esa-sceva/satcom-chunk-collection huggingface dataset:
# Precedence Table Construction Algorithm for CFGs Regardless of Being OPGs
## 5 Examples
### Example 3
#### 5.3.2. Left  
The Left sets are calculated as detailed in Table 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stack** & **Input** & **Action** \\ \hline \(\$\) & \(int\;id();int\;id(int,int);\$\) & Shift \\ \hline \(\$int\) & \(id();int\;id(int,int);\$\) & Reduce \\ \hline \(\$\) \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{l}{Left(S)} & & \\ \hline For \(S\longrightarrow ABC\) & & & \\ \hline \multirow{3}{*}{1. Evaluating the **first**} & \(A=S\) & & \(A=S\) \\  & \(\beta=\varepsilon\) & \(\beta=A\) \\  & \(B=A\) & 2. Evaluating the **first** & \(\beta=B\) \\  & conditional of Algorithm 2. & conditional of Algorithm 2. & \(\gamma=C\) \\  & \(\mathcal{L}(S)\supseteq\mathcal{L}(A)\) & & \(\mathcal{L}(S)\supseteq\mathcal{L}(A)\cup\mathcal{L}(B)\) \\ \hline \multirow{3}{*}{3. Evaluating the **first**} & \(A=S\) & & \\  & \(\beta=AB\) & & \\  & \(B=C\) & & \\  & \(\gamma=\varepsilon\) & & \\  & \(\mathcal{L}(S)=\mathcal{L}(A)\cup\mathcal{L}(B)\cup\mathcal{L}(C)\) & & \\ \hline \hline \multicolumn{2}{l}{Left(\(A\))} & & \\ \hline For \(A\longrightarrow aA\) & & & \\ \hline \multirow{3}{*}{1. Evaluating the **second**} & \(A=A\) & & \(A=A\) \\  & \(\beta=\varepsilon\) & & \(\beta=a\) \\  & \(a=a\) & & \(B=A\) \\  & \(\gamma=A\) & conditional of Algorithm 2. & \(\gamma=\varepsilon\) \\  & \(\mathcal{L}(A)\supseteq\{a\}\) & & \\ \hline \multicolumn{2}{l}{For \(A\longrightarrow a\)} & & \\ \hline \multirow{3}{*}{3. Evaluating the **second**} & \(A=A\) & & \\  & \(\beta=\varepsilon\) & & \\  & \(a=a\) & & \\  & \(\gamma=\varepsilon\) & & \\  & \(\mathcal{L}(A)=\{a\}\) & & \\ \hline \hline \multicolumn{2}{l}{Left(\(B\))} & & \\ \hline For \(B\longrightarrow bB\) & & & \\ \hline \multirow{3}{*}{1. Evaluating the **second**} & \(A=B\) & & \(A=B\) \\  & \(\beta=\varepsilon\) & & \(\beta=b\) \\  & \(a=b\) & conditional of Algorithm 2. & \(B=B\) \\  & \(\gamma=B\) & & \(\gamma=\varepsilon\) \\  & \(\mathcal{L}(B)\supseteq\{b\}\) & & \(\mathcal{L}(B)\supseteq\{b\}\cup\mathcal{L}(B)\) \\ \hline \multicolumn{2}{l}{For \(B\longrightarrow b\)} & & \\ \hline \hline \multicolumn{2}{l}{3. Evaluating the **second**} & \(A=B\) & & \\ \hline \multirow{3}{*}{3. Evaluating the **second**} & \(\beta=\varepsilon\) & & \\  & \(a=b\) & & \\ \cline{1-1}  & \(\gamma=\varepsilon\) & & \\ \cline{1-1}  & \(\mathcal{L}(A)\supseteq\{a\}\cup\mathcal{L}(A)\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Left Sets calculation for the CFG in Example 3.
## 2 Materials and Methods  
### Study Area  
The study was conducted at the Peace River Environmental Monitoring Super Site (PR-EMSS) within the Ecosystem Management Emulating Natural Disturbance (EMEND) site, located approximately 90 km northwest of Peace River, AB, Canada (Figure 1). This region is part of the Lower Foothills Natural Subregion [46]. The study plot, located at 56\({}^{\circ}\)44\({}^{\prime}\)38.10\({}^{\prime\prime}\) N, 118\({}^{\circ}\)20\({}^{\prime}\)38.08\({}^{\prime\prime}\) W, with an altitude of 867 m ASL, is characterized as an old growth stand of trembling aspen (_Populus tremuloides_). There are two vertical vegetation layers; the understory reaching a height of 3 m, while the overstory canopy growing to 12-15 m. Its topography is slightly sloping (<2%) in the west direction. The predominant wind direction is from the west. Mean annual air temperature is 0.6 \({}^{\circ}\)C and mean annual precipitation is 436.2 mm, with approximately 29% falling as snow [47]. Soils are primarily Orthic and Dark Gray Luvisols and have a depth of 1 m on average [48]. The study site has been monitored since 2013 with an eddy covariance system and meteorological station installed on a 30 m tower located at the eastern edge of the forest stand. Proximal remote sensing sensors, comprising of PRI and broadband spot radiometers mounted on the tower allowed the tracking of forest stand phenology, as well as monitoring the incoming and canopy reflected light. A wireless sensor network (WSN) continually measured the transmitted photosynthetic photon flux density (PPFD) within the canopy, providing continuous measurements of fraction of absorbed photosynthetically active radiation (fAPAR).
## 1 Introduction  
It is now well-known that the transport and retention behavior of organic and inorganic colloidal particles (<10 \(\upmu\)m) in porous media facilitates or retards contaminant transport when the contaminant is favorably adsorbed to colloidal particles [1; 2; 3]. Recent field and laboratory studies demonstrated that even a small amount of colloidal particles lead to the facilitation and retardation of contaminants such as radionuclides (e.g., \({}^{137}\)Cs and \({}^{239}\)Pu) or heavy metals (Pb(II) and Ni(II)) [4; 5; 6]. Therefore, the presence of colloidal particles as well as the transport behavior of colloidal particles should be taken into account in order to properly predict the contaminant transport. This would minimize underestimation or overestimation of the travel distance of contaminants [7; 8].  
Relatively small sizes of colloidal particles and large hydrodynamic forces exerted upon them cause the particles to be transported rather than retained, leading to the facilitation of contaminant transport. In contrast, if a relatively large number of colloidal particles are retained and not mobile, contaminants are more likely to be retarded and transport more slowly compared to the pore fluid. In this case, the larger amount of retained colloidal particles leads to the slower transport of contaminant because of more available adsorption sites in colloidal particles-sand media [9; 10]. Therefore, an accurate estimation of the quantity of immobile colloidal particles cannot be overemphasized for the prediction of contaminant transport.  
In order to predict the amount of immobile colloidal particles, many preceding studies of colloid transport have documented the impact of size ratio between colloidal particles and sand, solution chemistry (ionic strength or pH), fluid drag force, and degree of saturation [11; 12; 13; 14; 15]. However, the monodisperse latex colloids used in most of these experimental studies cannot represent the polydisperse colloids in subsurface such as clay particles, commonly observed in nature, that contaminant (e.g., heavy metals and radionuclides) are favorably adsorbed. In addition, sand also can be polydisperse, showing a coefficient of uniformity of about 10 [16]. This implies that the particle size distributions of sand and colloidal particles should be considered for the accurate prediction of contaminant transport.  
The objective of this study was to numerically investigate the impact of the particle size distributions of sand and colloidal particles on contaminant transport. The breakthrough behavior of contaminant at the given mean and standard deviation of particle size distributions of sand and colloid particles was evaluated and discussed. In addition, the amount of immobile colloidal particles at given particles size distributions was evaluated using the model proposed by [PERSON] and [PERSON] (2018) [17]. Consequently, the contaminant saturation and the bed efficiency of particle-retained sand medium were evaluated in order to represent simulated breakthrough curves quantitatively.
## 2 System Model
### Data Popularity Profile Modeling  
We consider a data library comprising \(M\) content represented by the set \(\mathcal{M}=\{1,2,\ldots,M\}\). For homogeneity, we consider each content size is equal to \(f_{m}\). The cache capacity of SBS and a UE is considered to be equal to \(c_{i}<M\) and \(c_{n}<M\), respectively. Since the cache resources of serving SBS and UE are limited, it is necessary to determine the popularity of content because data demands are trended accordingly [32]. The data popularity profile follows a Zipf distribution, where a content \(m\in\mathcal{M}\) request frequency is inversely proportional to its rank that is evaluated as follows:  
\[p_{m}=\frac{m^{-\gamma}}{\sum\limits_{m=1}^{M}m^{-\gamma}}, \tag{1}\]  
where \(\gamma\) represents the skewness of popularity distribution that controls the content popularity. If \(\gamma\) is large, less content will have high popularity, leading to the concentration of popularity weights on only a few pieces of content.
# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
## 2 Materials and Methods
### Segmentation Methods  
We employed two methods to obtain the segmentation masks of the cattle.  
The first is fully supervised segmentation (FSS). FSS uses the Mask R-CNN model, which requires a polygon-shaped ground truth mask to train a deep learning model. The masks in the FSS model are precise; however, creating a ground truth mask requires a human to outline the animal by selecting image points. A total of 9359 images were used in the study, and three livestock experts worked over two months to generate the ground truth masks for all images. The opinions of three experts were cross-validated to determine the final decision by majority vote in cases of disagreement.  
The time spent generating the ground truth masks is not the same for all image segmentation tasks. This is labor intensive, as the ground truth must be obtained each time additional data are acquired. These shortcomings of FSS motivated the development of a second method.  
The second method is a weakly supervised segmentation (WSS). It is a deep learning model developed using the Xception model as a backbone with Grad-CAM and Puzzle-CAM. The Xception network is trained to classify binary classes of 'Cattle' and 'Background', and the training dataset consisted of 5100 cattle images and 1554 images of the background. This model can perform image segmentation with the same level of ground  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Data** & **Validation Data** & **Test Data** & **Total** \\ \hline
4251 & 1063 & 4765 & 9359 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the dataset.  
Figure 4: Image frame selection results: (**a**) original data; (**b**) frame selection results.  
truth as a general classification model without the need to use the ground truth in the form of a mask. WSS is less accurate than FSS, but is easier to use when additional data are collected. The two methods have different advantages and disadvantages. The difference in the ground truth generation process is visualized in Figure 5.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 4 Results
### Reconstruction Accuracy in the Group-Delay Scenario  
The first set of simulations was carried out for the group-delay scenario, basically as reference for comparison with the single-delay scenario.  
WSI provided better accuracy than Margolis, Lagrange, and Akima uniform input signals with a high band limit \(f_{\mathrm{L}}>0.4\) Hz, and about the same accuracy as Kriging (Figure 9a). A SNR of 40 dB was achieved with OSR = 1.15. However, The RMSE declined only slightly for lower frequencies, for which WSI was outperformed by the other methods. Furthermore, WSI was highly sensitive towards distortions of the sample timing. It was impossible to achieve an RMSE below \(-10\) dB if samples were missing. Therefore, WSI was excluded from the further tests.  
The other methods were tested for jitter and missing samples. Kriging provided the lowest RMSE for all frequencies, followed by Margolis and Lagrange. For the missing samples test signal, every 11 th or 9% of the samples were deleted. The RMSE increased accordingly (dashed lines) and required a 6% to 12% higher OSR.  
Adding noise with RMSA = \(-60\) dB did not significantly affect the OSR for \(E_{Max}=-40\) dB (Figure 9b). The RMSE of all methods, except Akima, approached for \(f_{\mathrm{L}}<0.25\) Hz the noise floor of \(-60\) dB, with an offset between 0.8 dB (Kriging) and 1.9 dB (Lagrange). All methods proved to be stable towards noise in the group-delay scenario, although they did not provide additional noise smoothing capabilities.  
Kriging was the most accurate method, for both clean and noisy input signals. The restriction to time-limited signals required slight oversampling with OSR = 1.15 to achieve SNR = 40 dB and OSR = 1.34 for SNR = 60 dB.
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 4 Model Validation
### Comparison to Existing Models  
Table 3 compares the performance of the developed model against models built on each of the three existing metrics introduced in Section 3.1: spectral centroid (\(SC\)), \(\text{Ratio}_{1}\), and \(\text{Ratio}_{2}\). For each of these three metrics, a linear regression model was trained with the training dataset, and tested against the validation dataset. The crossover frequencies for the ratio metrics were selected from those listed in Section 3.1.1; choosing the frequencies that gave the highest Pearson's r and lowest RMSE.  
Since logarithmic transformations were tested for each metric during the development of the new model, the logarithmic transformations of the \(SC\) and ratio metrics were also calculated for comparison.  
Figure 6: Validation brightness ratings against the model of brightness, colours indicating each source type. Coloured dotted lines represent the line of best fit for each source, and the grey dashed line is the overall line of best fit (Colour online).  
For both the training and validation datasets, the new model out-performs models built on all existing metrics of brightness, in all performance measures.
# Dynamic Corrosion Test Using LiNO3 Containing Molten Salt for CSP Applications
## 1 Introduction
[15] performed a corrosion test under dynamic conditions using analytical grade solar salt (164 ppm Cl\({}^{-}\)) in a closed loop during 100 h. This research showed a notable increase \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Molten Salt (wt.\%)** & **Melting Point (\({}^{\circ}\)C)** & **Thermal Stability (\({}^{\circ}\)C)** & **Heat Capacity (/g \({}^{\circ}\)C)** & **Viscosity at 250 \({}^{\circ}\)C (cP)** & **Ref.** \\ \hline
60 NaNO\({}_{3}\)-40 KNO\({}_{3}\) & 221 & 589 & 1.54 & 5.51 & [2] \\
30 LiNO\({}_{3}\)-10 Ca(NO\({}_{3}\))\({}_{2}\)-60 KNO\({}_{3}\) & 132 & 567 & 1.40 & 5.72 & [3] \\
10 LiNO\({}_{3}\)-10 Ca(NO\({}_{3}\))\({}_{2}\)-60 & 132 & 580 & 1.55 & 5.78 & [4; 5] \\ KNO\({}_{3}\)-20 NaNO\({}_{3}\) & & & & & \\
30 LiNO\({}_{3}\)-57 KNO\({}_{3}\)-13 NaNO\({}_{3}\) & 123 & 595 & 1.74 & 5.65 & [6] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermal properties obtained in molten salts with LiNO\({}_{3}\)/Ca(NO\({}_{3}\))\({}_{2}\) additions compared with solar salt. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Molten Salt (wt.\%)** & **Steel** & **Temperature (\({}^{\circ}\)C)** & **Time (hours)** & **Corrosion Rate (mm/year)** & **Ref.** \\ \hline
20 LiNO\({}_{3}\) + 52 KNO\({}_{3}\) + 28 NaNO\({}_{3}\) & A1 & 390 & 2000 & 0.040 & [11] \\
30 LiNO\({}_{3}\) + 57 KNO\({}_{3}\) + 13 NaNO\({}_{3}\) & SB450 & 550 & 1000 & 0.150 & [12] \\
30 LiNO\({}_{3}\) + 57 KNO\({}_{3}\) + 13 NaNO\({}_{3}\) & T22 (2\%Cr) & 550 & 1000 & 0.130 & [12] \\
30 LiNO\({}_{3}\) + 57 KNO\({}_{3}\) + 13 NaNO\({}_{3}\) & T91 (9\%Cr) & 550 & 1000 & 0.013 & [12] \\
30 LiNO\({}_{3}\) + 57 KNO\({}_{3}\) + 13 NaNO\({}_{3}\) & X20 (12\%Cr) & 550 & 1000 & 0.005 & [12] \\
30 LiNO\({}_{3}\) + 57 KNO\({}_{3}\) + 13 NaNO\({}_{3}\) & AISI304 & 550 & 1000 & 0.004 & [13] \\
30 LiNO\({}_{3}\) + 57 KNO\({}_{3}\) + 13 NaNO\({}_{3}\) & In702 & 550 & 1000 & 0.002 & [13] \\
30 wt.\% LiNO\({}_{3}\) + 57 wt.\% KNO\({}_{3}\) + 13 wt.\% NaNO\({}_{3}\) + 13 wt.\% NaNO\({}_{3}\) & OCT & 550 & 1000 & 0.006 & [13] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Corrosion rates obtained in LiNO\({}_{3}\) ternary nitrate mixture. of corrosion in dynamic conditions. However, the experiment was very short to be compared with other studies. In the present study, a dynamic corrosion set up is proposed in order to simulate the real testing conditions in CSP plants, using a dynamic corrosion reactor with a mechanical stirrer. Corrosion test was carried out at 390 \({}^{\circ}\)C in A516 immersed in the ternary mixture composed by 30 wt.% LiNO\({}_{3}\) + 57 wt.% KNO\({}_{3}\) + 13 wt.% NaNO\({}_{3}\) during 1000 h.
# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 3 Classic Visual Saliency Models
### Top-Down Visual Saliency Models  
The top-down visual saliency model is often based on certain specific tasks. Due to the diversity and complexity of tasks, modeling is also more difficult [50]. The top-downvisual saliency model is mainly based on the Bayesian model. In addition, the Bayesian model can be regarded as a special case of the decision theoretical model, as both simulate the biological calculation process of human visual saliency.  
The Bayesian model in saliency prediction is a probabilistic combination model that combined scene information and prior information according to Bayesian rules. The model proposed by [PERSON] et al. [51] multiplied the bottom-up and top-down saliency maps to obtain the final saliency map. On this basis, [PERSON] et al. [52] integrated the feature prior information of the target into the above framework. [PERSON] et al. [53] proposed a saliency prediction model based on posterior probability. The SUN model proposed by [PERSON] et al. [54] used visual features and spatial location as the prior knowledge.  
The model based on decision theory in saliency prediction is a strategy model that decides the optimal plan based on the information and evaluation criteria requirements, i.e., how to make optimal decisions about perceptual information of the surrounding environment. [PERSON] and [PERSON] [55,56] believe that the salient features in the recognition process are derived from other classes of interest, and they defined top-down attention as a classification problem with the smallest expected error. [PERSON] et al. [57] recommended a temporal and spatial saliency model based on motion perception grouping. [PERSON] et al. [58] proposed a model based on the decision theory mechanism to predict regions of interest.  
Early machine learning models often use a variety of machine learning methods, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), or probability kernel density estimation, and they mostly combined the bottom-up and top-down methods. Notable examples included the nonlinear mapping model proposed by [PERSON] et al. [59], the regression classifier model proposed by [PERSON] et al. [60], and the linear SVM model proposed by [PERSON] et al. [61]. Those early machine learning models had a certain exploratory nature for subsequent deep learning models, and they played an important guiding role for the subsequently developed deep learning models.  
Although these classical models were designed in a variety of ways, their performance gradually reached a bottleneck due to handcrafted features. The development process of neurobiological models and classic models is shown in Figure 2.
# Nanofiber-Based Materials for Persistent Organic Pollutants in Water Remediation by Adsorption
## 2 Moderately Hydrophobic Pesticides
### Adsorbate Properties  
Table 1 presents the molecular characteristics of the four pesticides considered in this section, namely Alachlor, Atrazine, Chlorpyrifos and Diazion. Both Alachlor and Atrazine are organochlorine pesticides (OCPs). Alachlor is composed of an aniline aromatic structure, bound to an oxoacid and a chloroacetamide group via a central nitrogen group. Atrazine possesses a chlorotriazine aromatic structure, bound to two dimethylamine groups on the aromatic carbon atoms. Although Chlorpyrifos also possesses chlorine terminal groups, it is commonly classified with Diazion as organophosphorus pesticides (OPPs). Chlorpyrifos aromatic structure is made of a ring of trichloropyridine, with attachment of the three chlorine groups on three of the ring's carbon atoms. To form Chlorpyrifos, the chloropyridine is bound to an organophosphate structure, composed of a modified phosphate group, which is double-bonded to a sulphur atom, also called phosphorothioate. Diazion shares the same sulphur-phosphate structure, which is here bound to a pyrimidine or 1,3-diazine aromatic ring [26].  
The reported molecular weights range from 216 g/mol for Atrazine up to 351 g/mol for Chlorpyrifos, with each of the pesticides carrying a different molecular configuration. In terms of molecular size, Atrazine followed by Alachlor are smaller than organophosphates Chlorpyrifos and Diazinon, which is of interest for the study of the molecule diffusion in an adsorbent material with reported pore features.  
Hydrogen bond acceptor and donor counts are reported here to discuss the influence of chemisorption by H-bonding on the pesticide uptake by nanofiber adsorbents. Both Alachlor and Atrazine have an H-bond acceptor count of 2 due to their longer carbon chains. Atrazine, Chlorpyrifos and Diazinon have an H-bond donor count of 5, 5 and 6, respectively. H-bond donor groups are formed by amide and nitrogen groups in the case of Atrazine, and by nitrogen groups, as well as sulphur and oxygen atoms from the organophosphate backbone, as in the case of Chlorpyrifos and Diazinon. As each of the adsorbate molecules possesses an aromatic structure, adsorption by electron interaction through \(\pi\)-\(\pi\) stacking between the adsorbent surface unsaturated bonds and the molecule aromatic ring is believed to play a significant role in the pesticide uptake here [26; 27].  
Finally, the octanol-water partition coefficients are reported to point out the less hydrophobic character of the selected pesticides compared to persistent-classified pesticides, such as the here reviewed aliphatic organochlorines. Solubility in water at ambient temperature was also reported to give an insight into the maximum levels of water contamination achievable by these pesticides.
# Boron-Decorated Pillared Graphene as the Basic Element for Supercapacitors: An Ab Initio Study
## 1 Introduction
The carbon material by itself must have good electrical conductivity for efficient transfer of stored charge, high porosity for penetration of the electrolyte into the material cavities, and strong mechanical properties, since the flow of large discharge currents can lead to its overheating and destruction. These criteria are well met by a composite based on SWCNTs and graphene-pillared graphene (PGR). The use of this material in SC is currently being actively researched and discussed. Electrodes with a specific capacity of 145 to 200 F/g, excellent cyclicity, and high power density have already been synthesized [26, 27, 28, 29], but a numerical experiment can indicate the most effective direction for researchers. In our study, using the ab initio density functional theory (DFT) method performed in SIESTA software, we predicted methods to increase the efficiency of the SC electrode material on the base of pillared graphene and icosahedral boron clusters.