esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 2 Jet Flavour Tagging  
After a proton-proton collision, quarks and gluons hadronise and radiate, producing jets of particles. For the CMS Experiment, the jets are detected and clustered with the anti-\(k_{t}\) algorithm [17] with a radius \(R=\sqrt{\Delta\eta^{2}+\Delta\phi^{2}}=0.4\), where \(\eta\) is the pseudorapidity, and \(\phi\) is the azimuthal angle. Of particular interest are the jets coming from radiation and hadronisation of b or c quarks. Heavy-flavour jet tagging is linked to the properties of the heavy-hadrons in the jets. The main feature is the presence of a secondary vertex (SV) due to the long lifetime of heavy-flavour hadrons. The impact vector describes the distance between the primary vertex (PV), defined as the vertex with the greatest amount of transverse momentum (p\({}_{\rm T}\)), and the secondary vertex. Heavy-flavour jets are characterised by high-modulus impact vectors. The impact parameter (IP) is defined from the impact vector in two spatial dimensions (2D), in the transverse plane to the beam line, or in three spatial dimensions (3D). Figure 1 illustrates heavy-flavour jet production and the resulting SV.  
Figure 1: Illustration of heavy-flavour jet production with an SV [18].  
Two different algorithms are used to find the secondary vertices: adaptive vertex reconstruction (AVR) and inclusive vertex finding (IVF). After secondary vertex reconstruction is performed, dedicated algorithms have been developed by the CMS Collaboration in order to perform heavy-hadron jet tagging based on the properties of the secondary vertices from which particles in the jets originated. An example is the Combined Secondary Vertex (CSV) algorithm developed in Run-I that combines the variables of secondary vertexes in a likelihood-ratio discriminant [19]. In Run-II, new algorithms were developed for heavy-hadron tagging starting from the CSV and making use of ML techniques: _CSVv2_ and _DeepCSV_. Ultimately, a more-sophisticated technique, _Deeplet_, was developed that makes use of many variables of both high and low level.
# Interface Growth and Void Formation in Sn/Cu and Sn0.7 Cu/Cu Systems
###### Abstract  
In this work, the effects of electroplated Cu (EP Cu) and Cu addition (0.7%) in Sn solder on the intermetallic compounds (IMCs) growth and void formation were clarified by comparison with solder joints comprising of high purity Cu (HP Cu) substrate and pure Sn solder. After aging processes, a new IMC, Cu\({}_{3}\)Sn, was formed at the interface, in addition to Cu\({}_{6}\)Sn\({}_{5}\) formed in the as-soldered joints. The EP Cu and Cu addition (0.7%) both had limited effects on the total IMCs thickness. However, the effects varied on the growth behaviors of different IMCs. Comparing to the void-free interface between Sn and HP Cu, a large number of voids were observed at the Cu\({}_{3}\)Sn/Cu interface in Sn/EP Cu joints. The formation of these voids may be induced by the impurities and fine grain, which were introduced during the electroplating process. The addition of Cu suppressed the inter-diffusion of Cu and Sn at the interface. Consequently, the growth of the Cu\({}_{3}\)Sn layer and formation of voids were suppressed.  
Sn/Cu solder joint; intermetallic compounds; void; electroplated Cu; Cu addition Article
## 5 Experiment and Result
### Word Frequency Threshold  
In this work, we only considered high-frequency words in the training set and treated the other words as OOV. The word frequency threshold determines the vocabulary size, which also affects the system's performance. Here, we apply three different thresholds, i.e., 10, 50, and 100, and then count the vocabulary size and the ratio of OOV words in the training set. The results are shown in Table 2. For example, when the word frequency threshold is set to 50, the vocabulary contains 529 words and covers 77.3% of words in the training set. Generally, when the word frequency threshold grows, the vocabulary size decreases, and the OOV ratio increases.  
We also plotted the log-likelihood curves of phone-level prosodies with various word frequency thresholds. As is depicted in Figure 5, we saw the best performance at the threshold of 50. When the threshold is too large, fewer words are considered, and the system performance is harmed. However, when the threshold is too small, many low-frequency words are included in the vocabulary. These acoustic word embeddings are only trained with very limited data, which also leads to a decline in system performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Num Params** & **Inference Speed** \\ \hline None & 96.075 M & \(1.7138\times 10^{-3}\) \\ BLSTM & 98.341 M & \(1.9806\times 10^{-3}\) \\ Transformer & 132.872 M & \(2.2783\times 10^{-3}\) \\ Conv+Transformer & 133.003 M & \(2.3787\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of parameters and inference speed (second/frame) with various encoder architectures.  
Figure 4: The log-likelihood curves of phone-level prosodies with various word encoder architectures.
## 5 Discussion
### Local Targets and Global SDG Achievement  
Through the EMP framework, the potential technologies were prioritised to better suit the goals of the community. In addition, the influence of the resulting higher priority technologies could also be mapped to identify which global SDGs would be met by utilising the appropriate driving forces that were informed by the process. Therefore, the results of the EMP framework could not only answer community needs, but also contribute to the achievement of global SDGs. For example, the use of energy-efficient technology would influence SDGs 9 and 11, with it being regarded as an emerging and enabling technology in Australia [74]. In another example, the implementation of wind energy would help to decrease the energy use and the carbon intensity of FB, which is related to the technology (carbon intensity and energy intensity) driving forces and matches SDGs 4, 7-9 and 11-13. According to the results, SDGs 3, 4, 6-9 and 11-15 were addressed by the FB energy master plan in our case study. A more detailed elaboration of the way in which the EMP technologies and SDGs were mapped via the SSPs is listed in Appendix B.
## 3 Results
### Manipulability of Microtool
#### 3.1.1 Movement of Microtool in Optical Traps and Fluid Flows  
In the first experiment, a viscous drag force method was introduced to test the movement of the microtool in optical traps and fluid flows. Figure 7 illustrates the geometry of this experiment. First, a microtool was trapped and immobilized in the suspending medium. To avoid friction and collision between the microtool and culture dish, the microtool was lifted 1 \(\upmu\)m from the bottom of the culture dish. At this moment, the positions of the traps, i.e., Trap1, Trap2, and Trap3 in Figure 7, coincided with those of the microtool handling spheres, i.e., A, B, and C in Figure 7, respectively. Then, the motorized stage was moved in a defined velocity and applied to the fluid flow passing the microtool. The microtool handling spheres would deviate from the traps and be immobilized in new balanced positions under the dragging force of the fluid flow and trapping force of the traps. By recording the positions of the microsphere before and after moving the motorized stage, we could obtain the deviations of the optical handling spheres from the optical traps. Figure 8 shows the deviations of A, B and C from optical trap centers as functions of fluid flow velocities when the laser power of each optical trap was set to 0.3 W. We observed that the deviations of A, B, and C are nearly identical, thereby demonstrating positive stability and balance of the microtool under optical traps and fluid flows. Furthermore, we observed that the deviations are no larger than seven-tenths of the sphere radius until the velocity of the fluid flows increases to 30 \(\upmu\)m/s, which exhibits high trapping efficiency and stiffness. The experimental results lay the groundwork for the subsequent translational and rotational manipulation of the microtool.
# Effect of Heat Treatment on Water Absorption of Chinese fir Using TD-NMR
## 3 Results and Discussion
### Content of Free Water and Bound Water  
Previous studies showed that the high-temperature heat treatment might lead to the degradation of wood components, reducing the relative content of polysaccharides and the number of hygroscopic groups, such as hydroxyl groups, carbonyl groups, and so on. [43; 44]. Thus, the water absorption of wood was reduced and its dimensional stability was increased [45].  
The content of bound water and free water in untreated samples and heat-treated wood samples were measured as a function of immersion time, as illustrated in Figure 5. The content of bound water existing in untreated samples reached about 30% after the longest immersion time, and the MC values were slightly reduced for the 160 \({}^{\circ}\)C and 180 \({}^{\circ}\)C heat-treated samples, but no obvious difference was found between them. However, the heat treatment at 200 \({}^{\circ}\)C noticeably decreased the bound water content, reaching about 25.65% MC. The influence was more significant for the sample treated at 220 \({}^{\circ}\)C, reaching 22.17% MC. The decrease of the bound water content of the heat-treated samples might be caused by the degradation of cell wall components. The reduction in the hydroxyl groups resulted from the depolymerization and hydrolyzation of hemicellulose and the structural change of lignin due to the crosslinking at higher treated temperatures, because both could affect the amount of bound water [6; 46; 47]. Moreover, the heat treatment partially blocked the access of water to wood cell walls, which was most likely an important reason for the decrease of bound water content [48]. Unlike the bound water, the mass of free water for all the tested samples had not reached equilibrium after immersion for 60 days. In contrast to the untreated sample, the heat treatment decreased the content of free water for all the samples under current experimental conditions. The value decreased as the treatment temperature increased. Similar to the bound water, the heat treatments at 160 \({}^{\circ}\)C and 180 \({}^{\circ}\)C slightly reduced the free water content, while after 200 \({}^{\circ}\)C and 220 \({}^{\circ}\)C heat treatments, the MC values were only about 78.80% and 75.58% of the untreated sample at the 60 th day, respectively. In addition to the effects of changes in chemical composition, a large number of pits were closed when heat treatment was at temperatures above 200 \({}^{\circ}\)C [49]. Thus, the absorption routes of free water were blocked and the transport of free water was impeded.  
Figure 5: The content of bound water (**a**) and free water (**b**) in untreated and heat-treated Chinese fir wood samples.
## 4 Discussion
Additionally, the findings also indicate that in scenarios with poor connectivity, mobility, or other adverse conditions, cache memory serves as an efficient and valuable resource.
## 4 Case Study: The Northeast Part of the Slovenian Transmission System
### Case 1--Faults in Loop Topology with a Strong Network Supply  
In the first case, faults were assumed on the Formin-Cirkovce line, as shown in Figure 10. Power generation of IIDGs and line voltage magnitudes at the 110-kV level are shown in Figures 11 and 12 for a Ph-Ph fault. Due to the loop-network topology, all PVs and BESs changed their power during the fault, i.e., active power was reduced, while reactive power was increased. Minor responses were noticed by BES Pekre and by PVs Lenart and Sl. Vrh, since they were the more distant from the fault location, but close to the SS 400/110 kV Maribor. The largest responses were noticed by PVs Lendava, Liutomer, and Ormoz, which were the more distant from both SSs 400/110 kV. Other PVs show moderate responses in generated power. PVs Ptuj, Breg and BES Kidricevo also showed moderate responses, even though they are the closest to the fault location; however, they are also electrically close to the SS 400/110 kV Maribor. Voltages during the fault were less reduced at PV and BES locations with smaller changes in the generated power, which shows the dominant impact of the aggregated network supplies. Moreover, compared to the concept when  
Figure 10: Single line diagram of the Slovenian northeast transmission network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Substation** & **TR 110/X [kV]** & \(P_{\text{TR,n}}\) [MW] & \(P_{\text{L,max}}\) [MW] & \(P_{\text{IIDG,n}}\) [MW] \\ \hline Lenart & 21 & 2 \(\times\) 20 & 8.2 & 5.9 \\ Sl. Vrh & 21 & 2 \(\times\) 31.5 & 13.7 & 2.3 \\ Radenci & 21 & 2 \(\times\) 31.5 & 15 & 7.5 \\ Sobota & 21 & 2 \(\times\) 40 & 25.3 & 12.6 \\ Mackovci & 21 & 1 \(\times\) 31.5 & 4.9 & 1.5 \\ Lendava & 21 & 2 \(\times\) 31.5 & 15.8 & 3.3 \\ Liutomer & 21 & 2 \(\times\) 31.5 & 9.2 & 4.5 \\ Ormoz & 21 & 2 \(\times\) 20 & 7.0 & 2.1 \\ Ptuj & 21 & 2 \(\times\) 40 & 18.7 & 7.6 \\ Breg & 21 & 2 \(\times\) 31.5 & 13.6 & 16.1 \\ Kidricevo—PV & 10.5 & 1 \(\times\) 31.5 & 9.7 & 4.0 \\ Kidricevo—BES & 10.5 & 2 \(\times\) 31.5 & - & 16.0 \\ Kidricevo—load & 10.5 & 2 \(\times\) 60 & 60 & - \\ Pekre—BES & 21 & 1 \(\times\) 15 & - & 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main substation data with current capacities of the PV and BES units.  
PV and BES units would be switched off during the fault, the voltage profile was slightly improved by the FFC-based IIDGs at the buses near the fault location. Regarding the distance protection operation for different fault types, no issues were observed, regardless of the IIDG concept (FFC or switching off), which is entirely due to the dominant SSC of aggregated network supplies.
# Classification of Imbalanced Data Represented as Binary Features
## 2 Materials and Methods
### Evaluation Metrics  
In every classification method, evaluation metrics are essential in determining whether the method is effective in learning the data or not. Accuracy is the most frequently used evaluation metric in machine learning algorithms. However, since this study was focused on imbalance learning, accuracy alone was not sufficient to assess the effectiveness of a model [35]. Therefore, other than accuracy, we used other evaluation metrics in the family of threshold metrics such as specificity, sensitivity, recall, and F1-score to assess the effectiveness of our method. The definitions of true positive (TP), true negative (TN), false negative (FN), and false positive (FP), and all metrics used in this paper were calculated in the standard calculation [36] with \(\beta=1\) for F1-score.  
Accuracy is the most basic evaluation metric in machine learning. However, it only works well when there are an equal number of samples in each class. When it comes to class imbalance situation, other metrics are advisedly used along together with accuracy. This occurs because in the imbalance case, the model tends to classify each given sample as the majority class, whilst the actual class is the minority class, while still achieving high accuracy. When dealing with class imbalance problems such as in a rare disease dataset, the cost of failing to diagnose a sick person's sickness is far greater than the cost of subjecting a healthy individual to further testing. In this case, another metric such as sensitivity will give better interpretation than accuracy.  
The sensitivity or recall is a performance metric drawn from the positive samples. It shows the proportion of positive samples that are correctly predicted as positive. Contrary to this, specificity is drawn from the negative samples, where it shows the proportion of negative samples that are correctly predicted as negative. These two metrics are usually used in medical domain data, such as disease-related data, drug discovery, etc. Unlike accuracy, which measures how often a classifier correctly predicts the positive and negative samples, sensitivity and specificity also measure the same case, but in a separate way or in each class. Precision is the ratio between the TP and all of the predicted as positive values, i.e., TP and FP. It is drawn from the positive prediction value, which means that it is the measure of samples that are correctly classified as positive out of all the positive predictions.  
When it comes to reducing the number of false positives, this measure is crucial, since this metric puts more focus on the positive class. F1-score is the harmonic mean of the precision and recall/sensitivity. This score can be an indication of good precision as well as good recall. In the multiclass case, the above metrics are calculated by adopting the one versus all method. In this case, the dataset is treated as a binary classification where the target class becomes the first class, for example, class 1, while the other classes become class 0.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## 1 Introduction  
Nowadays, the total output of grapes in the world is about 23.38 million tons, ranking in the top five among all fruits [1]. However, lots of solid residues (about 25% of the weight of grapes) are produced in the brewing process, and the solid residues are mainly composed of grape pomace [2]. Grape pomace mainly consists of grape skin (38-52%) and grape seed, which are rich in bioactive compounds such as phenols [3, 4], oils [5], polysaccharides [6], proteins and dietary fibers [7]. These solid residues are usually discharged into the environment as waste, which causes more serious environmental pollution, such as surface and ground water pollution, waste decomposition and foul odor [8]. Therefore, the reuse of grape pomace could reduce the environmental impact, which greatly encourages wine industry sustainable growth and is particularly appealing to the food and pharmaceutical business.  
In recent years, as natural biomolecules, plant polysaccharides have become a research hotspot with their low toxicity, safety, high efficiency and other characteristics [9, 10, 11, 12, 13]. Many natural polysaccharides have been proved to be crucial to free radicals to protect the body against oxidative damage [14, 15, 16, 17]. Usually, grapes contain powerful antioxidants known as polyphenols. These are thought to have anti-inflammatory and antioxidant properties. Other compounds, such as resveratrol, lutein and zexaxanthin, also benefit health. Regarding the polysaccharides from grapes, it was reported that Japanese grape (Hovenia dulcis) polysaccharides can be exploited as a multi-functional additive or antioxidant agent [18]. Wine grape polysaccharides play important roles in the technological and sensory characteristics of wines. Moreover, many studies have mainly focused on thepharmacologic actions of grapes, and few studies have focused on the polysaccharides extracted from grapes and by-products, let alone their structural characterization and biological activities.  
Inspired by research on grape ingredients as antioxidant agents, we set out to explore the value of grape pomace polysaccharides (GPPs). We wanted to know whether GPPs have antioxidant activity in vitro and in vivo. Therefore, in this study, we extracted polysaccharides from grape pomace and characterized the physicochemical properties. Then, the antioxidant activity of grape pomace polysaccharides was observed using oxidative tests in vitro, and the hepatotoxicity effective effect on acute CCl\({}_{4}\)-caused liver damage in mice was tested in vivo.
_Applied_  
_sciences_  
_Presence of _Quercus Suber_ Soft-Leaf Defoliators on Trees with Distinct Foliar Monoterpene Emission Profiles_  
[PERSON] \({}^{\star}\)\({}^{\copyright}\), [PERSON] and [PERSON] \({}^{\copyright}\)  
Department of Agroforestry Science, Higher Technical School of Engineering (ETSI), Campus El Carmen, University of Huekva, 21007 Huekva, Spain; [EMAIL_ADDRESS] (D.R.); [EMAIL_ADDRESS] (R.T.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Featured Application: This work could serve as a basis for exploring the potential value of applying remote sensing and geographic information system procedures to elaborate defoliator risk maps based on differences in soft-leaf defoliator activity associated with the relative position of _Quercus suber_ trees within stands, as well as differences that can be found in the appearance (size/colour) of leaves from cork oak trees exhibiting distinct foliar monoterpene emission profiles.**  
**Abstract: The cork tree, _Quercus suber_ L., is a characteristic species of the Dehesa agrosilvopastoral system, typical of western Spain. Defoliating insects are an important component of these ecosystems. This study assessed the presence and impact of defoliators feeding on _Q. suber_ soft leaf tissue and their relationship with foliar monoterpene emission profiles. Samplings consisted of weekly tree beating (15 times per tree per sample) during the flight period of key species. We studied 26 cork trees with known profiles of foliar monoterpene emission (13 ppinene and 13 limonene chemotypes). We identified a total of 272 larvae from 9 species. The main species were _Catocala mupmhagga_ ESEP (40.8%) and _Perclista antriel_ know (27.6%). Notably, 70.6% of larvae were found on trees with a ppinene chemotype. The combined abundance of the four key defoliator species (_C. mymhagga_ L., _P. anfrei, Bena bicolarna L._, and _Cyclophophon punctaria_ L.) was 62.7% lower on limonene-chemotype trees than ppinene-chemotype trees. Significant differences were found in defoliation damage between leaves with distinct terpene emission profiles. These results suggest that both the abundance and damage caused by defoliators differ with trees' emission profiles, and this may indicate differences in palatability and/or nutritional quality between _Q. suber_ trees with distinct foliar monoterpene emission profiles.  
**Keywords:** Lepidoptera; Hymenoptera; cork oak; _Catocala; Perclista; Bena_