esa-sceva/satcom-chunk-collection huggingface dataset:
# Volatile Composition of Industrially Fermented Table Olives from Greece
## 1 Introduction
A total of 74 volatile compounds were identified in the headspace of samples, of which 12 were identified as significant volatiles contributing to the discrimination between Manzanilla and Hoijblanca black ripe olives [17]. A few studies have examined the influence of starter cultures on the volatile profile of Greek-style processed green [19; 20; 21; 22] and black [23] table olives from Italian cultivars (Giraraffa, Grossa di Spagna, Nocellara del Belice, Bella di Cerrionola, Nocellara Etene). In addition, several works have been carried out on Spanish-style processed table olives produced from Spanish cultivars (Manzanilla, Gordal, Hoijblanca) studying their volatiles [24; 25; 26; 27; 28; 29]. Only a few of them [24; 30] have focused on the discrimination of cultivars as well as the growing areas based on volatiles. Significant differences in the proportions of volatile compounds between samples from the Gordal cv. and those from Manzanilla and Hoijblanca cv. were detected and statistically visualized by PCA. The locations where the fruits were grown for a given cultivar were not clearly distinguished, indicating that the fruit growing environment had a minor influence on the volatile composition of Spanish-style green table olives [30]. However, extensive data on volatile composition of table olives from Greek cultivars are scarce in the literature. [PERSON] et al. [31] studied Greek-style fermentations of Kalamata and Conservolea cv. black olives and monitored, among other parameters, the evolution of volatiles to identify useful chemical markers for the fermentation process. [PERSON] et al. [32] studied the effect of different inoculation strategies of selected starter cultures on fermentations using the above-mentioned Greek varieties. To our knowledge, no work has been reported so far for the volatile profile of Halkidiki green tables. The main objective of the present work was to assess the volatile profile of (a) Greek-style fermented black olives of Conservolea and Kalamata cultivars, and (b) Spanish-style fermented green olives of cv. Halkidiki, which constitute the most prominent table olives commercially produced in Greece. The samples were fermented on the industrial scale and originated from different geographical regions for each olive cultivar. Using a volatilomics approach, the study aims to identify useful volatile chemical markers for the discrimination of the samples according to cultivar and growing location. This will aid to develop a reliable method for authentication purposes since consumers show a greater preference for products whose varietal or/and geographical origin is declared.
## 4 Conclusions  
The accurate estimation of turbulence severity is important to flight safety of civil aviation aircraft. This paper proposes a new method for aircraft vertical acceleration and EDR estimation. Some innovations are as follows:  
1. Based on the combination of wing and horizontal tail, an unsteady vortex ring method was introduced to compute the aerodynamic force induced by turbulence. In addition, three innovative operations were proposed to improve the calculation accuracy. First, vortex rings were distributed on the mean camber surface. Second, the semicircle division method was adopted to refine the lattices neighboring to wingtip, wing root and leading edge. Third, the divided lattices were as close as possible to the structural boundary of the control surface. Through the comparison of wind tunnel test data and aircraft-modeling data, we showed that with the proposed method, the aerodynamic force of the lifting surface was computed with high accuracy. The pitching moment change of real commercial aircraft was obtained accurately by the wing-tail combination.
2. An integrated computation flow for estimating vertical acceleration and turbulence severity was proposed. The fluctuation of vertical acceleration at arbitrary longitudinal locations of aircraft was computed. By the separation of turbulence-induced and maneuvering-induced aerodynamic force, lift and pitching incremental aerodynamic derivatives were developed. Compared with traditional transfer function-based algorithm, the new acceleration-based EDR algorithm showed higher accuracy and stability. In addition, the adverse influence of aircraft maneuvering on EDR estimation could be effectively eliminated.  
This paper is the first attempt to apply the unsteady panel method on the estimation of vertical acceleration and turbulence severity, and some preliminary conclusions are obtained. In the future, the vortex ring model and related algorithm will be refined to further improve the accuracy of acceleration and EDR estimation.  
Conceptualization, Z.G.; methodology, Z.G. and D.W.; software, Z.G. and D.W.; validation, D.W. and Z.X.; formal analysis, Z.X.; investigation, Z.G.; resources, Z.G.; data curation, Z.G.; writing--original draft preparation, Z.G.; writing--review and editing, Z.G. and Z.X.; visualization, Z.X.; supervision, Z.G.; project administration, Z.G.; funding acquisition, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China, Grant Number [NSFC: U1733122, U1533120].  
The authors declare no conflict of interest.
## 4 Estimation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **External Links** & **Internal Links** \\ \hline  & & average & 0.6782 & 0.7226 \\ \cline{2-4} TE & stdev & 0.1317 & 0.0973 \\ \cline{2-4}  & min & 0.3938 & 0.4883 \\ \cline{2-4}  & max & 0.9805 & 0.9758 \\ \hline \multirow{3}{*}{MTR\_LP} & average & 0.9279 & 0.8776 \\ \cline{2-4}  & stdev & 0.0780 & 0.1402 \\ \cline{2-4}  & min & 0.5269 & 0.0105 \\ \cline{2-4}  & max & 1.0000 & 1.0000 \\ \cline{2-4}  & max & 1.0000 & 1.0000 \\ \hline \multirow{3}{*}{MTR\_QP} & average & 0.8696 & 0.8290 \\ \cline{2-4}  & stdev & 0.0581 & 0.1164 \\ \cline{2-4}  & min & 0.6097 & 0.0907 \\ \cline{2-4}  & max & 1.0000 & 1.0000 \\ \hline \multirow{3}{*}{TE\_LP} & average & 0.6268 & 0.6309 \\ \cline{2-4}  & stdev & 0.1215 & 0.1200 \\ \cline{2-4}  & min & 0.2075 & 0.0093 \\ \cline{2-4}  & max & 0.9388 & 0.8963 \\ \hline \multirow{3}{*}{TE\_QP} & average & 0.5892 & 0.5969 \\ \cline{2-4}  & stdev & 0.1191 & 0.1070 \\ \cline{1-1} \cline{2-4}  & min & 0.2401 & 0.0806 \\ \cline{1-1} \cline{2-4}  & max & 0.9437 & 0.8459 \\ \hline \end{tabular}
\end{table} 
Table 3: SFA estimates of technical efficiencies and meta-technology ratios. A high TE value means that a firms' production is near the group production function. The internal link method has the advantage of improving user convenience because it provides users with a uniform format of news articles, but has the disadvantage of being difficult to differentiate the content delivery environment. For this reason, the TE value of the internal links group is very high, which is consistent with the study result of [PERSON] and [PERSON] [62]. On the other hand, in the case of the external links group, when a user clicks on a news link, the user goes to the website of the media company and receives the news in a unique environment created by the company. Since the news posting environment of media companies is very diverse, the standard deviation of the external links group was 0.1317, which was higher than 0.0973 of the internal links group, which is analyzed as a factor that made the TE of the external links group low. On the other hand, the fact that the MTR of the external links group is larger than the MTR of the internal links group means that the production function of the external links group is closer to the meta-frontier production function than that of the internal links group. Unlike the media companies in the internal links group, the companies in the external links group have to publish the news in their own format, so they have been striving for news placement that increases consumer convenience along with proper advertisement placement, which has increased the MTR of the external links group.
# Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
## 2 Model Construction
### Uncertainty Risk Measurement
Moreover, there is a contradiction between the fairness principle of emergency scheduling and the dynamic characteristics of disaster evolution. Therefore, it is difficult to estimate the relief needs and material requirements at each demand point, since the demand point changes dynamically. Fortunately, in the mobile emergency service system, we can easily obtain the location of the demand point by mobile communication. Thus, Formula (2) is often adopted to estimate requirements for different emergency supplies. In the formula, \(\overline{d_{j}^{k,u}}\) represents the nominal value of the demand for type \(k\) emergency materials or rescue services at the demand point \(j\). \(d_{j}^{k,u}\) represents the maximum fluctuation away from the nominal value. In the actual process of mobile emergency vehicle scheduling, if \(d_{j}^{k,u}=\overline{d_{j}^{k,u}}\), this indicates that the model does not predict enough change in demand point, and the vulnerability of the model is amplified. On the contrary, if \(d_{j}^{k,u}=\overline{d_{j}^{k,u}}+d_{j}^{k,u}\), this means the scheduling model becomes a fully robust model. Of course, the range of changes in demand for various materials is unlikely to approach the maximum, and in this case the decision is meaningless. Therefore, as shown in Formula (3), the relatively robust optimization method is adopted. The control parameter \(\gamma_{j}^{k,u}\) is introduced to control the degree of uncertainty of emergency material demand and rescue service demand in the disturbance interval. When \(\gamma_{j}^{k,u}=0\), the \(d_{j}^{k,u}=\overline{d_{j}^{k,u}}\) problem is transformed into a non-robustness problem. In other cases, \(\gamma_{j}^{k,u}\) within the interval [0, 1] controls the range of demand disturbances. However, due to the constant change in demand, it is hard to obtain an accurate value of the parameter \(\gamma_{j}^{k,u}\) in the short term. Therefore, a reasonable estimation of \(\gamma_{j}^{k,u}\) can be performed according to historical emergency event data with the same magnitude. If no historical data are available, the parameter \(\gamma_{j}^{k,u}\) can be estimated by \((\text{Max}d_{j}^{k,u}-\text{Min}d_{j}^{k,u})/\text{Max}d_{j}^{k,u}\). Here, \(d_{j}^{k,u}\) comes from the dataset that can currently be collected. \[d_{j}^{k,u}\in\left[\overline{d_{j}^{k,u}},\overline{d_{j}^{k,u}}+d_{j}^{k,u} \right],j\in J,k\in K,u\in U \tag{2}\]  
\[\gamma_{j}^{k,u}=\frac{\left|d_{j}^{k,u}-\overline{d_{j}^{k,u}}\right|}{ \overline{d_{j}^{k,u}}},j\in J,k\in K,u\in U \tag{3}\]
# Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels
## 5 Numerical Results and Discussions  
In this section, we will investigate the correlation properties of the propagation links in the proposed V2V channel model for tunnel scenarios. The space CCFs, temporal ACFs, frequency CCFs, as well as the Doppler PSDs are studied with respect to the different model parameters. Here, we consider the tunnels with a length of \(L=100\) m and a width \(10\) m. With reference to Figures 2-4, the length of the long axis and the short axis of the elliptical tunnel are \(10\) m and \(8\) m, respectively. In the Figure 3, the side length of the rectangular tunnel is \(10\) m. The locations of the V2V transmitter and the receiver are defined by the Cartesian coordinates \((x_{T},y_{T},z_{T})=(20,2,1)\) and \((x_{R},y_{R},z_{R})=(40,20,1)\). Other data parameters are set as follows \(\varphi_{v}^{T}=\varphi_{v}^{R}=0\), \(\gamma_{T}=\gamma_{R}=45^{\circ}\), \(\phi_{T}=\phi_{R}=45^{\circ}\), and \(f_{Tmax}=f_{Rmax}=91\) Hz.
# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
## 3 Vehicle Re-Identification With RANDnet
### Nuance-Disparity Masks
Conversely, enhancing the IIF in ambiguous classes facilitates the model's focus on regions with sub-discriminative features, thereby augmenting the robustness of feature representation. Stemming from this concept, we introduce the disparity and nuance masks, designed to adaptively discern and  
Figure 3: N&D masks generation process. We assume there are 4 IDs in the training set. A nuance or disparity mask is obtained by summing 3 IIFs belonging to other categories. The activation score in attention map reflects the importance of corresponding spatial units for recognition. accentuate the IIF at various levels within input images, promoting more nuanced and differentiated feature learning. The detailed procedure for obtaining D&N masks is outlined in Algorithm 1, wherein the parameters \(\theta_{\text{disp}}\) and \(\theta_{\text{nuc}}\) dictate the involvement of hard or easy categories in defining sub-discriminative or background regions, respectively. In addition to these parameters, another parameter set is introduced to modulate the extent of area covered by the mask within the image. The area of discriminative regions can be governed by a quantile threshold. Consequently, \(\sigma_{\text{disp}}\) and \(\sigma_{\text{nuc}}\) are conceptualized to denote the proportion of discriminative regions relative to the entire image. An in-depth analysis of these parameters is provided in Section 4.3. ```
1:Input:\(F\in\mathbb{R}^{BS\times C\times H\times W}\), ID count thresholds for Disparity & Nuance masks \(\theta_{\text{disp}}\) and \(\theta_{\text{nuc}}\), area thresholds \(\sigma_{\text{disp}}\) and \(\sigma_{\text{nuc}}\)
2:Output: Nuance Mask \(M_{\text{nuc}}\), Disparity Mask \(M_{\text{disp}}\)
3:\(f=\text{BN}(GAP(F))\)
4:\(S=softmax(f)\)
5:\(sort(S)\)
6:\(M_{\text{disp}}=\text{zeros}(1,h,w)\), \(M_{\text{nuc}}=\text{zeros}(1,h,w)\)
7:for\(b=1\) to \(BS\)do:
8:\(m_{\text{disp}}=\text{zeros}(1,h,w)\), \(m_{\text{nuc}}=\text{zeros}(1,h,w)\)
9:for\(s=S_{0}\) to \(s=S_{\theta_{\text{nuc}}}\)do:
10:\(III_{i,j}^{s}=\sum_{k=1}^{c}W_{s,k}\cdot F_{i,j,k}\),
11:\(m_{\text{nuc}}=m_{\text{nuc}}+III_{i,j}^{s}\)
12:endfor
13:\(m_{\text{nuc}}=\text{reshape}(m_{\text{nuc}},1,1\times h\times w)\)
14:\(\text{Idx}=top(n,h\times w\times\sigma_{\text{nuc}})\)
15:\(m_{\text{nuc}}^{\text{Idx}}=0,m_{\text{nuc}}^{-\text{Idx}}=1\)
16:for\(s=S_{\theta_{\text{disp}}}\) to \(s=S_{-1}\)do:
17:\(III_{i,j}^{s}=\sum_{k=1}^{c}W_{s,k}\cdot F_{i,j,k}\),
18:\(m_{\text{disp}}=m_{\text{disp}}+III_{i,j}^{s}\)
19:endfor
20:\(m_{\text{disp}}=\text{reshape}(m_{\text{disp}},1,1\times h\times w)\)
21:\(\text{Idx}=top(n,h\times w\times\sigma_{\text{disp}})\)
22:\(m_{\text{disp}}^{\text{Idx}}=1,m_{\text{disp}}^{-\text{Idx}}=0\)
23:\(\text{concat}(M_{\text{disp}},m_{\text{disp}})\) along \(b\)-axis
24:\(\text{concat}(M_{\text{nuc}},m_{\text{nuc}})\) along \(b\)-axis
25:endfor
```  
**Algorithm 1** Nuance-Disparity Mask
# Fatigue Life Prediction of Radial Tire Bead Using a Maximum Strain Energy Density Range Method
## 3 Experiment
### Fatigue Test Result  
The specific calculation method of \(SED\) can refer to ASTM-D4482-07 [12]. According to fatigue testing data, the strain-\(N_{f}\) curves of different bead compounds is obtained. \(\Delta SED\)-\(N_{f}\) curves were converted from strain-\(N_{f}\) curves, as shown in Figures 5-7. \(N_{f}\) is the crack propagation life, and \(\Delta SED\) is the strain energy density range.  
\begin{table}
\begin{tabular}{c c c} \hline
**Compounds** & **Maximum Strain** & **Minimum Strain** \\ \hline \hline Carass & 1.60/1.04/0.80/0.50 & 0 \\ Apex & 1.44/0.96/0.76/0.50 & 0 \\ Abrasion & 1.50/1.00/0.74/0.50 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Strain of rubber components for tests.  
Figure 4: Waveform type for tests.  
Figure 5: \(\Delta SED\)–\(N_{f}\) curve of carcass.  
Equations (2) to (4) show fatigue life prediction equations for three rubber compounds, the order is carcass, apex, and abrasion, respectively.  
\[N_{f}=49220(\Delta SED)^{-2.22} \tag{2}\]  
\[N_{f}=42788(\Delta SED)^{-3.04} \tag{3}\]  
\[N_{f}=140691(\Delta SED)^{-3.4} \tag{4}\]  
It shall be noted that Equations (2) to (4) are calculated based on the unit MJ/m\({}^{3}\). Therefore, in the subsequent life prediction, the unit needs to be considered.
# Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation
Several research groups have been working with Gaussian Mixture Model (GMM) to model audio signals [15]. They claim that the source noise is not only originating from a unique source (i.e., electromagnetic interference). For instance, the audio message can be corrupted by vehicle noise or unknown speakers. Each undesirable source (noise source) is modeled by a single class noise or an individual Gaussian from the GMM. Thus, besides the \(\alpha\)-stable, we also consider the Gaussian Mixture Model (GMM) as a candidate noise model. The analysis and investigation of impulsive noise in acoustic channels are still challenging, allowing some rooms for research opportunities. First, several commercial products, using traditional signal processing solutions, are emerging and subject to severe acoustic scenarios such as smart speakers [16; 17], robotics [18] and video conference systems [19]. Second, researches have shown methods for non-Gaussian noise without justifying the real occurrence of these cases. Moreover, few works have exposed a qualitative analysis among the non-Gaussian models that include experimental validation, and the identification of real-life scenarios for each model. Finally, little effort has been presented to assess different acoustic scenarios over the same unified quantitative analyses. This paper offers an evaluation of some acoustic scenarios using an experimental setup. First, two indoor environments are examined with less impulsiveness. Then, one outdoor scenario with severe impulsiveness is analyzed. We propose to analyze the fitting for the Gaussian, GMM, and \(\alpha\)-stable models for all scenarios. We present graphical and numerical evaluation, addressing their properties, disadvantages, and advantages. This paper is organized as follows. Section 2 presents the specificities of parameter estimation for non-Gaussian models related to impulsive noise characterization. We then introduce our experimental setup on Section 3, where measurements situations are described. The evaluation scenarios are defined on Section 4, along with the presentation and evaluation of the results. Finally, conclusions are summarized on Section 5.
## 2 Materials and Methods
### Labeling of Poly(dimethylsiloxane-co-(3-aminopropyl)methylsiloxane) with 1-Pyreneacetic Acid  
The synthesis of PDMS labeled with 5 mol % of pyrenyl groups is described below in detail. The syntheses to obtain the other levels of derivatization were done in a completely analogous fashion. A 1:1 molar ratio of 1-pyreneacetic acid (78 mg, 0.30 mmol) and CDI (50 mg, 0.30 mmol) was added to a 50 mL round bottom flask along with 20 mL of dichloromethane (DCM) and 1 mL of DMSO. The contents of the flask were stirred for 2 h at room temperature and the siloxane copolymer (AMS-1203; 500 mg) was added. Stirring continued for 24 h and then potassium bicarbonate (1.0 g-10 mmol) was added to the flask with the subsequent dropwise addition of acetic anhydride (0.60 mL; 0.60 mmol). After stirring for 2 more h, the reaction mixture was poured into 20 mL water, the layers were separated, and the organic portion was collected. The aqueous phase was washed twice with 20 mL dichloromethane and then the organic phases were combined and reduced with a rotary evaporator set at 40 \({}^{\circ}\)C to a residue. A yellow-brown wax was collected and washed twice with methanol to afford 100 mg (19%) of Py-PDMS product. The general chemical structure of the Py-PDMS samples is shown in Figure 1. The \({}^{1}\)H NMR spectra of the Py-PDMS samples are presented in Figure S3.
## 3 Results and Discussion
### Volatile Compound Composition
#### 3.1.5 Volatile Phenols  
Volatile phenols are known to be one of the most important volatiles for incorporating aging flavor into the overall aroma of wine during the aging process [10]. Both clone wines before the aging process exhibited the same volatile phenol composition (15 volatile phenols), and no significant differences were observed with respect to their concentrations between the two clone wines (Table 2). As illustrated in Figure 1e, the aging process resulted in a significant accumulation of most volatile phenols in these wine samples, and after 12 months of aging, 12 volatile phenols displayed significant concentration differences in these two clone wines (Table 2). Clone 169 wines after the aging process contained significantly (\(p\leq 0.05\)) higher concentrations of guaiacol, phenol, 4-propylguaiacol, 4-vinylphenol, 4-vinylguaiacol, 4-ethylphenol, and 4-ethylguaiacol compared to the clone 191 wines. The variation of volatile phenols between these wine samples after the aging process may be attributed to the different levels of hydroxycinnamic acids precursors present in the grapes [52]. 4-Vinylguaiacol and 4-vinylphenol have been reported to be metabolized from fertile acid and \(p\)-coumar acid, respectively, under the activity of microorganisms, and then reduced to ethyl derivatives (4-ethylguaiacol and 4-ethylphenol) by enzyme vinylphenol reductase [31,52]. On the other hand, it has been reported that the use of oak barrels could enhance the levels of volatile phenols in wine, since they can be extracted from the oak barrel into the wine during aging [34,53]. Oak barrel type, oak toasting level, and wine oak barrel aging period essentially determine the concentrations of volatile phenols in wine [32,53,54,55]. In the present study, it was observed that the wine samples aged in the Bordeaux oak barrel contained significantly (\(p\leq 0.05\)) higher levels of guaiacol, 4-methylguaiacol, 4-ethylguaiacol, and \(m\)-cresol, whereas the Wolin oak barrel resulted in wines with significantly higher levels of 4-ethylphenol, 4-vinylguaiacol, _trans_-isoeugenol, and 4-vinylphenol (Table 2). These results indicated that the Bordeaux oak barrel might be abundant in guaiacol, 4-methylguaiacol, 4-ethylguaiacol, and \(m\)-cresol, whereas 4-ethylphenol, 4-vinylguaiacol, _trans_-isoeugenol, and 4-vinylphenol might be the major volatile phenols in the Wolin oak barrel.
## 3 Results and Discussion
### Push-Pull Circuit Efficiency Test  
To test whether the efficiency of the push-pull circuit meets the design requirements, the efficiency at eight power points was measured. With the battery discharging and the load connected to the post-stage inverter, the voltage and current at the busbar end and the battery end were measured. Figure 17 shows the measured efficiency; the efficiency of the push-pull discharge circuit generally increases with an increase in the power. The highest efficiency point appears at the maximum load, which is approximately 93.43%, indicating that the system satisfies the design requirements.  
Figure 16: Partial enlargement of the bus voltage and bus current waveforms when switching loads.  
Figure 17: Efficiency–load power curve.  
Figure 15: Partial enlargement of the bus voltage and battery current waveforms when switching load.