esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The effects of the level of berry thinning (30% and 50% berry removal) on the quality and sensory properties of Shine Muscat grapes were investigated. As berry thinning increased, the total soluble solids content increased and tittatable acidity decreased. Berry thinning increased berry size and cluster weight but caused no change in individual berry weight. Phenolic concentrations as measured by total phenolic, proanthocyanidin, and polymeric tannin concentrations tended to increase with an increase in berry thinning. Gas chromatographic analysis indicated that C6-compounds were the significant constituents of volatile alcohols and aldehydes; linalool was the most abundant monoterpene. Odor activity analysis indicated that (E)-2-hexen-1-ol, (E)-2-hexenal, (E)-3-hexenal, (E)-\(\beta\)-damascenone, linalool, and (E)-linalool oxide were active odorants. Berry thinning increased the accumulation of linalool contributing to high sensory flavor scores in thinned berries. Furthermore, its oxidized derivative-linalool oxide-contributed to enhancing the Muscat flavor. In conclusion, berry thinning induced compositional changes in Shine Muscat grape berries by accelerating the ripening rate, contribution to improved sensory properties.  
Shine Muscat grape; berry thinning; sensory flavor attribute; linalool; linalool oxide Article
## 3 Fuzzy-Logic Topology Control (FTC)
### FTC Protocol  
According to the design of FTC in Section 3.1, corresponding FTC protocol is presented in this section, as shown in FTC protocol running for a generic node. Each node broadcasts the HELLO message at the maximum communication range \(r_{max}\), in order to collect neighbors' information. The communication range is modified accordingly based on the fuzzy-logic controller.  
On the one hand, in this paper, we only consider undirected links (that is, there is a link between two nodes if and only if they are both in each other's communication range), because in practice many routing protocols assume that the link between two nodes are undirected. On the other hand, FTC is a localized control algorithm running at each node independently. As a result, the node degree changes over time. For instance, node \(u\) is the neighbor of \(v\) at this round of communication range updating, but it is likely that in next round \(u\) is not the neighbor of \(v\) due to the communication range of \(u\) and/or \(v\) being altered. We will show in Section 4.2 that the system goes to steady state by appropriately defining the value of \"rounds\" in the FTC protocol.
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 5 Some Applications
### Other Applications I: Magnetic Media  
Magnetic recording systems have been very important for the wide diffusion of music, videos, and information at low cost. Although a change of paradigm is currently occurring with the growing uses of solid-state devices which offer better performance, the interest in magnetic devices is still maintained due to the lower costs per storage capacity unit. In current hard disk drive (HDD) systems, a rigid disk is rotated at a speed of up to 10,000 revolutions per minute (rpm) [115]. Generally, the rigid disk is composed from an Al-Mg alloy or glass substrate, undercoat sheet, a magnetic multilayer, a carbon coverage, and a very fine lubricant layer. In particular, the carbon overcoat is necessary to prevent wear and corrosion problems, even if this activity is mainly carried out by the thin lubricant that must reduce the contact between the read/write magnetic head and the media surface. Perfluoropolyether (PFPEs) lubricants have so far been the best lubricating system for magnetic media, even if [PERSON] et al. have, over time, developed synthetic methodologies to prepare ILs, possessing both a perfluoroalkyl group and an ester, amide, and carboxylic acid ammonium salt with a long chain (Table 7) [116].  
The authors justified the increased performance of lubricants by these class of ILs with the uniform coverage of the rubbing surface by the ammonium salt portion and a higher dispersive interaction of their hydrophobic group, combining characteristics typical for high lubrication due to secondary interactions of various kinds between contacting surfaces and polar heads and apolar chains of the cation and anion portions.  
Figure 17: Chemical structure of quaternary phosphonium-based ILs used as titanium lubricant.
## 3 Implementation Method of Proposed Macrodiversity System for Maritime Wireless Sensor Networks
### Cascaded Combining Scheme
Finally, the current coded frame ID in receiving stage of packages is inserted into the header of new information frame to facilitate the identification of the second-level diversity combining. The second-level diversity combining is implemented on the convergence center, which completes the convergence and combining of multiple frames from different local convergence modules. The second-level diversity combining includes convergence stage and combining stage. In convergence stage, the processor is first to set a fixed internet protocol (IP) address and port to inform the local convergence node of the destination address firstly. Then, the processor creates and binds a local network socket to receive and buffer information frame. When the processor receives the information frame, a coded frame ID detection is necessary. The coded frame ID detection is mainly used to determine whether the current information frame transmission is completed by compared to the previous coded frame ID. When the coded frame ID changes, the package with different coded frame ID is preserved and packages in the cache are combined by EGC strategy. In combining stage, the symbols of corresponding positions in the received information frames are added and a weighted sequence is obtained firstly. Then, the weighted sequence is hard-judged to obtain the hard decision binary information sequence. Finally, the information sequence is put into the turbo decoder to recover the original frame and a CRC check is performed to determine the accuracy of the package.
## 2 Materials and Methods
### SIF Spectra Retrieval  
#### 2.2.1 Band Selection  
It is challenging to decouple the SIF contribution from the total upwelling radiance because SIF emission is quite weak compared with the reflected radiance. Although we aimed to retrieve the full-spectrum SIF, only the solar Fraunhofer lines or telluric atmospheric absorption bands could be used as, here, the emitted SIF is comparable to the reflected radiance.  
The SIF spectra cover the spectral region from 650 to 850 nm. Figure 1 shows the total upwelling radiance and SIF spectrum at the TOC for the configuration of the QE Pro spectrometer that was used in the field experiments. There are four absorption bands in this spectral region that have the potential for SIF retrieval, including the H\(\alpha\) line centered at 656.5 nm, the O\({}_{2}\)-B band at 687.1 nm, the water vapor band at 718.8 nm and the O\({}_{2}\)-A band at 760.7 nm. However, as the water vapor absorption band is quite unstable when used with field-measured data [31], it was excluded in this study. Three absorption bands (H\(\alpha\), O\({}_{2}\)-B and O\({}_{2}\)-A) were, therefore, employed.  
Figure 1: Total upwelling radiance and Solar-Induced chlorophyll Fluorescence (SIF) spectrum at the Top of Canopy (TOC) for the spectral range 650–800 nm with a spectral resolution (SR) of 0.3 nm and Spectral Sampling Interval (SSI) of 0.15 nm.
# Retinex-Based Relighting for Night Photography
## 3 Proposed Method
### Diffusing Lighting Source Points
#### 3.3.1 Omnidirectional Diffusion  
First, we explain omnidirectional diffusion. A primary method of omnidirectional diffusion uses the PSF of Gaussian filtering (GF). This kernel spreads light evenly in all directions. However, PSF of GF is not a sufficient method because it produces light outside of the target object. Therefore, we additionally use the PSF of edge-preserving filters: JBF, domain transform filtering (DTF) [57], and adaptive manifolds filtering (AMF) [58]. The PSF of edge-preserving filters depends not only on a spatial kernel, such as GF, but also on a range kernel. The kernel of joint edge-preserving filtering using the input image as a guide image can suppress light diffusion at the object boundary of the reflectance guide.  
For the range kernel, JBF uses a Gaussian distribution with the Euclidean distance defined in Equation (6). The DTF range kernel uses the geodesic distance of image edges instead of the Euclidean distance, and the AMF range kernel uses the mixed Euclidean-geodetic distance. The core ideas are defined as follows:  
\[w^{\text{DTF}}_{r}(\mathbf{f}_{\mathbf{p}},\mathbf{f}_{\mathbf{q}})=\exp(-\frac{\|\mathbf{f}_{\mathbf{q }}-\mathbf{f}_{\mathbf{p}}\|_{\text{geo}}^{2}}{2\sigma_{r}^{2}}),\quad w^{\text{AMF}} _{r}(\mathbf{f}_{\mathbf{p}},\mathbf{f}_{\mathbf{q}})=\exp(-\frac{\|\mathbf{f}_{\mathbf{q}}-\mathbf{f}_{ \mathbf{p}}\|_{\text{mix}}^{2}}{2\sigma_{r}^{2}}), \tag{8}\]  
where \(\|\cdot\|_{\text{geo}}\) indicates geodesic distance and \(\|\cdot\|_{\text{mix}}\) indicates the mixed Euclidean-geodetic distance. The geodesic distance considers changes in the value along the path. The definition is as follows:  
\[\|\mathbf{f}_{\mathbf{p}}-\mathbf{f}_{\mathbf{q}}\|_{\text{geo}}=\int_{\mathbf{p}}^{\mathbf{q}}|\mathbf{f }_{o}^{\prime}|do, \tag{9}\]  
where \(\mathbf{f}_{o}^{\prime}\) is a differential value along the path from \(\mathbf{p}\) to \(\mathbf{q}\) and \(|\cdot|\) is the absolute function. The mixed Euclidean-geodetic distance is the blended value between Euclidean and geodetic distances:  
\[\|\mathbf{f}_{\mathbf{p}}-\mathbf{f}_{\mathbf{q}}\|_{\text{mix}}=\text{blend}(\|\mathbf{f}_{\mathbf{p }}-\mathbf{f}_{\mathbf{q}}\|_{\text{geo}},\|\mathbf{f}_{\mathbf{p}}-\mathbf{f}_{\mathbf{q}}\|) \tag{10}\]  
Figure 5: Examples of blue noise sampling from dithering samples.  
where blend is a blending function. The actual response is different from these functions because of the approximation processes: separable approximation and range value approximation. See the papers [57; 58] for the actual definitions.  
Figure 6: Flowchart of sampling light source from dithering points.
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Study Area and Reference Data  
The study area is the North-Eastern part of the Autonomous Province of Trento (PAT) in Italy (see Figure 1). Characterised by predominantly mountainous terrain, the landscape is dominated by tree species such as Norway spruce (_Picea abies_ (L.) [PERSON]), silver fir (_Abies alba_ Mill.), and European beech (_Fagus sylvatica_ L.). In late October 2018, the entire Northeastern region of Italy, including the study area, was struck by the devastating Vaia storm, one of the most severe storm events witnessed in Italy in recent decades [20]. This storm had devastating effects on the forests of Northern Italy and of the Autonomous Province of Trento in particular [21].  
Reference data used for windthrow detection were established in two distinct sites of about 27 km\({}^{2}\) each (Figure 1, Table 1). These sites were previously examined in a separate study, with further details available in [18] (areas A and B of the study in [18]). The reference windthrow maps were established through manual photointerpretation using VHR imagery (i.e., SPOT5 images) [18]. Given the absence of relevant differences between the two sites, in this analysis they were treated as a single site.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **A** & **B** \\ \hline \multirow{4}{*}{Area (km\({}^{2}\))} & Non-forest & 6.1 & 3.9 \\ \cline{2-4}  & Intact forest & 16.3 & 20.9 \\ \cline{2-4}  & Windthrows & 5.0 & 3.1 \\ \cline{2-4}  & Total & 27.4 & 27.9 \\ \hline \multirow{4}{*}{Windthrows patches} & Number & 456 & 774 \\ \cline{2-4}  & Area range (m\({}^{2}\)) & 36–1,746,189 & 9–389,781 \\ \cline{2-4}  & Median area (m\({}^{2}\)) & 472.5 & 288 \\ \hline \multirow{2}{*}{Altitude a.s.l. (m)} & Range & 1095–2318 & 989–2291 \\ \cline{2-4}  & Median & 1513 & 1511 \\ \hline \multirow{2}{*}{Slope (\({}^{\circ}\))} & Range & 0.003–77.7 & 0.009–79.2 \\ \cline{2-4}  & Median & 29.7 & 24.9 \\ \hline \multirow{2}{*}{Aspect (\({}^{\circ}\))} & Range & 0–360 & 0–360 \\ \cline{2-4}  & Median & 154.8 & 233.8 \\ \hline \multirow{2}{*}{Forest Types (\%)} & Deciduous & 12.2 & 5.4 \\ \cline{2-4}  & Evergreen & 87.8 & 94.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the two study areas.  
Figure 1: The location of PAT in Italy and Europe (inset (**a**)), the location of the two reference sites inside the territory of PAT and the DTM of PAT (inset (**b**)), and the DTM of the two reference areas A and B (inset (**c**)).
## 3 Distributed Cognition-Based Data Analysis and Results
### Sensitivity Analysis  
The sensitivity analysis chart shows the changes in the coefficients of each hypothetical path under different prior Settings. Analysis shows that the coefficient estimates of core paths such as LF\(\rightarrow\)PEU, PF\(\rightarrow\)LF, PEU\(\rightarrow\)PU, and PEU\(\rightarrow\)SA remain relatively stable under different prior types (no information, weak information, medium information, and strong information), indicating that the estimation results of these relationships are robust to prior selection. The coefficients of paths such as PEU\(\rightarrow\)WTCTU, PE\(\rightarrow\)WTCTU, and SA\(\rightarrow\)WTCTU  
Figure 2: Autocorrelation Plots for Prior Distributions (n = 500).  
fluctuate significantly, especially between the prior without information and the prior with strong information, indicating that their estimation results may be more dependent on the prior Settings. The coefficient variation ranges of the paths EF\(\rightarrow\)PF and LF\(\rightarrow\)WTCTU are moderate. Overall, the robustness of the core path verifies the reliability of the main structure of the model, except for the strong prior information model. Figure 3.
## 5 Results and Discussion
### Reservoir Diversity and Error Performance
On the LZ dataset: From Figures 9c,d, 10c,d and Table 1, the training error, testing error, and extreme generalization performance of pruned DeepESNs were all significantly improved compared to unpruned DeepESN, and the diversity of different similarity estimation criteria were not prominent. The noncriterion-based IPMA method performed as well as criterion-based NS-IPMA methods. In summary, all these experimental results showed that the NS-IPMA method is a successful approach to improving the generalization performance of DeepESN, which is specific, in almost all of our designed experiments, in that the DeepESN pruned by NS-IPMA has better generalization performance than the standard unpruned DeepESN. Figure 10: Testing NRMSE comparison of unpruned DeepESN and DeepESN pruned by IPMA, ED-IPMA, PC-IPMA, SC-IPMA, KC-IPMA. (**a**): Initial 4 layer reservoirs with 100 neurons in each reservoir on \(MG\) dataset; (**b**): Initial 8 layer reservoirs with 50 neurons in each reservoir on \(MG\) dataset; (**c**): Initial 4 layer reservoirs with 100 neurons in each reservoir on \(LZ\) dataset; (**d**): Initial 8 layer reservoirs with 50 neurons in each reservoir on \(LZ\) dataset.
## 3 Reduced-Complexity ICFO and CIDS Detection Method
### Performance Analysis
 \tag{25}\]  
where  
\[\binom{6U+2}{u_{1},u_{2},\cdots,u_{M_{\text{c}}+1}}=\binom{u_{1}}{u_{1}}\binom{ u_{1}+u_{2}}{u_{2}}\binom{u_{1}+u_{2}+\cdots+u_{M_{\text{c}}+1}}{u_{M_{\text{c}}+1}}. \tag{26}\]By averaging \(P_{f}(\mathcal{G})\) over \(\mathcal{G}\), where the probability density function of \(\mathcal{G}\) is represented as \(f(\mathcal{G})=(1/\sigma_{G}^{2})e^{-\mathcal{G}/\sigma_{G}^{2}}\), one arrives at  
\[ \begin{split} P_{f}&\geq 1-\sum_{u_{1}+\cdots+u_{M_{ \mathrm{c}}+1}=6U+2}\prod_{i=1}^{M_{\mathrm{c}}}(-a_{i})^{u_{i}}\binom{6U+2}{ u_{1},u_{2},\cdots,u_{M_{\mathrm{c}}+1}}\\ &\times\left(1+\sum_{i=1}^{M_{\mathrm{c}}}b_{i}u_{i}\right)^{-1/ 2}F(u_{1},u_{2},\cdots,u_{M_{\mathrm{c}}}),\end{split}  \tag{27}\]  
where  
\[F(u_{1},u_{2},\cdots,u_{M_{\mathrm{c}}})=\int_{0}^{\infty}\frac{1}{\sigma_{G}^ {2}}e^{-\frac{\lambda^{2}}{2}\left\{1-\left(1+\sum_{i=1}^{M_{\mathrm{c}}}b_{i} u_{i}\right)^{-1}\right\}}e^{-\frac{\mathcal{G}}{\sigma_{G}^{2}}}d\mathcal{G}. \tag{28}\]  
When the SNR is sufficiently large, the second squared term in the denominator on the RHS of (22) can be considered insignificant. Consequently, by neglecting this term, we can approximate \(\lambda\) as  
\[\lambda\approx\sqrt{\frac{M_{\mathrm{v}}(M_{\mathrm{s}}-1)\gamma_{2}\gamma_{i }}{\gamma_{2}+\gamma_{i}}}. \tag{29}\]  
By substituting (29) into (28), and performing some mathematical simplifications, we can get  
\[F(u_{1},u_{2},\cdots,u_{M_{\mathrm{c}}})\approx\frac{2(\tilde{\gamma}_{z}+ \tilde{\gamma}_{i})}{(M_{\mathrm{s}}-1)\tilde{\gamma}_{z}\tilde{\gamma}_{i} \left\{1-\left(1+\sum_{i=1}^{M_{\mathrm{c}}}b_{i}u_{i}\right)^{-1}\right\}+2( \tilde{\gamma}_{z}+\tilde{\gamma}_{i})}, \tag{30}\]  
where \(\tilde{\gamma}_{z}=\mathbb{E}\{\mathcal{G}\}E_{X}/\sigma_{Z}^{2}\) denotes the average SNR and \(\tilde{\gamma}_{i}=\mathbb{E}\{\mathcal{G}\}E_{X}/\sigma_{I}^{2}\) denotes the average SIR. Therefore, the unconditional probability of detection failure is computed as  
\[ \begin{split} P_{f}&\approx 1-\sum_{u_{1}+\cdots+u_{M_{ \mathrm{c}}+1}=6U+2}\prod_{i=1}^{M_{\mathrm{c}}}(-a_{i})^{u_{i}}\binom{6U+2}{ u_{1},u_{2},\cdots,u_{M_{\mathrm{c}}+1}}\Bigg{(}1+\sum_{i=1}^{M_{\mathrm{c}}}b_{i}u_{i }\Bigg{)}^{-1/2}\\ &\times\frac{2(\tilde{\gamma}_{z}+\tilde{\gamma}_{i})}{(M_{ \mathrm{s}}-1)\tilde{\gamma}_{z}\tilde{\gamma}_{i}\left\{1-\left(1+\sum_{i=1}^ {M_{\mathrm{c}}}b_{i}u_{i}\right)^{-1}\right\}+2(\tilde{\gamma}_{z}+\tilde{ \gamma}_{i})}.\end{split}  \tag{31}\]  
Please note that the detection probability denoted by \(P_{d}=\mathrm{Prob}\{(\hat{v},\hat{w})=(v,w)\}\) is the probability that the RV \(y\) under hypothesis \(H_{1}\) exceeds all other \(3(2U+1)-1\) RVs under hypothesis \(H_{0}\), which is the complementary probability of \(P_{f}\) and \(P_{d}\) is expressed in a upper-bound form due to the use of (23). As a global performance metric, we will assess the performance of the presented NR-PSS detection methods equivalently, using \(P_{d}=1-P_{f}\), in the simulation section.
# Adaptive Semi-Active Suspension and Cruise Control through LPV Technique
## 4 System and Decision Integration Method
In the latter case scheduling variable is closer to '1', where controller works comfort-oriented. In the former case, scheduling variable is closer the '0', where controller works safety-oriented. For instance, let the road type be the sine-sweep irregularity and the vehicle velocity 60 km/h. Here, \(\zeta_{sine-sweep,60}^{td}=27.2\) and \(\zeta_{sine-sweep,60}^{va}=84.6\), thus \(\zeta_{ij}^{va}>\zeta_{ij}^{td}\) case is fulfilled and first equation is used in (20). The calculated scheduling variable in this example is \(\rho=0.75\), thus the behavior of the suspension is chosen to be more comfort oriented than stability oriented. The selected scheduling variables for the simulation are shown in Table 3, while a detailed description of irregularities and simulation route will be explained in Section 5. \begin{table}
\begin{tabular}{c c c c} \hline
**Road Type** & **Velocity (km/h)** & **Vertical Acceleration(\(\mathcal{P}^{va}\)) [\(\zeta^{va}\)]** & **Tire Deformation(\(\mathcal{P}^{td}\)) [\(\zeta^{td}\)]** \\ \hline \multirow{4}{*}{10 cm bump} & 20 & 0.116 [8.7] & 0.0006 [5.4] \\  & 40 & 0.221 [16.6] & 0.0013 [11.8] \\  & 60 & 0.428 [32.3] & 0.0024 [21.8] \\  & 80 & 0.537 [40.5] & 0.0039 [35.4] \\ \hline \multirow{4}{*}{Multiple bumps} & 20 & 0.370 [27.9] & 0.0110 [100] \\  & 40 & 0.431 [32.5] & 0.0017 [15.4] \\  & 60 & 0.466 [35.2] & 0.0029 [26.3] \\  & 80 & 0.444 [33.5] & 0.0041 [37.2] \\ \hline \multirow{4}{*}{Sine-sweep} & 20 & 0.431 [32.5] & 0.007 [63.6] \\  & 40 & 0.751 [56.6] & 0.002 [21.8] \\ \cline{1-1}  & 60 & 1.122 [84.6] & 0.003 [27.2] \\ \cline{1-1}  & 80 & 1.326 [100] & 0.011 [100] \\ \hline \end{tabular}
\end{table} 
Table 2: Passive suspension simulation results.
# Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification
## 2 Related Work
### Statistical Machine Learning-Based Model  
Traditional statistical machine learning-based representation models mainly focus on classification algorithms and feature engineering. Specifically, the conventional approaches for text sentiment analysis use the typical features such as bag-of-words [8], n-grams [7], and TF-IDF [9] as the input of machine learning algorithms such as Naive Bayes classifier (NB) [18], Decision Tree (DT) [19], and Support Vector Machine (SVM) [20] for classification. In terms of sentiment classification, these text features are mostly designed based on statistical word frequency information of sentiment-related words derived from resources such as sentiment lexicon [21]. For instance, Kang et al. proposed to improve the performance of Naive Bayes classifier for sentiment analysis of restaurant reviews by designing a high-quality sentiment lexicon, which can directly affect the text representation capability based on n-grams feature [22]. Hu et al. developed an improved Decision Tree model with the help of a sentiment lexicon, where co-occurrence sentiment-related words are reflected in the form of tree structure [23]. More typically, Support Vector Machine converted the raw text into a high-dimensional text representation vector, using the following word frequency features: n-grams, lexicon features, and part-of-speech tags, thus defeated all strong competitors in the SemEval-2014 task [24]. Accordingly, it is easy to see how the sentiment lexicon becomes a gold mine of sentiment linguistic knowledge to benefit the task of sentiment classification.  
Although statistical machine learning-based representation models have achieved a comparable performance in the task of text sentiment classification, its shortcomings are obvious. Firstly, these methods only focus on word frequency features and completely ignore the context structure information of text, so that it is difficult to capture the semantics of words. Secondly, the success of these statistical machine learning approaches generally relies heavily on laborious feature engineering work and massive extralinguistic resources. Unlike these models, our model uses the sentiment lexicon as an auxiliary information to automatically learn the semantic relations between context words.