esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Applied Model
### Mathematical Models
#### 2.2.3 Mesh  
When performing the finite element calculations for the acoustic problems, the setting of both the structural mesh and the medium mesh requires extra attention, therefore, the mesh sensitivity is thoroughly analyzed before writing the paper. In general, the maximum grid size should be set to one-sixth of the wavelength [27], with the grid size at the corresponding frequency being directly calculated for the water and air domains based on their respective sound velocities. In accordance with the substrate and the steel plate's transverse wave velocities, the grid size at the corresponding frequency is calculated.  
In this paper, the calculation frequency range is between 100 Hz and 3000 Hz, and the number of grids will not vary significantly with the frequency change. In the simulation process, using a small number of grids will save a significant amount of regridding time, based on the assumption that the grid does not change with the frequency. Consequently, after combining the sensitivity analyses, the maximum grid size of the selected structure is set to 10 mm, while the maximum grid size of the watershed is set to 80 mm. This ensures the accuracy of the model at all of the frequencies and efficiencies.  
As depicted in Figure 4, the acoustic structure of a particular configuration was meshed using the meshing principles mentioned above; the number of meshes in this model was 9466 and the number of nodes was 1987.
## 3 Results and Discussion
### Analysis of Contour Graphs  
Figure 14 shows the contour graph for the variable \(T\) as a function of the variables that show significance. Values that minimize defect formation are obtained by selecting high pressure levels and low spindle feed rates. In this way, angles of less than \(1.2^{\circ}\) are obtained. Also, based on the information obtained in previous sections, it would be more convenient to add intermediate abrasive flows to reduce the angle.  
For surface quality analysis, Figure 15a-c shows the contour graphs for Zone 1, Zone 2, and Zone 3, respectively. In this case, as there are no significant parameters, the graphs are presented as a function of the most influential parameters as a function of the F-value. Thus, in all three zones, results were graphed as a function of TFR and WP with the same scaling factor.  
Figure 14: Taper contour graph as a function of significant parameters: TFR and WP.  
Figure 15: Roughness contour graphs as a function of significant parameters: (**a**) Zone 1: TFR and WP; (**b**) Zone 2: TFR and WP; and (**c**) Zone 3: TFR and WP.  
The results for the three contour graphs show that there is an area where roughness results below 5 \(\upmu\)m are obtained. Specifically, this area comprises the range 100-200 mm/min for TFR and 250-300 MPa for WP. Thus, by selecting low TFR and low and medium WP levels, it is possible to achieve a high surface quality. It should be noted that these parameters must be accompanied by high abrasive flow rates. On the contrary, if high TFR and low WP levels are selected, roughness values above 50 \(\upmu\)m are reported. This shows that TFR is the most influential parameter by zones, as shown in Table 5.
## 2 Results and Discussion
### Greenness Evaluation  
Nowadays, resource sustainability and environmental protection have gained great importance; thus, the evaluation of the green character of an analytical protocol must be taken into account.  
Figure 4: Measured intensities for both analytes for five consecutive extractions on a single \(\upmu\)SPfed cartridge.  
Figure 3: Stacked view of the experimental chromatograms of quantification ions of iphosphonate (**top**), cyclophosphamide (**centre**) and trophosphamide (**bottom**) obtained for the 10 pg/mL calibration level.  
Different tools for assessing the greenness of the analytical procedure are available online [26], so an evaluation of the one which best fits the purpose of the authors has been performed. The oldest one, the National Environmental Methods Index (NEMI) [27] was discarded because it works with chemicals which are reported in official lists, such as EPA TRI list, and antiblastic drugs are not included in it. Moreover, this tool does not consider the consumption of chemical and reagent, and the amount of waste generated. Another interesting approach is the Analytical Eco-Scale [28] which is based on penalty points attributable to each step of the analytical process and then subtracted from a base of 100, but the main drawback is the inability to discriminate between the macro- and microscale of method applications. An additional interesting tool to determine the sustainability of a method is the Analytical Method Greenness Score (AMGS) calculator [29], which is not an absolute measure of the method greenness in that it considers only the environmental impact of the instrumental determination of a sample, while the sample pre-treatment is not taken into account.  
Last, being the most complete tool to the authors' knowledge, the Green Analytical Procedure Index (GAPI) was utilized [25] to highlight that the microextraction procedure developed in this work in addition to being safer for the operators and faster in the sample preparation, also complies with the green chemistry principles. Figure 5 shows the pictograms of the two methods, and it is quite clear how the procedure involving microextraction has a greener character.  
In this approach, each step of the analytical procedure is identified by a pentagram using a colour scale from red to green to indicate high or low environmental impact. As shown in Figure 5, the first pentagram at the bottom left is equal for the two methods because the sample collection and storage are the same for both procedures. While concerning the sample pre-treatment (pentagram at the upper left) and the reagent usage (pentagram at the upper right) we have the main differences, involving the microextraction approach's less amount of sample and reagents, and less sample manipulation by the operator. The pentagram at the bottom right refers to the instrumentation step which differs only in the amount of waste generated and the hazard for the operator.
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 1 Introduction
By considering the complex geological-geophysical mapping relationship of deep strike-slip faults, the structural styles of strike-slip faults and their controlling effects on oil and gas are summarized in this paper through outcrop and physical simulation experiments. To improve the quality of three-dimensional (3D) seismic data, principal components, structure-oriented filtering, and fault-focused imaging were used to control the quality of the seismic data. Based on mathematical theories, the seismic attributes were divided into four categories: energy, curvature, correlation, and gradient. In this study, the amplitude gradient attribute and ant track attribute were selected. The new seismic attribute was generated by the image fusion of grid generations, ellipsoid expansions, and wavelet transforms, which can be used to identify the exact location and inside details of faults. The strike-slip fault structure model was established by profile interpretation and outcrop calibration. The fault characterization results can provide a research example for the geological structure and oil and gas enrichment theory of strike-slip faults in cratonic basins.
## 3 A Technique for Prediction of Branch Power Consumption Change
Therefore, \(dP_{n}\) estimation and consumer meter gain error estimation cannot be solved separately. Using the balance equation gain error and afterward \(dP_{n}\) can be evaluated. Since both quantities are solved in the testing phase, the higher quality of \(dP_{n}\) prediction positively influence precision of gain error estimation. Therefore, \(dP_{n}\) estimation error minimization and lower sensitivity to both meters random and systematic errors remains relevant, though itself it is not the final goal of monitoring. Figure 4: Equivalent circuit of the branch of distribution grid. Assuming that the relative systematic error of consumer meter is \(\gamma_{V}\), the predicted power losses within the DG branch can be expressed as:  
\[d\hat{P}_{w}=\left(V_{s2}-V_{c2}/(1+\gamma_{V})\right)^{2}-\left(V_{s1}-V_{c1}/(1 +\gamma_{V})\right)^{2}\right)/\overline{R_{eq}}. \tag{21}\]  
The predicted PCC of network loads is evaluated using the model (19) derived in training phase and using corrected predictor readings:  
\[d\hat{P}_{nL}=\text{f}\bigg{(}\frac{V_{c2}}{1+\gamma_{V}},\frac{V_{c1}}{1+ \gamma_{V}},\frac{P_{c2}}{1+\gamma_{I}+\gamma_{V}},\frac{P_{c1}}{1+\gamma_{I}+ \gamma_{V}}\bigg{)}. \tag{22}\]  
The predicted quantity of the change of the sum meter PCC in response to a consumer load change can be expressed:  
\[d\hat{P}_{s}=d\hat{P}_{w}+d\hat{P}_{nL}+\frac{P_{c2}}{(1+\gamma_{I}+\gamma_{V} )}-\frac{P_{c1}}{(1+\gamma_{I}+\gamma_{V})}. \tag{23}\]  
To estimate the systematic gain errors of voltage and current \(S_{IV}=(\hat{\gamma_{I}},\hat{\gamma_{V}})\) or their expression in percentage \(S_{IV}=(\hat{\epsilon_{I}},\hat{\epsilon_{V}})\) it is necessary to solve the following minimization problem:  
\[S_{IV}=min\left(\sum_{i=1}^{M}\big{(}d\hat{P}_{s}(i)-\ dP_{s}(i)\big{)}^{2} \right), \tag{24}\]  
where index \(i\) denotes the number of power event, and \(M\) is the total number of power events, \(dP_{s}=P_{s2}-P_{s1}\) is the sum meter measured PCC. The solution of problem (24) is achievable using the least squares minimization solver. The absolute estimation error of the systematic gain error of voltage (current or active power) is defined as  
\[\Delta(e_{V})=\hat{\epsilon}_{V}-e_{V}, \tag{25}\]  
where \(e_{V}\) represents the actual gain error associated with voltage (the same for current, or active power measurements), as established during the synthesis of the test data set. Standard deviation \(\sigma(\overline{\Delta(e_{V})})\) of mean is a typical measure of repeatability of the estimation error. The predicted \(d\hat{P}_{n}\) can be found by substituting systematic error of voltage and current with their estimates correspondingly \(\hat{\gamma_{V}}\) and \(\hat{\gamma_{I}}\) in (21), (22) and the expression  
\[R_{eq}=\frac{dV_{c}-dV_{s}}{dI_{s}},\]0  
The mean value \(\overline{dP_{n}}\) is obtained by averaging predictions \(d\hat{P}_{ni},\ i=\overline{1,M}\) that were obtained using voltage, current and power readings corresponding to _ith_ power event.
## 3 Results and Discussion
### Effect of BZ25 Addition on NK Activity  
As shown in Figure 4, a significant difference was observed in the changes in NK activity (\(p<0.05\)). NK activity gradually increased with the extension of fermentation time. Before 18 h, The NK activity in NMB was higher than that in the NMBB sample. The NK activities in both groups of samples were similar at 18 h. After more than 18 h of fermentation, NMBB showed higher NK activity than NMB. From Figure 1, the number of viable cells of GUTU09 in NMB is much higher than that of NMBB, and it is obvious that NK is produced by GUTU09. That indicates that it is not that the more viable bacteria GUTU09 is, the higher its NK-producing activity is, and other factors, such as BZ25, regulate its NK-producing. There are many cases of two bacteria co-fermentation promoting the production of metabolites. For example, [PERSON] et al. [57] found that the total phenolic and genistein contents were increased by co-fermentation of _Lactobacillus_ with _Bacillus subtilis_. In the later stage of fermentation, BZ25 promoted the production of NK by GUTU09. NK increased from 143.89 FU/g to 151.05 FU/g after 30 h of fermentation. In natto, the addition of BZ25 promoted the accumulation of NK and the increment in the content and quality of active substances of natto.  
Figure 4: Changes in NK activity during single-strain fermentation and mixed-strain fermentation. Means with dissimilar characters (ai–ei, i = 1 or 2) in the same curve indicate significant differences (\(p<0.05\)).
## 4 Node Activity Scheduling
### Justification of Design Choices
#### 4.1.1 Choice of Three-hop _versus_ Two-hop Coloring  
Three-hop coloring requires more colors than two-hop coloring and is more complex. However, it can be the only possible solution regarding the MAC layer choices. In wireless sensor networks, the wireless transmissions are subject to versatile radio propagation conditions and hence are not reliable. That is why, immediate acknowledgement of unicast transmissions is used at the MAC level. In this case, two-hop node coloring is not sufficient as illustrated in Figure 6. Nodes \(A\) and \(D\) have the same color. Node \(C\) immediately acknowledges \(D\) causing a collision on node \(B\). If edge coloring is chosen, links should use the same color in both directions to support immediate acknowledgement. Two links \(AB\) and \(CD\) cannot have the same color if they are incident to the same node or to two one-hop neighbors. In a distributed implementation of edge coloring, a node colors its links. In such a case, a node must know the colors of links of any node up to 3 hops. This leads to a complexity higher than vertex coloring one.  
Figure 6: Collision with two-hop coloring and immediate acknowledgement.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 5 Numerical Analysis  
Most analyses have been done with one type of traffic, which is usually assumed as a Poisson process. In this dynamic spectrum access model, heterogeneous types of traffics and their traffic characteristics are considered together. That is, the performance analysis for the admission control and the spectrum channel allocation should be based on heterogeneous traffic modeling. In particular, we need to consider the traffic characteristics which IoT sensor nodes generate in CR-LPWAN.  
Whenever an IoT sensor node wakes up from a sleep state, it can communicate with the outside world. In general, it depends on the applications for IoT devices in CR-LPWAN to decide when to turn off and on its radio transceiver. For simplicity, however, IoT sensor nodes are assumed to be turned on and off in a random style independent of other IoT devices with certain probability distribution. That is, IoT sensor nodes in CR-LPWAN are assumed to follow an independent random on/off schedule, where an individual IoT sensor node can be modeled as an alternating Markov renewal process with an on process and off process. Under the assumption that the channel requests of IoT sensor nodes will approach infinity, more specifically, the overall IoT sensor nodes can be modeled as Markov process, which can be derived as a special case of Markov renewal processes. Hence, the unlicensed spectrum access in CR-LPWAN can be modeled as a continuous time Markov chain. Without loss of generality, we can model the spectrum access with two types of radio systems illustrated in Figures 3 and 4.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 4 Results
### Experimental Setup  
The model was trained and evaluated using Google Colab, leveraging cloud-based GPU acceleration to improve computational efficiency. The training process was conducted in Python (v3.9) using TensorFlow 2.13.0, with standard data handling and visualization libraries such as NumPy 1.23.5, SciPy 1.11.6, Pandas 2.0.3, and Matplotlib 3.7.1. The use of a free NVIDIA Tesla T4 GPU significantly reduced training time compared to CPU-based implementations, ensuring the approach remains computationally feasible. The cloud-based environment also facilitated collaboration and reproducibility, making the model accessible for further research and practical applications.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 6 Prototyping and Preliminary Experimental Results
We have investigated possible reasons of such effects and concluded that they were caused by scattering of the TX antenna field by the elements of the experimental setup other than the PMS itself. When such parasitic scattered field is added to the field reflected by the PMS, a distorted radiation pattern is observed due to the interference between the two fields. Therefore, it was concluded that a more elaborate experimental setup is needed in order to test more complex beamforming scenarios. In particular, a larger PMS sample may be needed to increase measurement accuracy. Fabrication of such sample and additional measurements are reserved for a future work. For more details on the related experimental and analytical methods, the reader is referred to Refs. [28; 29; 46]. Figure 16: PMS prototype and results of radiation pattern measurements and simulations. (**a**) The PMS installed in a custom 3D-printed plastic holder. (**b**) The experimental setup. (**c**) Radiation patterns in the \(xz\)-plane produced by \(3\times 10\) PMS.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 3 Prediction of RUL Based on the Multi-Feature Fusion Method
### Parallel Multi-Scale Attention Mechanisms  
In most of the parallel attention mechanism structure research, each branch network is the same through the multi-head self-attention mechanism, which is used to adjust the internal relationships within the data. If you use the same feature extraction method for the fusion of the output of each branch network, it will lead to the important features not being highlighted and the redundant features being retained. Inevitably, the performance of the whole network would be impacted. To tackle this issue, this research proposes the development of a novel parallel, multi-scale attention method. The principle of its attention mechanism is shown in Figure 8.  
FNet [25] replaces the self-attention layer with a Fourier sublayer, which speeds up the Transformer's encoder with less loss of accuracy; however, FNet may lose some positional information when processing the sequence data, which will affect the precision of the RUL forecast. As a result, a parallel multi-scale attention mechanism is proposed in this paper, inspired by FNet. In order to increase the speed while reducing the loss of accuracy, the attention mechanism is implemented in conjunction with the FFT of the input features because the Fourier transform is faster than the calculation of the convolution and attention mechanism, which not only accelerates the training speed of the model but also enhances the feature extraction capability and stability. Utilizing self-attention and FNet, the process of encoding sequences concurrently at several sizes. Self-attention and FNet can be combined to learn sequence representations at a greater variety of scales. It consists of three primary components: self-attention, which captures global characteristics in the temporal domain; an FNet module for capturing local deep features in the frequency domain; and a feed-forward network used to capture positional characteristics. The module receives as input the result obtained from the -layer and produces an output representation in the form of a fused manner:  
\[X_{i}=X_{i-1}+Attention(X_{i-1})+FNet(X_{i-1})+Pointwise(X_{i-1}) \tag{17}\]  
where \"_Attention_\" pertains to the mechanism of self-attention, \"_FNet_\" denotes the FNet module, and \"_Pointwise_\" denotes the position feed-forward network. Below are the specifics of FNet and the FNet structure shown in Figure 8.  
Given a sequence \(\{x_{n}\},n\in[0,N-1]\), the discrete Fourier transform (DFT) can be defined as follows:  
\[X(a)=\sum_{n=0}^{N-1}x_{n}e^{-\frac{2\pi i}{N}na},0\leq a\leq N-1 \tag{18}\]  
where \(X(a)\) denotes the data after DFT. From Equation (18), the time complexity of DFT is \(O(N^{2})\). FFT is a fast algorithm for discrete Fourier transform, which recursively obtains the  
Figure 8: The schematic diagram of the parallel multi-scale attention mechanism.  
result by butterfly operation, which reduces the number of DFT multiplications and the time complexity to \(O(N\log N)\).
## 1 Introduction
In some researches, green FPAR is defined as the product of the total FPAR and the fraction of leaf area in the canopy [2; 18]. That approach is based on the idea that the green leaves and NPV have the same spectral characteristics. For forest canopies, NPV is significantly different from green leaves, which makes the approach invalid. Fortunately, green FPAR can be calculated from radiative transfer models describing the transfer of solar radiation in plant canopies [25; 26]. Physics-based radiative transfer models represent the scattering and absorption of radiation by scattering elements of canopies. These models are helpful for scientists to understand how radiations interact with the environment, and how they propagate towards the sensor. But such an approach calculating FPAR through radiative transfer models is time consuming [20]. Vegetation indices play key roles in FPAR inversion based on remotely sensed data. Live green plants strongly absorb solar radiation in red spectral region, and scatter (reflect and transmit) solar radiation in the near-infrared spectral region. Vegetation indices derived from radiometric measurements in red and near-infrared wavelengths can reflect absorption and reflection of vegetation to solar radiation. Consequently, some vegetation indices (e.g., NDVI, EVI) are related to FPAR, and are frequently used to estimated FPAR due to its ease of use and simplicity [13; 26; 27; 28; 29]. Relationships between vegetation indices and FPARPAV need be assessed for forest ecosystems in order to inverse the green FPARPAV. In this study, the scattering by arbitrary inclined leaves model (SAIL model) was used to partition the canopy FPAR. The aims of our study are twofold, (I) to simulate and analyze the variation of green FPAR with the canopy structure; (II) to explore the relations between the green FPAR and vegetation indices for estimation of green FPAR.