esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Table Grape Cultivars on Fruit Quality and Aroma Components
## 3 Results and Discussion
### Principal Component Analysis (PCA) and PLS-DA of Different Table Grape Varieties  
In the comprehensive evaluation of the quality of table grapes, the appearance, flavor, texture, and other factors of the fruit should be considered, as well as the nutritional value of the fruit, such as soluble solids and titratable acids, to ensure the comprehensive evaluation of information. The PCA is a multivariable data analysis technique for dimensionality reduction and showing relationships/correlations between variables and samples [36; 37]. In this study, 11 main indices that can reflect fruit quality were selected for a comprehensive evaluation of grapes using PCA, and the results were objective. The first two principal components explained 53.4% of the original variables, and varieties C, B, and I were more affected by acids. Varieties M and G had higher contents of aroma components, which could be classified into one group, variety A had higher contents of vitamin C, variety H had higher contents of tannin and C6 compounds, and variety I had higher contents of vitamin C than other varieties, which were close to the principal component axis (Figure 3a).  
Figure 3: _Cont._Figure 3: Principal component analysis and partial least squares discriminant analysis of different table grape varieties. Biplots of aroma (**c**) and quality index (**a**) of different table grape varieties; (**b**) Partial least squares discriminant analysis (PLS-DA). The circles represent aroma compounds, and the pentagram represents grape varieties.  
Biplots (score plots combined with loading plots, Figure 3c) of the PCA showed that 68.7% of the variance was explained by 80 different aroma components, with PC1 and PC2 accounting for 56.7% and 12% of the variance, respectively. Except for varieties C, G, L, F, A, N, and K, the other varieties were not well differentiated. However, the L, G, F, A, N, and K varieties were located at the positive end of the second principal component. The other varieties were located at the negative end of the second principal component, and the difference in positions was obvious.  
To explain the experimental results more comprehensively, PLS-DA was used to analyze the obtained data. As shown in Figure 3b, the first two principal components (PC1 and PC2) accounted for 50.2% of all variables. The results showed that 17 varieties could be clearly distinguished from each other. The results were consistent with PCA (Figure 3a), further validating the objectivity and rationality of the data analysis.
# End-Users' Augmented Reality Utilization for Architectural Design Review
## 4 Results
### User Experience  
Table 6 summarizes the UEQ results for the AR system. The UEQ does not produce an overall score for the user experience. The scale of an item ranges from \(-3\) (horribly bad) to \(+3\) (extremely good). The result values between \(-0.8\) and \(0.8\) represent a neutral evaluation of the corresponding scale, with values over \(0.8\) describing a positive evaluation, and values under \(-0.8\) describing a negative evaluation. In that respect, the satisfaction level of the user experience of the AR system was positively evaluated in each category. It received the highest evaluation in terms of novelty (M = 1.75) and the lowest evaluation in efficiency (M = 0.98). The UEQ scales can be grouped into pragmatic quality (perspicuity, efficiency, and dependability) and hedonic quality (stimulation and originality). Pragmatic quality describes task-related quality aspects, while hedonic quality describes non-task-related quality aspects. The mean of the pragmatic quality aspects was 1.11, and the mean of hedonic quality aspects was 1.73. The Cronbach-Alpha coefficients in Table 6 measure the consistency of a scale from the UEQ survey. Though there are no clear rules describing how big the Alpha coefficient should be, values larger than 0.6 or 0.7 are considered sufficient. According to the data collected, these responses had sufficient scale consistency.
# Unfair and Risky? Profit Allocation in Closed-Loop Supply Chains by Cooperative Game Approaches
## Appendix B
With first-order conditions \(\frac{\partial u_{F}^{DC}}{\partial p_{n}^{F}}=0\) and \(\frac{\partial u_{F}^{DC}}{\partial p_{r}^{F}}=0\), F's optimal response functions are obtained as \(p_{n}^{F^{*}}\left(w_{n}^{F},w_{r}^{F}\right)=\frac{\lambda+w_{n}^{F}-\lambda c _{n}+2\lambda w_{n}^{F}+1}{2\left(\lambda+1\right)}\), \(p_{r}^{F^{*}}\left(w_{n}^{F},w_{r}^{F}\right)=\frac{\delta+w_{r}^{F}+\delta \lambda-\lambda c_{r}-\lambda A+2\lambda w_{r}^{F}}{2\left(\lambda+1\right)}\). By substituting \(p_{n}^{F^{*}}\left(w_{n}^{F},w_{r}^{F}\right)\) and \(p_{r}^{F^{*}}\left(w_{n}^{F},w_{r}^{F}\right)\) into demand functions \(q_{n}^{F^{*}}\left(p_{n}^{F},p_{r}^{F}\right)\) and \(q_{r}^{F*}\left(p_{n}^{F},p_{r}^{F}\right)\), the optimal quantities \(q_{n}^{F*}\left(p_{n}^{F},p_{r}^{F}\right)\) and \(q_{r}^{F*}\left(p_{n}^{F},p_{r}^{F}\right)\) for F are obtained as functions composed of wholesale prices \(w_{n}^{F}\) and \(w_{r}^{F}\). By plugging \(q_{n}^{L*}\left(w_{n}^{L},w_{r}^{L}\right)\), \(q_{r}^{L*}\left(w_{n}^{L},w_{r}^{L}\right)\), \(q_{r}^{F*}\left(p_{n}^{F},p_{r}^{F}\right)\) and \(q_{r}^{F*}\left(p_{n}^{F},p_{r}^{F}\right)\) into M's profit function \(\pi_{M}^{DC}\) with the first-order conditions \(\frac{\partial\pi_{M}^{DC}}{\partial w_{r}^{L}}=0\), \(\frac{\partial\pi_{M}^{DC}}{\partial w_{r}^{L}}=0\), \(\frac{\partial\pi_{M}^{DC}}{\partial w_{r}^{L}}=0\) and \(\frac{\partial\pi_{M}^{DC}}{\partial w_{r}^{L}}=0\), M's optimal decisions are obtained as \(w_{n}^{L*}=\frac{c_{n}+1}{2}\), \(w_{r}^{L*}=\frac{c_{r}+A+\delta}{2}\), \(w_{n}^{F*}=\frac{\lambda+c_{n}+3\lambda c_{n}+1}{4\lambda+2}\) and \(w_{r}^{F*}=\frac{c_{r}+A+\delta+3\lambda c_{r}+3\lambda A+\lambda\delta}{4 \lambda+2}\). Subsequently, the channel and individual equilibrium profits are calculated, respectively, as \(\pi_{M}^{*DC}\), \(\pi_{L}^{*DC}\), \(\pi_{F}^{*DC}\), and \(\pi_{G}^{*DC}\). Proposition 2 is therefore established. \(\square\)
## 3 Results and Discussions
### Structural Characteristics of O/W-Type Nanoemulsion Formulation  
The Cryo-TEM micrographs of the ACM stabilized O/W-type nanoemulsion showed that ACM encapsulated the O/W-type nanoemulsion and formed a consistent aggregate structure (Figure 3). Cryo-Tem images revealed that ACM encapsulated the micelles at oil/water interfaces of the nanoemulsion to ensure stability (Figure 3). This was evidenced by the smaller particle sizes of nanoemulsions with higher ACM-EOR (Figure 3). Authors have reported that emulsifiers adsorb at the oil and water interface to enable dispersion of oil droplets in water [60; 61]. Additionally, emulsifiers adsorb more to the surface of smaller droplets to ensure that the emulsion is stable [61]. Authors have reported that an emulsion can attain stability when its particle size is reduced [8; 17; 62]. The literature established that the lower the particle size the more stable an emulsion becomes [8; 18]. Hence, it can be inferred that ACM encapsulated the oil/water interfaces of the nanoemulsion to ensure stability as depicted by lower particle size.  
As a result of the mucoadhesive nature of ACM and the microstructural stability of the nanoemulsion formulated, it could be used in the controlled release of drugs by reducing the speed of drug absorption by the body. Therefore, the structural stability achieved in O/W nanoemulsions can be employed in biomedical and personal care applications.  
Figure 3: Structural characteristics of ACM stabilized O/W-type nanoemulsions with emulsifier-ACM oil ratios of 1% (**a**–**d**), midpoints (**e**–**g**), and 5% (**h**–**k**).
###### Abstract  
To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4-0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.  
wireless sensor network; AGglomerative NESting; dual-cluster heads; dormancy; throughput; lifetime +
Footnote †: journal:  
_Article_  
An Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on AGNES with Balanced Energy Consumption Optimization  
[PERSON], [PERSON], [PERSON], and [PERSON] (2018)
# Emergency Evacuation Simulation Study Based on Improved YOLOv5s and Anylogic
## 3 Analysis of Experimental Results
### Analysis of Simulation Results Based on the Anrologic Emergency Evacuation Model
To achieve this function, we first set a local variable k and a large enough initial value var, and create a collection of all passable exits color, when the alarm button is triggered, pedestrians start to follow the process of selecting the exit they want to go to, first calculate the distance from the current position to the first exit, then compare the obtained distance with var, if the value is less than var then the value will be given to var, if the value is greater than var then go back to the previous step of the loop, to avoid the problem of infinite loops when comparing with var, we must set the value of var large enough to ensure that the value of var is always larger for the first time so that the pedestrian will compare the distance to each exit, at this time the value of I will also be equal to the value of the set of exits, jump out of the loop and outputs the value of k. The value of k is the nearest exit serial number, while the value of var is the distance from the pedestrian's current position to exit k, i.e., the shortest distance. Following the above process, each pedestrian chooses the nearest exit or staircase to him or her. Once the evacuation route is determined, the pedestrian moves along that route. To see the trajectory of the pedestrian movement more clearly, we have added a pedestrian density map here, as shown in Figure 12.  
The different colors in the simulation results represent different densities, from blue, green, yellow, and red, in four levels from small to large. Looking at Figure 7, we can see that pedestrians on the ground floor evacuate according to the nearest exit, while those on the negative floor go up the nearest stairs or lift first and then follow the nearest exit when they reach the ground floor. If an exit cannot be opened for a particular reason, it is also possible to delete or close that exit in the model and re-run the simulation to quickly obtain a new planned path. The results of this experiment also show that in unexpected situations, Anylogic can not only provide a quick evacuation plan but that the plan can also guide pedestrians away from the nearest exit.
## 5 Simulation Analysis
### System Parameters for Fault Scenarios
#### 5.1.2 Single Fault Effect Analysis under Closed-Loop System Conditions
The first interval matrix represents the system performance under normal conditions, which can be described as \(\mathbf{\psi}_{1}=diag[1,1]\). Therefore, the \((\text{k}-1)\) interval degradation matrix corresponding to the \((\text{k}-1)\) level degree of fault is \(\mathbf{\psi}_{\text{k}}=diag\{\mathbf{\psi}_{\text{k}3},\mathbf{\psi}_{\text{k}4}\}^{T} =\mathbf{\psi}_{1}-(\text{k}-1)\frac{(\mathbf{\psi}_{1}-\mathbf{\psi}  
form system with different gradual faults level can be obtained. In this paper, the largest interval matrix is divided into three intervals, resulting in the four system performance status: (a) system in normal conditions, (b) system with minor fault conditions, (c) system with moderate fault conditions, and (d) system with major fault conditions, respectively. Using the above fault level determination principle, the system matrices changing bounds for the minor fault and moderate fault can be calculated and shown in the last two rows of Figure 7:37  
**Remark 3**. : _Since the proposed FTC strategy in this paper focused on gradual faults under certain range, and obtained the optimized PFTC control gain by using convex optimization technique, for the system with minor fault conditions (performance status (b)), or for more severe moderate fault conditions (performance status (c)) when the FTC effect can have requested system tracking performance, it is applicable. Unfortunately, for the system with major fault conditions (performance status (d)), the solved control gain would have limited effect; therefore, another AFTC strategy (switching to Mode-2 quickly and smoothly) should be studied in the future work, as the situation is no longer discussed in this paper._  
The previous analysis of pole distribution as a function of the fault severity leads to a conclusion that even gradual faults can affect the pole distribution. The system response performance is discussed in the following section, and the rationale for the necessity to develop PFTC strategy under gradual fault conditions is presented.
## 2 Materials and Methods  
### Fabrication of SiNW Arrays  
SiNWs, Si wafers (p-type, [100], 2-10 \(\Omega\) cm, 500 \(\upmu\)m thickness) were dipped into a HF solution for 1 min to remove the native oxide. To cover the surface with Ag particles via electroless silver plating [31], the Si wafers were immersed in a solution of 4.8 M HF and AgNO\({}_{3}\) for 1 min. The concentration of AgNO\({}_{3}\) in the solution was varied as follows: 0.0025 M, 0.005 M, 0.01 M, 0.015 M,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Length of SiNW** & **Purpose** & \begin{tabular}{c} **Doping Concentration** \\ **of SiNW** \\ \end{tabular} & \begin{tabular}{c} **pn-Junction** \\ **Type** \\ \end{tabular} & **Substrate** \\ \hline This study & 9 \(\upmu\)m & \begin{tabular}{c} Absorption \\ layer \\ \end{tabular} & 1015–1016 cm\({}^{-3}\) & \begin{tabular}{c} Heterojunction \\ (SiNW/a-Si) \\ \end{tabular} & \
\begin{tabular}{c} SOI substrate \\ (SiNW/a-Si) \\ \end{tabular} \\ \hline Ref [29; 30] & \textless{1 }\(\upmu\)m & Anti-reflection & 10\({}^{15}\)–10\({}^{16}\) cm\({}^{-3}\) &
\begin{tabular}{c} Heterojunction \\ (organic/SiNW) \\ \end{tabular} & Si wafer \\ \hline Ref [31; 32] & \textless{4 }\(\upmu\)m & Anti-reflection & \textgreater{10}\({}^{19}\) cm\({}^{-3}\) & Homo-junction & Si wafer \\ \hline \hline \end{tabular}
\end{table}
Table 1: The silicon nanowire (SiNW) characteristic and its structure in solar cell between the reported literatures and this study.  
and 0.02 M. To optimise the lengths of the SiNWs, the Si wafers were chemically etched using 4.8 M HF and 0.15 M H\({}_{2}\)O\({}_{2}\), at room temperature for an etching time of 1-60 min. The etched wafers were subsequently placed in an HNO\({}_{3}\) solution to remove Ag particles. Finally, the oxide layer on the fabricated SiNW array was removed by immersing it in an HF solution. However, the surface pores were observed around the SiNWs. To remove the surface pores, the HNO\({}_{3}\) treatment and HF treatment were conducted three times. The morphology of the prepared SiNW arrays was characterised using field-emission scanning electron microscopy (FE-SEM) (JEOL JSM-7001F). The reflectance of the SiNW on the Si wafer was measured using ultraviolet-visible-near-infrared (UV-Vis-NIR) spectrophotometry (Shimadzu Co., Ltd., SolidSpec-3700). The absorption coefficient of the n-type a-Si layer was measured via spectroscopic ellipsometry ([PERSON], M-2000).
# Compact Fuzzy Systems Based on Boolean Relations
## 6 Application Example of Architecture II: PID Control
### Controller  
The controller is defined from a discrete-time linear controller transformed into a compact fuzzy shape when employing fuzzy sets. Then, the optimization of the controller parameters takes place with the plant and controller. Such optimization happens with the BFGS Quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno) that explains the relevance of the initial search point [65].  
A PID controller in discrete time is described by the transfer function:  
\[C(z)=K_{p}+\frac{K_{i}}{1-z^{-1}}+K_{d}(1-z^{-1}) \tag{65}\]  
performing the respective operations is obtained:  
\[C(z)=\frac{(K_{p}+K_{i}+K_{d})-(K_{p}+2K_{d})z^{-1}+K_{d}z^{-2}}{1-z^{-1}} \tag{66}\]  
in general, it can be written like:  
\[C(z)=\frac{b_{0}-b_{1}z^{-1}+b_{2}z^{-2}}{1-z^{-1}} \tag{67}\]  
The respective equation in discrete time for this controller is  
\[u[n]=b_{0}e[n]-b_{1}e[n-1]+b_{2}e[n-2]+u[n-1] \tag{68}\]  
For the fuzzy controller, the expression is:  
\[u[n]=f_{1}(e[n])+f_{2}(e[n-1])+f_{3}(e[n-2])+f_{4}(u[n-1]) \tag{69}\]  
For implementing the \(f_{i}\) sigmoidal functions are utilized to represent negative \(\mu_{N,i}\) and positive \(\mu_{P,i}\) values, as shown in Figure 23. Thus, Figure 24 shows the fuzzy compact PID controller.  
Figure 23: Membership functions employed.  
Figure 22: Plant response.  
The \(f_{i}\) functions are given in the form:  
\[f_{i}(x_{i})=v_{N,i}\Big{(}1+e^{-\sigma_{N,i}(x_{i}-\gamma_{N,i})}\Big{)}^{-1}+v_{ P,i}\Big{(}1+e^{-\sigma_{P,i}(x_{i}-\gamma_{P,i})}\Big{)}^{-1} \tag{70}\]  
The parameters employed are the following: for the centers \(\gamma_{N,i}=0\), \(\gamma_{P,i}=0\), for the slope \(\sigma_{N,i}=-0.08\), \(\sigma_{P,i}=0.08\); and the virtual actuators \(v_{N,i}=-25\), \(v_{P,i}=25\). In this way, Figure 25 shows the respective functions \(f_{i}\).  
The rules used to implement the structure of Figure 24 can be represented as shown in Boole Table 20. In general, these rules are described as:  
* _If_ \(x_{i}\) is \(\mu_{N,i}\)_Then_ the activation function is \(Y_{N,i}\).
* _If_ \(x_{i}\) is \(\mu_{P,i}\)_Then_ the activation function is \(Y_{P,i}\).  
Figure 24: Compact fuzzy PID controller.  
Figure 25: Input-output relationship for each \(f_{i}\).  
Then, the controller output is calculated as:  
\[u=v_{N,1}Y_{N,1}+v_{P,1}Y_{P,1}+v_{N,2}Y_{N,2}+v_{P,2}Y_{P,2}\\ +v_{N,3}Y_{N,3}+v_{P,3}Y_{P,3}+v_{N,A}Y_{N,A}+v_{P,A}Y_{P,A} \tag{71}\]
# Temperature Comparison of Looped and Vertical Carbon Nanotube Fibers during Field Emission
## 4 Conclusions  
In summary, we have studied field emission and temperature distribution from a looped CNT fiber and a single vertical CNT fiber. For both emitters, the field emission current level of mA can be easily reached with an applied voltage of \(<\)1000 V, demonstrating their excellent field emission properties. For a given applied voltage, the single vertical CNT fiber gave a higher emission current because of its higher field enchantment factor. However, at the same emission current of 3 mA, the maximum temperature of the looped fiber (\(\sim\)300 \({}^{\circ}\)C) was significantly lower than that of the vertical fiber (\(\sim\)600 \({}^{\circ}\)C). The temperature dependent electrical and thermal conductivities were also measured for the CNT fiber. Based on these measured conductivities, the maximum temperature and its location in the fibers were compared with a recent theory on joule heating of a one-dimensional conductor [27]. The measured temperature distribution for both the looped and vertical CNT fibers were well fitted with the theory by adjusting the voltage drop across the emitter. Our study suggests that the novel configuration of a looped CNT fiber can significantly improve the thermal management of field emitters, which could improve the reliability and lifetime of field emitters for high power and high current operations. Future studies will include the effects of different looped fiber geometries (e.g., separation between the two ends and height of the loop) on electric field, current emission, and temperature distribution. Additionally, the effect of increased fiber conductivity on field emission properties will be measured.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON], [PERSON], and [PERSON] performed the experiments. [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data. [PERSON] and [PERSON] contributed to the modeling. [PERSON] wrote the original draft. All authors contributed to the editing of the paper.  
We would like to thank [PERSON], Rice University for providing the materials. This material is based upon work that is supported by the Air Force Office of Scientific Research YIP award No. FA9550-18-1-0061, by the Air Force Office of Scientific Research under award No. FA9550-17 RXCOR428, and by the Air Force Office of Scientific Research under award No. FA9550-18-1-0153.  
The authors declare no conflict of interest.
## 3 Materials and Methods  
### InSAR Observations  
This study benefitted from the abundant SAR images over the epicentral region, providing us the opportunity to explore this event. We employed InSAR technology to process observation data, InSAR technology has received extensive attention in recent years with its ability to obtain high-precision, continuous coverage of ground elevation and surface information, and it is widely used in disaster monitoring and evaluation, topographic surveying, mapping, resource and energy extraction, etc. [19; 20; 21]. Since 1993, [PERSON] et al. used two ERS-1 images of pre and post the Mw 7.3 earthquake in Landers, CA, USA, and successfully obtained theoseismic deformation field using the two-track differential interferometry method [7]. InSAR technology is widely used in seismic deformation monitoring research and to invert the geometric parameters of seismic faults and slip distribution [22; 23; 24; 25; 26; 27]. In this study, we obtained the InSAR observation data of the Sentinel-1A IW mode satellite data provided by the European Space Agency (ESA). The working band is C-band, including the image data of the InSAR ascending orbit from 8 May to 1 June 2021, and the descending orbiting image data from 10 May to 22 May 2021. The time baseline was 24 days and 12 days, respectively, and the time interval for acquiring image data was relatively short, which can ensure that the interferogram has a good correlation. We obtained the line-of-sight (LOS) surface displacements by the two-pass differential InSAR (DInSAR) method using the GMTSAR software [28; 29]. The satellite orbital parameters are listed in Table 1.
## 4 Experiments
### Performance Comparison Experiment According to the Amount of Data  
To investigate the performance difference depending on the amount of data, an experiment with different amounts of data were performed: 100, 200, 400, 800, and 1450 k. The performance fluctuation according to the data size is depicted in Figure 3.  
Figure 3 shows that the performance generally tended to improve with the increase in data. This suggests that the proposed augmentation technique is effective in achieving performance. In addition, because of fine-tuning the data obtained by performing POS-tagging and lemmatizing, considering the semantic and grammatical relationships, respectively, the performance was increased compared to the result of training the randomly and brutely augmented data that randomly extracted incorrect answers. Furthermore, Mixed data with both relationships considered exhibited much higher performance. This implies that the performance varies depending on the manner in which the data is augmented even with the same amount of data.