esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### GSV and NPP  
From the BGC-MAN simulations, the estimated values of the GSVs of oak forests in Kasuya (Japan), Gwangneung (South Korea), Seolmacheon (South Korea), Sohung (North Korea), and Bikinsky (Russia) were 410, 511, 154, 65, and 153 m\({}^{3}\) ha\({}^{-1}\), respectively, in 2005. The GSVs of pine forests in Gwangneung (South Korea), Wonsan (North Korea), Honghuaerji (China), Oblast (Russia), and Pribaykalsky (Russia) were 392, 145, 147, 282, and 124 m\({}^{3}\) ha\({}^{-1}\), respectively, during the same period. The GSVs of larch forests in Laoshan (China), Tsenkhermandal (Mongolia), Khentei (Mongolia), Yeravninsky (Russia), and Buryatia (Russia) were 127, 156, 150, 227, and 116 m\({}^{3}\) ha\({}^{-1}\), respectively (Figure 3).  
Figure 2: Climatic characteristics of the northernmost site (Buryatia, Russia) and the southernmost site (Kasuya, Japan) in 2005.  
Along the MLE in Northeast Asia, the GSV ranges were wide in the order of oak, pine, and larch forests. Protected forests showed much higher GSV, but GSVs in this region converged around 154 m\({}^{3}\) ha\({}^{-1}\). Following the same order of sites among all forest types as for the GSV results, NPP values in oak forests were also estimated to be 5.05, 5.39, 5.22, 4.18, and 3.70 Mg C ha\({}^{-1}\), respectively, in 2005. The NPP values of pine forest sites were 6.39, 6.75, 2.68, 3.49, and 2.50 Mg C ha\({}^{-1}\), and the NPPs of larch forests were 5.24, 1.12, 1.35, 1.27, and 0.49 Mg C ha\({}^{-1}\), respectively (Figure 4).  
Figure 3: Growing stock volume (GSV) estimation through BGC-MAN simulations from 1960 to 2005.
## 4 Discussion
### Residency and Site Fidelity  
The residency and site fidelity of tiger sharks were demonstrated through both mark-recapture and telemetry. However, the overall recapture rate near Bimini was low, suggesting many tiger sharks may be moving out of the area. The local recapture rate of 4.7% was lower than the 7.3% recapture rate for tiger sharks tagged in the USA over the past 50 years [49]. This may be a result of shark fishing and surveys occurring at considerably higher frequencies in the USA than in The Bahamas, thereby increasing opportunity for recaptures. This is supported by the shorter mean time at liberty for tiger sharks tagged in the USA (mean 365 days) [49] compared to those tagged near Bimini (mean 790 days).  
There was low residency and longevity of small juvenile tiger sharks near Bimini, indicating limited long-term fidelity of this age class to the area. It is possible that small juvenile tiger sharks are using the broader Great Bahama Bank area, which provides ample key habitats for tiger sharks, such as seagrass, sandflat, and nearshore waters [50], but this vast area has relatively little acoustic receiver coverage compared to the nearshore waters of the USA. Hence, the lack of acoustic coverage could partially explain the infrequent acoustic detections in the area. The overall low detections, limited residency within the array, and short longevity of small juvenile tiger sharks may also be indicative of high natural mortality at this early life stage. In the western North Atlantic, the annual mortality rate was estimated at 61% and 73% for YOY and age 1 tiger sharks, respectively [30], and this high mortality could limit the ability to monitor young tiger sharks across long durations.  
Large juvenile and mature tiger sharks had a higher residency index and longevity than small juveniles, potentially due to higher survival or biological drivers such as foraging and reproduction. Multi-year fidelity of tiger sharks in The Bahamas was previously documented at a provisioning dive site, Tiger Beach, Grand Bahama [51; 52]. At this location, the seasonal residency of mature female tiger sharks was hypothesized to be partly drivenby reproductive cycle, with mature females utilizing the waters of The Bahamas during gestation [27]. Mature female tiger sharks had the highest residency to Bimini during the cooler water winter months, before departing in late spring, potentially following parturition. Despite the estimated 2-year reproductive cycle, some females were present in Bimini in consecutive years. Therefore, reproduction alone may not be driving seasonal residency, as some tiger sharks were using the waters around Bimini presumably during both 'pregnant' and'recovery' years. This is similar to what has been documented for great hammerhead sharks near Bimini, in which females had annual fidelity to the area [5].
## 5 Isolated S-Band Antenna Results
Figure 13: AR measurement vs. simulation of isolated antenna, \(\text{f}=2.205\) GHz: (**a**) Port 1; (**b**) Port2. operating within the same frequency band, utilizing circular polarization, and employing microstrip patch technology. The analysis in Table 3 demonstrates that the designed antenna operates in circular polarization within the S-band, aligning with most references, yet it exhibits several advantages over alternative designs. Firstly, it is highly compact, with a total height of only 6 mm and a weight of 30 g. It provides a relatively high gain (7 dBi) for a single antenna, maintaining wide angular coverage. However, its key innovation lies in achieving impedance bandwidth and axial ratio (AR) bandwidth greater than 8% (better than the referenced antennas) despite being fed from a single point, which simplifies the structure and minimizes height. This is made possible through a stacked dual-patch configuration with integrated notches, a feature not achieved in the referenced works. In addition to that, the antenna has been space-qualified, demonstrating no degradation in its electromagnetic performance after rigorous environmental tests. It was successfully integrated and launched into space aboard the Falcon 9 launcher by SpaceX on January 14 as part of the Leader-S CubeSat mission within the ANSER constellation.
###### Abstract  
5-Aminoisoquinoline (5-AIQ) is a water-soluble, potent and selective Poly (ADP-ribose) polymerase 1 (PARP-1) inhibitor, widely used as a biochemical and pharmacological tool to study the inhibitory effect of PARPs enzyme. In this study, a simple, selective and reliable ultra-performance liquid chromatography-tandem mass spectrometry assay has been developed for the quantitative analysis of 5-AIQ in plasma using pantprorazole as an internal standard (IS). Both 5-AIQ and IS were separated on an Acquity CSH\({}_{18}\) (2.1 \(\times\) 100 mm; 1.7 \(\mu\)m) column after chromatographic elution of mobile phase comprising of 10 mM ammonium acetate and acetonitrile (35:65; \(\
icefrac{{\pi}}{{\upsilon}}\)) at a flow rate of 0.3 mL/min. Electrospray ionization in positive mode was used for sample ionization and precursor to product ten transitions of 145.0 \(>\) 91.0; 145.0 \(>\) 117.4 for 5-AIQ and 384.0 \(>\) 138.1 for IS were used for detection and quantification in multiple reaction monitoring mode. The assay was linear in the concentration range of 1.0 to 666 mg/mL with correlation coefficient of \(\geq\)0.995. The precision and bias were within the acceptable limits of \(\leq\)12.68% and \(-\)8.6 to 5.9%, respectively, with mean recovery of 79.1% from plasma and negligible matrix effects (92.4%). In silico ADME prediction, 5-AIQ showed to be very soluble in water and high gastrointestinal absorption along with blood-brain barrier (BBB) permeability. The validated assay was successfully applied in a metabolic stability study, and 5-AIQ was moderately metabolized by human liver microsomes with an in vitro half-life of 14.5 min and intrinsic clearance of 47.6 \(\mu\)L/min/mg. The validated method can be utilized for future pharmacokinetic and bio-distribution studies.  
5-Aminoisoquinoline; UPLC-MS/MS; metabolic stability; microsomes +
Footnote †: journal: Applied Sciences
# Blur Kernel Estimation by Structure Sparse Prior
## 4 Experiments and Results
### Evaluation of The Parameters  
In the proposed method, four parameters can be adjusted to improve the deblurring performance: \(\eta,\lambda_{1}\), \(\lambda_{2}\) and \(ks\). Among them, \(\lambda_{1}\) is the weight coefficient in Equation (10); \(\eta\) and \(\lambda_{2}\) are the weight coefficients in Equation (12); and \(ks\) represents the size of kernel \(k\). The size of kernel \(k\) was initialized as \(3\times 3\) and it grew with the ratio of \(\sqrt{2}\) between different levels. The number of levels was decided by the final size of kernel \(k\).  
We set \(\eta=0.001\), while the size of kernel, \(\lambda_{1}\),and \(\lambda_{2}\) were flexible to ensure the best recovery effect for different input images. The parameters were initialized as follows: \(ks=19,\lambda_{1}=0.1\), and \(\lambda_{2}=0.5\).  
First, we fixed \(\lambda_{1}\) and \(\lambda_{2}\), and chose the best \(ks\) for kernel estimation. If the kernel size we set were too small or too large, it would not be good for accurately estimating the kernel. Experimental results can be found in Figure 5, from which we can get that the best size of kernel is 27. Then we fixed \(ks\) and \(\lambda_{1}\) and chose the best value for \(\lambda_{2}\). If the \(\lambda_{2}\) we set were too small, it would not have the effect of stabilizing the solution and the regularization term on \(x\) would have little influence in the process. However, if \(\lambda_{2}\) were too large, \(x\) would be over constrained. Experimental result can be found in Figure 6, from which we can get that the best value for \(\lambda_{2}\) is 0.5. Finally we fixed \(ks\) and \(\lambda_{2}\) and chose the best value for \(\lambda_{1}\). Similarly, \(\lambda_{1}\) cannot be too large as \(k\) would be over constrained. However, if \(\lambda_{1}\) were too small, the regularization on \(k\) would not have the function of enforcing sparsity. Experimental result can be found in Figure 7, from which we can get that the best value for \(\lambda_{1}\) is 0.5.  
Figure 5: Evaluation of the parameters. SSIM values with different kernel size on the experiments.  
Figure 6: SSIM values with different \(\lambda_{2}\) on the experiments.
## 2 Appendix Segmentation with Double-Layered Outlier Rejection FCM
### Double-Layered Outlier Rejection FCM (DORFCM)
#### 2.3.1 Layer-1 Learning (Step 1 to Step 4)  
**Step 1:** Initialize \(c\) (\(2\leq c<n\)) as the number of clusters, and exponential weight \(m\) (\(1\leq m<\infty\)), and initialize the error threshold (\(\varepsilon\)) for terminating condition of the first layer learning and the membership degree \(U(0)\), repetition parameter \(r=0\).  
**Step 2:** Compute the value of central vector \(v_{ij}\) as shown in Equation (3) for \(\{v_{i}\ |\ j=1\), \(2\),..., \(c\}\).  
\[v_{ij}^{(\mathrm{g})}=\frac{\sum_{k=1}^{n}(U_{ik})^{m}X_{kj}}{\sum_{k=1}^{n}( U_{ik})^{m}} \tag{3}\]  
where \(X\) is the input pattern, \(i\) is the cluster index, and \(j\) is the pattern node index. \(k\) is the pattern index, \(n\) is the number of patterns, and \(U\) is the membership function.  
**Step 3:** Define the ORFCM cost function as Equations (4) and (5) with \(\beta\) as Equation (2).  
\[d_{ik}=\left[\sum_{l=1}^{l}\left(x_{kj}-v_{ij}\right)^{2}\right]^{\frac{1}{2}} \tag{4}\]  
\[u_{ik}^{(\mathrm{g+1})}=\frac{1}{\sum_{j=1}^{c}\left[\frac{\beta\times d_{ik}^ {\beta}}{\beta\times d_{jk}^{\beta}}\right]^{2}}\text{ for }I_{k}=\varnothing \tag{5}\]  
where the distance \(d_{ik}\) is defined as the Euclidean distance between the \(k\)-th pattern \(x_{k}\) and \(v_{i}\), the central vector of the \(i\)-th cluster, and \(u_{ik}\) is the membership degree of \(x_{k}\) among patterns in the \(i\)-th cluster.  
Figure 1: Double-layered learning structure.  
**Step 4:** Compute the difference (\(U_{ik}(r+1)-U_{ik}(r)\)) between the new membership and the previous membership degree at the time of \(r\). If the difference is less than the error threshold (\(\varepsilon\)), then the algorithm goes to Step 5, otherwise go to Step 2.
## 3 Results and Discussion  
### Quantification of Major Compounds and the Residual Quantity of Methanol  
In recent last years, a re-emerging interest in natural products for industrial purposes has been observed due to their reputable sources of new active pharmaceutical ingredients [1]. _S. nigra_ L., for example, has been extensively used in traditional medicine, particularly in Europe, where it has been officially recognized as a potential bioactive ingredient by the European Union. Due to its properties in several supplements and nutraceutical products, it is now commercially available in markets in different countries [3; 29]. However, the biological activities of elderflowers have been demonstrated mostly in non-clinical studies [6]. Therefore, the aim of the studies performed by our group was to provide a scientific support and validation to the use of _S. nigra_ L. [5]. In a previous study, several compounds were identified in the elderflowers' methanolic extract, including rutin, isoquercetin, isorhamnetin-3-\(O\)-glucoside, naringenin, quercetin-4-\(O\)-glucoside, isorhamnetin-3-rutinoside, luteolin-7-\(O\)-glucose, malic acid, and eriodictyl [6]. In the same work, rutin and naringenin were identified as the major phenolic compounds in extract (Figure 2) [6]. These compounds were quantified at \(74.93\pm 17.00\) mg/g and \(31.70\pm 0.14\) mg/g of extract, respectively. The quantification of naringenin was done mainly due to the association of this flavonoid to the Coll inhibition [30], as previously reported [6].  
The analysis of volatile compounds, particularly MeOH, was performed to quantify the residual amount of the solvent used in the extraction method. The residual MeOH concentration in the extract was 3012 ppm, as shown in Figure 3. The ICH Q3C (R6) classify the MeOH as a Class II solvent and it established the limit of 3000 ppm per day [31]. The results revealed solvent levels just around this value, with no statistical difference when compared to the guideline's recommendation. Assuming that the same dose is safely used in the in vivo assays to a 70 kg human adult, the amount of methanol would be around 105 ppm per dosage, which is below the maximum dosage recommended per day.
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
## 1 Introduction
The aforementioned research achievements have provided theoretical foundations for the efficient application of gas drainage. However, gas flow in coal seams constitutes a complex physical process. Most researchers primarily focus on constructing theoretical analytical models to investigate porosity and permeability evolution during gas drainage or studying the impact of individual parameters on drainage efficiency. Consequently, it is imperative to conduct comprehensive simulation analyses integrating multiple influencing factors to determine optimal borehole parameters and validate their rationality through engineering practices. To address the unresolved problem of excessive gas concentration in the upper corner of Fengtai coal mine in Pingding County, Yangquan City, Shanxi Province, China, this study used COMSOL numerical simulation software to analyze the variations in gas pressure and effective extraction radius under different borehole parameters, including diameter, negative extraction pressure, and extraction duration. This study determined the optimal borehole configuration through systematic simulation and subsequently carried out on-site verification through engineering practice to validate the theoretical results.
# Polypphenols: Natural Antioxidants to Be Used as a Quality Tool in Wine Authenticity
## 4 Current Analytical Approaches to Wine Authenticity  
### Overview  
Conventional methods of wet chemistry or basic applications of liquid chromatography are widely used in the determination of wine polyphenols as these procedures are part of the official methods of analysis [119]. However, the emergence of disciplines such as HRMS or NMR metabolomics combined with advanced chemometric techniques has been proven as a powerful tool to perform chemotaxonomic studies through identification of numerous grape and wine metabolites [120]. Metabolomics is a field involved in the study of multiple metabolites in a cell, a tissue, or an organism. The precise structure determination provided by NMR or the extensive metabolite coverage of MS-based metabolomics are among the properties that have made them the two most popular disciplines in food authenticity. Both, however, have limitations as described in detail in previous reports [120; 121; 122].  
Metabolomic studies are divided into two main platforms: the targeted and non-targeted or untargeted approaches. Targeted metabolomics is hypothesis testing approaches that aim at the detection, identification, and quantification of a specific group of compounds. They usually require a sample preparation step (though minimal in most cases) and subsequent optimization and validation studies as well as the provision of adequate purity commercial standards. Non-target or untargeted screening (NTS) approaches are \"unbiased\" hypothesis-generating approaches that focus on the comprehensive detection and (putative) annotation of multiple endogenous and exogenous low-molecular-weight (<1 kDa) molecules/metabolites in a biological system. Suspect screening metabolomics is a midway approach [123] where specific mass to charge ratios are targeted in the absence of commercial standards based on literature or mass spectral bases.  
Current scientific efforts on wine authenticity aim to unveil significant information in various grape and wine-related aspects including variety and origin verification as well as process monitoring by detecting and identifying characteristic patterns or markers for each aspect [124]. The purpose of this section is to present the contemporary methods platforms approved by the OIV regarding wine quality and, in consequence, highlight current research findings focusing on polyphenic compounds and wine authenticity.
## 2 Artificial Neural Networks Topology
### DFF Neural Network  
A DFF network, also called a Multilayer Perceptron (MLP), is a neural network structure that is used in many applications. It is applied to both discrete and discontinuous problems [36; 37].  
MLPs consist of neurons, which are structured into layers. The input layer and the output layer are the first layer and the last layer, respectively. The hidden layers are the ones in between the first layer and the last layer.  
There is a specific function for each layer in the MLP. The input signals are accepted through the input layer and redistributed to all the hidden layer neurons. The hidden layer stimulus patterns are propagated to the output layer, which produces the entire network output pattern. The hidden layer neurons detect the input patterns' hidden features. The output layer uses these features to calculate the final output. Figure 2 shows the topology of the developed DFF neural network.  
As illustrated in Figure 2, the developed DFF model is composed of four layers. The input layer accepts the panel's temperature, the solar radiation, and the panel's Short Circuit Current (SCC) and Open Circuit Voltage (OCV) at 1 kW/m\({}^{2}\) and 25 \({}^{\circ}\)C. Each layer of the two hidden layers is composed of five neurons. Each of these neurons uses an activation function, which is the hyperbolic tangent sigmoid transfer function. The output layer consists of two nodes, which calculate the optimum operating voltage and the optimum current that correspond to the inputs at the input layer. Nodes of the output layer use a linear activation function.  
Thus, the actual output of the neurons in the computational layers can be represented as in Equations (1)-(3):  
\[Hf_{j}=\frac{2}{1+e^{-2\left(\sum\limits_{l=1}^{4}x_{i}w_{ij}-\theta_{l} \right)}}-1 \tag{1}\]  
\[Hs_{k}=\frac{2}{1+e^{-2\left(\sum\limits_{l=1}^{5}Hf_{j}w_{jk}-\theta_{k} \right)}}-1 \tag{2}\]  
\[Yo_{l}=\sum\limits_{k=1}^{5}Hs_{k}w_{kl}-\theta_{l} \tag{3}\]  
where \(Hf_{j}\) is the \(j\)th node output in the first hidden layer,\(\theta_{j}\) is the threshold applied to the neuron \(j\), \(w_{ij}\) is attained through the neural network training stage and it represents the weight between \(i\)th node in the input layer and the \(j\)th node in the first hidden layer, \(x_{i}\) is the value of input \(I\), \(Hs_{k}\) is the \(k\)th node output in the second hidden layer, \(\theta_{k}\) is the threshold applied to the neuron \(k\), \(w_{jk}\) is obtained during the training phase of the neural network and it represents the weight between the \(j\)th node in the first hidden layer and \(k\)th node in the second hidden layer, \(Yo_{l}\) is the \(l\)th node output in the output layer, \(\theta_{l}\) is the threshold applied to the neuron \(l\), and \(w_{kl}\) is obtained during the training phase of the neural network and it represents the weight between the \(k\)th node in the second hidden layer and \(l\)th node in the output layer.
## 2 Theoretical and Methodological Considerations
### Empirical Approaches to Modeling the Translation Process  
Eye-tracking technologies allow for the precise observation of how translators read and allocate attention, providing valuable temporal and spatial data. While these tools are highly effective, they still face methodological limitations, particularly in distinguishing between early comprehension and deeper interpretive phases. The boundary between understanding and production can also become blurred in authentic translation settings [31] (p. 129). Nonetheless, the combined use of eye-tracking and keystroke logging has emerged as one of the most efficient methods for investigating cognitive processes in translation.  
Several empirical models have been developed to account for the complexities of translation cognition. [PERSON] [37] proposed that literal translation serves as a default strategy until a problem is detected, which then triggers a shift to conscious decision-making [31] (pp. 407-408). Building on this, [PERSON] et al. [38] suggested that translators initially form provisional representations that mirror the source text's structure and meaning, adjusting these only when contextual or linguistic constraints intervene.  
[PERSON] and [PERSON] [33], drawing on Relevance Theory [39-41], introduced a dual-mode model comprising a stimulus mode (s-mode) and an interpretive mode (i-mode), which are thought to unfold along distinct cognitive timelines [33] (p. 256). [PERSON] [31] expanded on this framework through the Monitor Model, which conceptualizes translation as a process driven by both automatized priming routines and higher-order monitoring strategies. Translation, according to this model, progresses in translation units, with oversight mechanisms regulating the degree of interlingual similarity based on task-specific goals, stylistic norms, or quality expectations [19] (p. 257). Moreover, this Monitor Model emphasizes the interplay between horizontal (within-level) and vertical (cross-level) monitoring processes, as outlined by [PERSON] [27]. This theoretical framework is particularly relevant to our study, as it offers a robust basis for analyzing cognitive effort using eye-tracking and keylogging. It provides a nuanced lens through which to understand the coordination of automatic and controlled processes, especially in relation to variations in cognitive demand across translation directions.