured method & C12 \\ \hline CM & Number of methods affected by the measured method & C13 \\ \hline NOPM & Number of packages in the model & C14 \\ \hline PUC & Percentage of used classes/Number of classes used outside the measured package & C15 \\ \hline NMR & Number of messages received by a class & C16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Object-oriented metrics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Linguistic Terms** & **Triangular Fuzzy Numbers** \\ \hline No influence (NO) & (0, 0, 0.25) \\ \hline Very low influence (VL) & (0, 0.25, 0.5) \\ \hline Low influence (L) & (0.25, 0.5, 0.75) \\ \hline High influence (H) & (0.5, 0.75, 1) \\ \hline Very high influence (VH) & (0.75, 1, 1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy linguistic scale.  
After the selection of the set of metrics and the experts in the object-oriented programming field, the experts evaluate the effect between metrics using a pairwise comparison, and we start the process of generating and normalizing the Fuzzy Direct-Relation Matrix (FDRM) as an aggregation of all expert matrices. Then, we calculate the total-relation fuzzy matrix, and we obtain as the final step the classification of metrics based on their importance and influence. Finally, we validate our findings from our previous work [12], by refining the set of the rules-based metrics identified in [12] regarding the metrics importance and influence found using Fuzzy DEMATEL. We present in Figure 1 the main steps to apply Fuzzy DEMATEL.
## 4 Conclusions  
MESS spectroscopy of banknotes was measured for the first time, and the spectra were very rich in information (many peaks) and reproducible. The spectra provide a clear distinction between authentic and counterfeit bills, as well as a distinction between the different forgery processes used. The distinction is both in peak locations and intensities. It seems that MEES provides unique fingerprinting of banknotes, which might be beneficial in forensic investigations. Although several forensic methods may solve the problem is simple cases, and powerful analytical methods can be used in most difficult cases, MEES might still be useful: it operates under ambient conditions, is fast, and is very sensitive. Its performance (in terms of sensitivity and analytical information) was shown to be superior to most other spectroscopic techniques [27]. Irradiation of banknotes with UV pulses causes a certain level of bleaching, so in principle, the method cannot be considered as nondestructive. However, the sensitivity of MEES is so high that considerable reduction of the irradiation intensity can be carried out while still obtaining a good distinction between forged and authentic items. Under such conditions, the method can be considered as almost nondestructive.  
The MEES technique, which is demonstrated in this particular application, might also be useful for investigation of the early stages of laser-matter interaction, when the irradiance on the sample is lower than the plasma formation threshold. The first stages in plasma formation involve multiphoton ionization, and MEES spectroscopy reveals sensitivity to laser wavelength and to molecular composition. This results in dependence of plasma formation threshold on wavelength and molecular composition (unpublished results; further investigation is needed). Another potential development would be combining MEES with LIBS: first, MEES provides molecular analysis, and then, the irradiance is increased for plasma generation and LIBS elemental analysis.  
Conceptualization, D.F.; methodology, D.F. and V.B.; validation, formal analysis, investigation, data curation, and writing--original draft preparation, D.F.; writing--review and editing, D.F., V.B. and I.S.; supervision, V.B. and I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All relevant data is included in the article.  
The collaboration with the Israel Police Questionnaire Locuments Laboratory, Division of Identification and Forensic Science (DIFS), Israel Police and the Currency Department, Bank of Israel, is much appreciated.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Peak/nm** & **Authentic** & **Counterfeit** \\ \hline
212 & - & + \\ \hline
220 & - & + \\ \hline
224 & - & + \\ \hline
237 & - & + \\ \hline
250 & - & + \\ \hline
294 & + & - \\ \hline \end{tabular}
\end{table}
Table 6: List of main peaks which are missing in the authentic 100 NIS banknote and present in the counterfeit (and vice versa).
## 4 Simulation and Experimental Validation  
### Fast Estimation Method of Milling Force Fluctuations  
The characteristics of milling force fluctuations have been discussed in detail in the previous section, but the derived theory is a qualitative description. In order to quantify the intensity of force fluctuation, two prediction methods are proposed: fast estimation and accurate simulation. The fast estimation method introduces an intensity indicator related to the axial depth of cut to approximately estimate the force fluctuation.  
Due to the periodicity and continuity characteristics, the fluctuation of the milling force must have minimum and maximum values in a period. Therefore, the intensity indicator of the milling force fluctuation can be expressed by a value varying from zero to 100%. Then, the symmetry characteristic makes the fluctuation of the first and second half periods symmetrical. Hence, milling force fluctuations in a period can be divided into two parts: the ascending part and the descending part. These two parts share the converse indicator. Therefore, the research range of \(A_{p}\) is reduced to [0, \(A_{poc}/2\)]. According to Equations (8) and (9), the milling force increases with the value of \(A_{p}\) when the engaged angle \(\theta_{en}\) is bigger than \((A_{p}\times tan(\beta)/R)\). For fast estimation, assume that the relationship between \(A_{p}\) and \(F_{pp}\) are approximated to a sine function. Then, the intensity indicator (\(l_{i}\)) for fast estimation of the milling force fluctuation can be expressed as:  
\[l_{i}=\bigg{|}sin\bigg{(}\frac{\pi A_{p}}{A_{poc}}\bigg{)}\bigg{|}\times 100\%, \bigg{(}\theta_{en}\geq\frac{A_{p}tan\beta}{R}\bigg{)}. \tag{27}\]  
where,  
\[\theta_{en}=\begin{cases}\theta_{ex}-\theta_{st}&,\text{ }down\text{ } milling\\ \theta_{st}-\theta_{ex}&,\text{ }up\text{ } milling\end{cases}. \tag{28}\]  
It should be noted that the intensity indicator is an approximate value for a fast estimate and it is affected by \(A_{c}\), so it can be used to estimate the milling force fluctuation only with a low precision requirement.  
Figure 6: The symmetry characteristics of force fluctuation.
## 3 System Model  
In this study, we propose a D2D-assisted MEC server computation offloading and resource allocation scheme for multi-access edge computing scenarios. This model aims to minimize the total offloading cost, including task execution time and energy consumption. The proposed system model, as shown in Figure 2, is crucial to understanding how UE can optimize offloading decisions based on the device state, proximity to computational resources, and the nature of computational tasks. The system model consists of three parts: the network model, the communication model, and the computational model, which will be introduced in detail.
## 2 Methodology
### Ship Motions  
The vessel under consideration in the study has an overall length of approximately 230 m and a breadth of about 32 m. Therefore, in the analysis, the effects of wind and sea currents can be considered negligible compared to the effects of waves.  
Since the ATP system is placed on the ship's deck, system operations could be influenced by ship motions that result in system oscillations in magnitude depending on the navigation conditions.  
While the effects arising from the angular motions of the ship are the most influential, translational motions have a lesser impact on the pointing system, introducing a very insignificant error, and will not be considered in the analysis.  
In the model, ship motions are considered as Euler angular displacement quantities of the ship's deck with the roll-pitch-yaw convention presented in Figure 4.  
Several sea conditions about vessel type considered for the investigation have been previous analyzed in [20; 21; 22]. This work focuses on the environmental conditions from [23; 24], considering long-crested waves and assuming a Bretschneider spectrum (Table 2). Ship motions have been predicted by a linear seakeeping approach that, for example, was also reported in [25; 26].  
In Table 2, the acronym SWH refers to Significant Wave Height, which is defined as four times the standard deviation of the surface elevation [27], while Heading is the relative angle between the direction of the vessel and the propagation direction of the waves.  
As introduced, ship motions are expressed as the Euler angular displacement of the ship deck; Figure 5 presents an overview of ship motions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario** & **Heading** & **Ship Speed (kn)** & **Peak Period (s)** & **SWH (m)** \\ \hline
01 & 90\({}^{\circ}\) Beam sea & 0 & 23.2 & 14.3 \\
02 & 90\({}^{\circ}\) Beam sea & 0 & 16.2 & 10.8 \\
03 & 180\({}^{\circ}\) Head sea & 5 & 20.4 & 13.1 \\
04 & 180\({}^{\circ}\) Head sea & 10 & 20.4 & 13.1 \\ \hline \end{tabular}
\end{table}
Table 2: Overview of sea scenarios considered.  
Figure 4: RPY convention.  
The acquisition phase of ship motions is affected by GPS/INS unit attitude measurement accuracy. Following the study presented in [28], it is possible to define the Euler's angular error quantities (Table 3):  
And then, transpose it to the azimuth and elevation DoFs of the ATP system (Table 4).
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 6 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Set** & **Methods** & **MI** & **FMI** & **PSNR** & **SD** & **QY** & **QViF** & **TIME** \\ \hline \multirow{3}{*}{Color multi-focus set} & CNN & 1.1512 & 0.8913 & 36.4127 & 10.1764 & 0.9801 & 1.6011 & 85.1135 \\  & Ours & 1.1235 & 0.9010 & 36.4724 & 10.2797 & 0.9791 & 1.6023 & 1.0652 \\ \multirow{3}{*}{Gray multi-focus set} & CNN & 0.9976 & 0.8762 & 35.4385 & 9.9277 & 0.9386 & 1.4995 & 49.1315 \\  & Ours & 1.0506 & 0.8897 & 35.7860 & 10.1688 & 0.9616 & 1.5071 & 0.3329 \\ \multirow{3}{*}{Medical multi-modal set} & CNN & 0.7362 & 0.8928 & 31.4792 & 9.5564 & 0.7982 & 0.8869 & 24.9169 \\  & Ours & 0.7819 & 0.8984 & 32.1865 & 9.5780 & 0.9616 & 0.9579 & 1.1853 \\ \multirow{3}{*}{Infrared and visual sets} & CNN & 0.6283 & 0.7936 & 28.5707 & 8.5180 & 0.7920 & 0.8163 & 23.1417 \\  & Ours & 0.5300 & 0.8991 & 29.3041 & 8.9991 & 0.8328 & 0.9826 & 1.0568 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of deep convolutional neural network and the algorithm in this paper on 4 data sets.
## 3 Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC)
### Structure of Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC)
#### 3.2.6 Maximum-Minimum Crossover (MMC)  
The crossover genetic operator acts at the local search, extracting parts of two individuals (parents) and combining them to form the offspring (children), in order to get a better fit. Without this crossover, a random search takes place. By contrast, the union between selection and crossover performs a local search near the best solutions, being the GA main search tool [21]. In GA-MMC, the differentiated coding mode, inspired by the physical structure of PA (Figure 9), enables the crossover to make the direct exchange of genes from parents, using real numbers. Without the need to convert into binary system, such as in conventional GA, processing time is quite reduced.  
Furthermore, in PARs, the search spaces are vast. Thus, a crossover only with fittest individuals accelerates the convergence, but it requires high mutation rates to maintain the genetic diversity and global search, which increases processing time. Therefore, a remarkable feature of the developed GA is the new genetic operator Maximum-Minimum Crossover (MMC), which combines both the most and least fit individuals to increase genetic diversity. In Figure 10, there is an illustration of selection and crossover in GA-MMC. To control this process, a crossover rate, \(T_{C}\), is used, that determines whether the crossover is applied between a pair of individuals. The tests, taking into account the fitness value fitness, indicated a fixed rate \(T_{C}=35\%\).  
In GA-MMC tests, each situation is simulated 10 times [55] to verify the ability of convergence and repeatability of GA results, given it is a probabilistic method. Therefore, simulations for controlling the main lobe tilt and maximizing RSLL were performed 10 times with conventional crossover, and another 10 times with MMC. In both cases, there was a success rate equal to or greater than 80% in tilt angle. However, GA-MMC allowed an average improvement of 1.6 dB in RSLL and of 20.9% in  
Figure 10: Selection and crossover in GA-MMC [14].  
fitness. Therefore, MMC conducts a better search space scanning, avoiding premature convergence and high mutation rates.
## 2 Materials and Methods
### Numerical Study
#### 2.3.2 Description of Ground Motion Sets (GMS)  
Two different GMSs have been considered: a single representative accelerogram (referred to as EUK), and a set of accelerograms (referred to as ESB). All motions have been taken from the European Strong Motion Database [40; 41]. Only horizontal components are considered.  
Set EUK comprises only the X-component of the Kalamata Earthquake (000414 XA in the database), a far-field accelerogram recorded at ground type B (360 m/s \(\leq\)\(v_{s,30}\) < 800 m/as defined in Eurocode 8) with peak horizontal acceleration \(PHA=0.24\) g, peak horizontal velocity \(PHV=31.5\) cm/s and peak horizontal displacement \(PHD=6.57\) cm, epicentral distance of 11 km, focal depth of 1 km, Arias Intensity \(I_{A}=0.553\) m/s and significant duration (as defined by [PERSON] and Mendis [42]) \(D_{5-95\%}=5.13\) s. The accelerogram is normalized by its PHA. The 5% elastic spectrum of this normalized accelerogram, shown in Figure A1 (Appendix A) approaches reasonably well the Eurocode 8 5% normalized elastic response spectrum throughout a wide range of periods.  
Set ESB is formed by 20 non-impulsive motions, with magnitude Mw \(\geq\) 5.5, epicentral distance from 10 km to 50 km, recorded at firm soil (ground type B). The motion selection is listed in Table A1, and the statistical properties of the motions are given in Table A2 (Appendix A). Both horizontal components from every record are consistently included in the set, adding up a total of 40 accelerograms. The motions are individually normalized by their PHA and pre-scaled so that the mean 5% elastic spectrum of the pre-scaled set approaches well the 5% Eurocode 8 elastic response spectrum over a wide range of periods. Spectra of pre-scaled accelerograms are shown in Figure A2 (Appendix A). TH response for set ESB is taken as the median (\(\hat{x}\)) of individual responses to each accelerogram in  
Figure 6: Description of building and frames used in the study: (**a**) plan layout; (**b**) definition of frame.  
the set, assuming a lognormal distribution; correspondingly, the 16% (\(x^{16}\)) and 84% (\(x^{84}\)) percentiles are taken as indicative of data dispersion:  
\[\hat{x}=\exp(\mu(\ln x_{i})), \tag{15}\]  
\[x^{16}=\hat{x}/\exp(\sigma(\ln x_{i})), \tag{16}\]  
\[x^{84}=\hat{x}\exp(\sigma(\ln x_{i})), \tag{17}\]  
where, \(x_{i}\) is the individual response to the \(i\)-th accelerogram, \(\mu(\cdot)\) represents mean and \(\sigma(\cdot)\) standard deviation. Time history analysis is extended 10 s after the end of the recorded signal, in order to achieve a state of negligible motion in the structural system.