esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Min-Max Models for Determination of Weights
### Min-Max Model for the Weights of Criteria  
Since any expert should possess a uniform viewpoint on the importance (weight) of each criterion when he/she ranks all the alternatives, the objective function in the first model is to minimize the maximum variation between the actual score vector and the ideal one for all the alternatives such that the optimal weights of criteria are consistent in ranking all the alternatives for the same expert. For example, if an expert is asked to evaluate the academic output of some professors by two criteria: (1) the results in scientific research, such as the number of journal articles, and (2) the completed assignments in teaching, then in evaluating each professor, the expert uses the same weights in principle for the above two evaluation criteria.  
Mathematically, for the given expert \(k\), the above idea can be specified by constructing the following nonlinear optimization model:  
\[\begin{array}{rl}\min_{u^{k}}&\max_{1\leq i\leq n}\left\{\sum \limits_{j=1}^{m}\left(x_{ij}^{k}-u_{j}^{k}\sum\limits_{j=1}^{m}x_{ij}^{k} \right)^{2}\right\}\\ \text{s.t.}&\sum\limits_{j=1}^{m}u_{j}^{k}=1,\\ &0\leq u_{j}^{k}\leq 1,\ \ j=1,2,\cdots,m.\end{array} \tag{3}\]  
It is noted that there have been three main methods to determine the weights of criteria in the literature, which include the variation coefficient method (VCM) in [6], the entropy-based method (EBM) was proposed to calculate the weights of criteria in [7] and the distanced-based method (DBM) in [8]. In order to facilitate a comparison between Model (3) and these three methods, we will summarize these methods in detail in Appendix and show that the weights determined by any one of the above three methods are not the solution of Model (3) in general. In other words, these methods cannot guarantee the uniformity of the importance (weights) of a criterion for the same expert.  
Actually, in the case study given in Section 4, we first obtain the weights of the criteria by different methods (see Table 3 in Section 4). Then, we compute the values of the objective function in (3) corresponding to these weights for all four methods, respectively.  
In Table 1, \(DM_{i}\), \(i=1,2,3\), denote the \(i\)-th decision-maker. From the results in Table 1, it is clear that the uniformity degree of the objective function in Model (3) is different for the four methods. The last row in Table 1 indicates that the uniformity obtained by our method (denoted by Min-Max in Table 1) is the most satisfactory compared to the other three methods.
## 4 Discussion
### Relationship between Capillary Water Absorption and the Pore Structure
#### 4.2.1 Porosities and Capillary Water Absorption Coefficients
When the porosity of the cement-based material was less than 12.87%, namely, the proportion of the gel pores in the total pores was relatively large, the capillary water absorption effect was relatively weak, and rapid water absorption did not occur. With the continuous increase in porosity, the number of transition pores inside the cement-based material increased substantially, and the capillary water absorption effect increased rapidly. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Pore-Size Range** & **Classification** \\ \hline \multirow{5}{*}{[PERSON] [68]} & \(\rho<20\) nm & Harmless pore size \\  & 20 nm \(\leq\)\(\rho\)\(\leq\) 50 nm & Less harmful pore size \\  & 50 nm \(\leq\)\(\rho\)\(\leq\) 200 nm & Harmful pore size \\  & \(\rho>200\) nm & Multi-hazardous pore size \\  & \(\rho<10\) nm & Gel pore \\  & 10 nm \(\leq\)\(\rho\)\(\leq\) 100 nm & Transitional pore \\  & 100 nm \(\leq\)\(\rho\)\(\leq\)1000 nm & Capillary pore \\  & \(\rho>1000\) nm & Macropore \\  & \(\rho<1.2\) nm & Gel microcrystalline Inner \\  & & holes \\ \multirow{2}{*}{Kondo Renichi and Daimon} & \multirow{2}{*}{0.6 nm \(\leq\)\(\rho\)\(\leq\) 1.6 nm} & Gel inter-microrystalline \\  & & Pores/gel pores \\ \multirow{2}{*}{Masaki [70]} & \multirow{2}{*}{3.2 nm \(\leq\)\(\rho\)\(\leq\) 200 nm} & Gel interparticle \\  & & pores/transition pores \\ \multirow{2}{*}{} & \multirow{2}{*}{\(\rho>200\) nm} & Capillary pore/macropore \\ \end{tabular}
\end{table} 
Table 7: The classification of cement stone pores. However, when different specimens had similar porosities inside their structures, their capillary water absorption performance varied substantially due to the state of the pores. Therefore, porosity alone does not reflect the specific pore distribution inside the material and the connectivity between pores. In other words, porosity did not fully represent the pore structure characteristics of cement-based materials, and the main factor affecting the capillary water absorption characteristics was the amount of pores within a certain size range.
# Ultrafast and Wideband Microwave Photonic Frequency-Hopping Systems: A Review
## 2 Microwave Photonic Frequency-Hopping Systems
### MWP RF Switch for Frequency-Hopping Signal Generation  
Microwave switches, or RF switches are an essential tool in microwave systems for signal routing and signal modulation. RF switches usually have switching speed of hundreds of nanoseconds and operation bandwidth of several tens of GHz. To enable fast switching of RF signal for the generation of frequency-hopping signal, microwave photonic RF switches with switching time at tens of picosecond speed [42], which is way faster than any existing RF switch technologies, is a promising candidate. In this section, we will discuss two techniques to achieve high-speed RF switching--(1) Tunable microwave photonics filter, (2) Optical nonlinear wave mixing. The use of tunable microwave photonic filter with tens of gigahertz frequency tuning speed is an effective way to use microwave photonic technology for performing RF switching. MWP filters with single and multiple passbands have been intensively demonstrated using various techniques including the use of optical comb filter [25; 43; 44], stimulated Brillouin scattering (SBS) [24; 45], and liquid crystal on silicon (LCoS) [46; 47]. Various MWP filters could have slightly different pros and cons; for example, the use of SBS filter [24] could give a sharper MWP filter profile and results in a finer frequency resolution in the frequency-hopping  
Figure 7: **(a)** Experimental setup of frequency-hopping signal generation by switching the bias point of an optical dual-drive Mach–Zehnder modulator: LD, laser diode; PC, polarization controller; DD-MZM, dual-drive Mach–Zehnder modulator; MSG, microwave signal generator; PPG, pulse pattern generator; EA, electrical amplifier; PD, photodetector; EBPF, electrical bandpass filter; DSO, digital sampling oscilloscope.; **(b)** Transmission characteristics of the DD-MZM at quadrature (QITP) and minimum (MITP) biasing points. **(c)** The measured frequency-hopping signal waveforms (blue solid line) and the calculated instantaneous frequencies (red dotted line). **(d)** A zoom-in view of the generated frequency-hopping RF signal in the yellow dotted line region in **(c)** (with the courtesy of Dr [PERSON]).  
scheme. The use of an electrical delay line in a MWP filter [25] could limit the operation bandwidth of the frequency-hopping signal. However, most of the techniques have a fixed passband frequency or the tuning mechanism are based on manual control of tunable optical delay line, thermal tuning, or mechanical laser array tuning, which are too slow to achieve fast switching. To perform RF switching, only the techniques that are capable of fast tuning of the passband/bandstop frequency at GHz speed can be used. In this section, we will discuss several techniques for achieving high-speed RF signal switching for the generation of frequency-hopping signals.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 4 Applications of Label Correlation in MLC Methods
### Global or Local MLC Methods Considering Both Positive and Negative Label Correlation  
To enhance classification performance, recent studies have started investigating how to address both positive and negative label correlations within a unified framework. This approach seeks to leverage the intricate relationships between labels more effectively. For instance, the LPLC [33] and LF-LPLC [76] methods integrate negative label correlations into MLC by incorporating them into local pairwise label correlations. The LPLC method identifies the most positively and negatively correlated labels for each true label from the nearest neighbor sets, determined by maximum a posteriori probability. Similarly, the LF-LPLC method employs nearest neighbor techniques to introduce pairwise label negative correlations and incorporates label-specific features, demonstrating robust generalization in MLC tasks. [PERSON] et al. [39] proposed a method that utilizes a Bayesian network to directly model label correlations, where each node represents a label and captures the probabilistic dependencies among them through conditional probabilities. [PERSON] et al. [75] introduced a cost-sensitive MLC model called CPNL, which considers both positive and negative correlations between pairs of labels. [PERSON] et al. [32] enhanced the RakEL method [77] by integrating local label negative correlations and introduced a novel multi-label ensemble classification approach. This approach utilizes KNN techniques to refine the original label membership information and employs matrix similarity measures to identify local pairwise label negative correlations. Additionally, a calibration mechanism is applied during the classification process to modify the ensemble model's output in accordance with label correlations.  
The aforementioned methods primarily focus on exploring negative correlations between pairwise labels, while other methods introduce negative label correlations by investigating the mutual exclusion relationships among multiple labels. To identify the mutual exclusion relationships among multiple labels, [PERSON] et al. [74] utilize the Apriori [79] algorithm to find all maximal sets of mutually exclusive labels, starting from pairwise mutually exclusive labels and progressively identifying triplets, quadruplets, and so forth.  
In general, the approach that incorporates both positive and negative label correlations can more comprehensively consider the complex relationships between labels. These methods that comprehensively consider positive and negative label correlations have certain advantages when dealing with datasets with complex label structures, and they are of significant importance for thoroughly analyzing label relationships to enhance model performance.
# A Stray Light Detection Model for VR Head-Mounted Display Based on Visual Perception
###### Abstract  
In recent years, the general public and the technology industry have favored stereoscopic vision, immersive experience, and real-time visual information reception of virtual reality (VR) and augmented reality (AR). The device carrier, the Head-Mounted Display (HMD), is recognized as one of the next generation's most promising computing and communication platforms. HMD is a virtual image optical display device that combines optical lens modules and binocular displays. The visual impact it brings is much more complicated than the traditional display and also influences the performance of image quality. This research investigated the visual threshold of stray light for three kinds of VR HMD devices, and proposes a qualitative model, derived from psychophysical experiments and the measurement of images on VR devices. The recorded threshold data of the psychophysical stray light perception experiment was used as the target when training. VR display image captured by a wide-angle camera was processed, through a series of image processing procedures, to extract variables in the range of interest. The machine learning algorithm established an evaluation method for human eye-perceived stray light in the study. Four supervised learning algorithms, including K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), were compared. The established model's accuracy was about 90% in all four algorithms. It also proved that different percentages of thresholds could be used to label data according to demand to predict the feasibility of various subdivision inspection specifications in the future. This research aimed to provide a fast and effective stray light qualitative evaluation method to be used as a basis for future HMD optical system design and quality control. Thus, stray light evaluation will become one of the critical indicators of image quality and will be applicable to VR or AR content design.  
st
## 3 Results and Discussion
### Effect of Pin Fin
#### 3.4.3 Effect of Pin Fin Shape  
The reference model has the circular pin fin, and this research chooses two kinds of elliptical pin fins for comparison. Two kinds of pin fins are shown in Figure 20. The major axis of the elliptical I pin fin is parallel to the air flow direction, and that of elliptical II is vertical to the air flow direction. The major diameter of the two kinds of elliptical pin fins is 10.8 mm, which is the same as the diameter of the circle pin fin. The minor diameter is 5.4 mm, which is the same as the radius of the circle pin fin.  
The average Nusselt number of the lower surface under various pin fin shapes at \(Re\) = 2 \(\times\) 10\({}^{3}\)-6 \(\times\) 10\({}^{4}\) is shown in Figure 21. At the low \(Re\), there is little difference in the average Nusselt number on the lower surface under three-pin fin shapes. When \(Re\) increases to 3 \(\times\) 10\({}^{4}\), the average Nusselt number of the lower surface with circle and elliptical I pin fins exceeds that of elliptical II pin fins. Because the windward side of the elliptical II pin fin is large, which hinders the air flow, and increases the turbulence intensity.  
Shown in Figure 22 is \(Nu\) distribution of the lower surface at \(Re\) = 2 \(\times\) 10\({}^{4}\) under different pin fin shapes. The maximum \(Nu\) on the lower surface with elliptical I pin fins is 56.171. It is the largest among the three shapes, followed by the circle.  
Figure 19: Comparison of the frictional factor under various pin fin spacings.  
Figure 20: Schematic of two kinds of elliptic pin fins.  
Figure 23 is a comparison of frictional resistance under three kinds of pin fin shapes. The flow resistance of the impingement cavity with elliptical pin fins are 3.9% higher than that with circle pin fins. Considering the heat transfer and pressure drop, a circle pin fin can be selected.
## 4 Component Design
### Heat Exchanger Design  
As previously mentioned in this paper, the main advantages of the S-CO\({}_{2}\) cycle are that it helps arrange the system in a compact way. It is well known that the overall footprint of the S-CO\({}_{2}\) system is directly related to the heat exchanger sizes among major components of the system because the turbomachinery size of the S-CO\({}_{2}\) system is significantly smaller than the heat exchanger size.  
In this study, all heat exchangers of the modified partial heating cycle with SA heat are designed while assuming that the heat exchanger type is the Printed Circuit Heat Exchanger (PCHE). PCHE is selected to take full benefits such as high surface to volume density, wide operational range of pressure and temperature over 30 MPa and 800 \({}^{\circ}\)C, respectively [30].  
In order to design heat exchangers of the cycle, an in-house PCHE design code, KAIST Heat eXchanger Design (KAIST-HXD) is used. Particularly the KAIST-HXD was developed to evaluate the geometry of the heat exchangers that meets requirements such as heat exchanger effectiveness and pressure drop values [31]. All the heat exchangers were designed with the configuration of a counter current flow and the optimal design conditions were also calculated to obtain the minimum volume while satisfying the target performance requirements. Table 11 summarizes the design result of the heat exchangers and Figure 26 shows the temperature profile in the recuperator.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter.** & **Pre-Cooler** & **Recuperator** \\ \hline Type & PCHE & PCHE \\ \hline Shape & Zig-zag flow channel & Zig-zag flow channel \\ \hline Hot channel fluid & CO\({}_{2}\) & CO\({}_{2}\) \\ \hline Cold channel fluid & Water & CO\({}_{2}\) \\ \hline Hot channel D (semi-circular) & 2.0 mm & 2 mm \\ \hline Cold channel D (semi-circular) & 2.0 mm & 1.8 mm \\ \hline Hot channel No & 8500 & 135,000 \\ \hline Cold channel No & 8500 & 6750 \\ \hline Hot side Re \# (Avg) & 70,215 & 54,602 \\ \hline Cold side Re \# (Avg) & 5008 & 37,820 \\ \hline Length & 0.78 m & 0.96 m \\ \hline Volume & 0.088 m\({}^{3}\) & 0.12389 m\({}^{3}\) \\ \hline Pinch point & 6.1 \({}^{\circ}\)C & 4.97 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 11: Heat exchanger design of the modified partial heating cycle with SA heat.  
Figure 26: Temperature profile of the designed recuperator.
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Antioxidants and Antimicrobials
#### 5.4.8 Neutral Electrolyzed Oxidizing Water (NEW)  
It has been used as an alternative to prolong the shelf-life of minimally processed vegetables. It is generated by an electrolytic cell from tap water or a diluted sodium chloride solution pumped into it. Consequently, the product has a high chlorine effect among other compounds with antimicrobial effect, with a wide sterilization range and posing no threat to health and the environment [113]. Thus, some authors have studied the effect of EOW on some vegetables. That is the case of fresh-cut cabbage being undergone NEW treatment (45 mg/L free chlorine concentration keeping pH at 8.3). Thus, browning has been arrested probably due to PPO oxidization and its consequent weakening. Moreover, PPO might be inhibited by NaCl, due to complex formation between the halide ion and cooper in the enzyme. However, before inactivating the enzyme some phenols might get oxidized, taking place some browning [83]. Another study on fresh-cut lettuce was carried out, showing more inhibition when high free chlorine concentrations (60-120 mg/L) were used, possibly on account of PPO's amino acids or disulfide bounds oxidation. However, the highest free chlorine concentration conveyed loss of turgor, needing the optimization of this parameter as in the case of many other techniques [114].
## 2 Results
### Variability for GPC, Grain Quality Traits, and Yield  
Frequency distribution plots for GPC, grain quality, yield, and yield attributing traits showed transgressive segregants for all the traits in one or both directions (Figures 8-10). This could be due to the combination of favorable and unfavorable alleles from the parents, 'BPT 5204' and 'JAK-686'. Using phenotypic data from the F\({}_{2}\) population, a normal distribution and significant variability was observed, suggesting polygenic inheritance of the traits. The GPC ranged between 7.2% (F\({}_{2}\)-186) and 14.7% (F\({}_{2}\)-140), with an average GPC of 10.1% among the F\({}_{2}\) population. Five F\({}_{2}\) plants, F\({}_{2}\)-140, F\({}_{2}\)-12, F\({}_{2}\)-7, F\({}_{2}\)-147, and F\({}_{2}\)-41, had high GPC values of 14.67%, 14.36%, 14.32%, 13.60%, and 13.36%, respectively. Additionally, their respective GYPP values were 29.03 g, 19.12 g, 16.54 g, 25.90 g, and 15.87 g (Table 4). Figure 7: Box plots illustrating the epistatic interactions for amylose content on chromosomes 6A (RM3408-RM586) and 8B (RM23556-A08P25335); 6A (RM3408-RM586) and 7B (RM21652-RM1132); 3A (RM22-A03P09039) and 9B (RM13021-A09P12377). Figure 8: Frequency distribution of F\({}_{2}\) population for morphological and yield-related traits. Figure 9: Frequency distribution of F\({}_{2}\) population for yield and physical quality traits. Among the quality traits, GC exhibited the highest variation within the F\({}_{2}\) population, ranging from 22 mm to 100 mm, with an average GC of 35.7 mm. AC showed a range from 10.14% (in F\({}_{2}\)-127) to 29.01% (in F\({}_{2}\)-66), with an average AC of 24.05%. Among the yield attributing traits, GPP, followed by PH and GYPP, showed the highest variation in the F\({}_{2}\) population. The values ranged from 24 to 169 for GPP, 65 cm to 143 cm for PH, and 5.12 g to 55.04 g for GYPP. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Genotypes**} & **GPC** & **PH** & **PLT** & **GPP** & **TGW** & **GYPP** & **DFF** & **AC** & **GC** & **KL** & **KB** & **LB** \\  & **  
Correlation studies revealed a negative association of PC with both AC and GC. AC and GC were also negatively correlated (Table 5). AC showed a significant negative correlation with KL and L:B, but a positive correlation with KB. KL had a significant positive correlation with multiple yield-related traits, including GYPP. However, KB showed a significant positive association only with TGW. GPP and TGW exhibited a significant positive correlation with KL, PTPP, and GYPP. GYPP showed a positive and significant association with TGW, GPP, PL, and PTPP. Among the thirteen traits, the present work revealed high heritability with moderate GAM for TGW (61.4, 14.1), DFF (68.5, 17.3), and AC (75.4, 16.6). We observed high heritability and high GAM for PH (93, 25.3), PTPP (89.2, 71.7), PL (96.4, 28.6), GPP (72.0, 34.3), GYPP (97, 80.8), and GC (90.3, 76.9). The study detected moderate heritability and GAM for GPC (52.4, 16.2). Further, we observed low heritability and GAM for KL (35.1, 2.8), KB (44.9, 4.8), and L:B (33.8, 4.6; Table 6).
# Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
## 4 Results of the Breakdown Test Procedure
### TE Sample  
Figure 7 shows the recorded PRPD patterns of a representative sample (one of the eight tested samples) during the TE sample breakdown test. The PDIV was measured at 5.5 kV, as shown in Figure 7. The results indicated that the positive half-cycle discharge was larger and more concentrated, exhibiting a vertical growth pattern similar to that of the negative half-cycle observed in Figure 4. The negative half-cycle discharge was concentrated at approximately 10 pC with no connection to the discharge at 5 pC.  
Figure 7 shows the PRPD pattern at an RPDIV of 9 kV. Compared to Figure 4, the discharge phases were increasingly distributed across the first and third quadrants. The overall pattern was more complete, although a noticeable gap appeared in the lower-right  
Figure 6: PE sample breakdown test results.  
Figure 7: Breakdown test results of the TE sample.  
corner of the first quadrant. In contrast, the discharge in the third quadrant was denser and larger, resembling the typical surface discharge patterns.  
Figure 6(c) shows the final PRPD pattern recorded immediately before breakdown of the sample, which ultimately occurred at 17 kV. The maximum discharge was approximately 30 nC. Unlike Figure 6(c), the PRPD pattern in Figure 6(c) captured the chaotic discharges at the moment of breakdown. The corresponding discharge activity and voltage timing diagram with a pressure duration of approximately 42 min is shown in Figure 6(d).  
Figure 8 shows the images of the TE sample before and after the breakdown test, with the breakdown location highlighted by a red circle. Although the exact position of the carbonization could not be determined from the surface observations, it was located at the junction between the two electrodes. The epoxy resin covering the breakdown area showed signs of cracking, but did not detach.  
Figure 9 shows the statistical results compiled from the eight TE samples that completed the breakdown tests. The average PDIV value was 6.13 kV, the average RPDIV was 8.04 kV, and the average breakdown voltage of the samples was 14.9 kV.