esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers
###### Abstract  
Coal-bearing strata belong to sedimentary strata, and there are multiple aquifers. The accurate detection of deep aquifers is helpful to the safe mining of the working face. In order to provide guidance for the interpretation of the surface-to-underground transient electromagnetic method (SUTEM) that can be used to detect deep aquifers, we used theoretical analysis and numerical simulation methods in this study. Taking uniform half-spaces, single aquifers, and double aquifers as examples, we systematically studied the data characteristics and degree of influence of SUTEM under the influence of shallow aquifers. The results indicate the following: Under the influence of the primary field distribution, the \(x\) or \(y\) component of the induced electromotive force received by the underground receiving point has a positive and negative inflection point, which increases the difficulty of data interpretation, and the \(z\) component is easier to use for data interpretation. The influence of the aquifer on the early data of the underground receiving point is much greater than that of the ground receiving point, and the late influence is closer to the ground receiving point. The change in resistivity of the shallow aquifer has the greatest influence on the ability of each measuring point to detect the data of the deep aquifer; this influence is followed by change in thickness, and change in depth has the least influence on the detection capability of each measuring point.  
surface-to-underground transient electromagnetic method (SUTEM); coal mine water control; forward modeling; multilayered aquifer
## 5 Conclusions  
1. The failure of the sidewall of the non-fissured sandstone test can be divided into four stages: the calm stage, the spalling stage of fine rock particles, the generation and expansion stage of the sidewall cracks, and the buckling deformation and failure stage of the sidewall rock chips. As fissure penetration increased, the initial failure location of the tunnel sidewall shifted toward the fissured side, and the number of rock slabs resulting from slab cracking decreased.
2. As fissure penetration increased, the initial failure stress \(\sigma_{zi}\) first rose and then decreased, while the peak failure stress \(\sigma_{xp}\) consistently decreased, indicating that fissures weakened the surrounding rock's strength. This also made it more difficult for the surrounding rock to experience initial failure under medium fissure penetration.
3. The presence of fractures altered the particle size distribution of rock fragments. As fissure penetration increased, the number of larger rock blocks decreased, and the distribution became more block-like. Additionally, fracture penetration significantly impacted the fractal dimension of the rock, with lower penetration resulting in a higher fractal dimension in the rock fragments. When fissure penetration is high, it is necessary to reinforce support and control excavation boundaries to reduce stress concentration.  
Conceptualization, J.Z. and S.L.; methodology, J.Z.; software, M.J.; validation, M.J., S.L. and K.L.; formal analysis, J.Z.; investigation, J.Z.; resources, K.P.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, K.P.; visualization, K.L.; supervision, K.P.; project administration, K.P.; funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number No. 52474120, the National Key Research and Development Program of China--2023 Key Special Project grant number No. 2023 YC2907400, Science and Technology Innovation Program of Hunan Province grant number No. 2022 RC1173. And the APC was funded by the National Natural Science Foundation of China.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 2 Correlation Theory
### Stokes Vector  
The Stokes vector is a fundamental tool for describing the polarization state of light, comprehensively characterizing its polarization properties. In natural environments, polarized light is predominantly linearly polarized, with circular polarization components typically negligible. Therefore, by capturing four images \((I(0),I(45),I(90),I(135))\) at specific polarization angles (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\)) using a polarimetric camera or rotating polarizer, we can compute the Stokes vector components as follows:  
\[S =(I(0)+I(45)+I(90)+I(135))/2 \tag{3}\] \[S_{1} =I(0)-I(90)\] \[S_{2} =I(45)-I(135)\]  
In the Equation (3), \(S\) represents the total light intensity, and \(S_{1}\) represents the intensity difference between the \(0^{\circ}\) and \(90^{\circ}\) directions; \(S_{2}\) is the intensity difference between the \(45^{\circ}\) direction and the \(135^{\circ}\) direction.  
According to [PERSON]'s theorem, the light intensity values for different polarization directions are given by Equation (4).  
\[I_{0} =\frac{1}{2}S(1-p)+Sp\text{cos}^{2}(\theta-0^{\circ})\] \[I_{45} =\frac{1}{2}S(1-p)+Sp\text{cos}^{2}(\theta-45^{\circ})\] \[I_{90} =\frac{1}{2}S(1-p)+Sp\text{cos}^{2}(\theta-90^{\circ})\] \[I_{135} =\frac{1}{2}S(1-p)+Sp\text{cos}^{2}(\theta-135^{\circ}) \tag{4}\]  
Figure 1: Atmospheric scattering model diagram.  
Here, \(p\) is the degree of polarization (DoP) and \(\theta\) is the angle of polarization (AoP). Using [PERSON]'s theorem and the Stokes vector, the degree of polarization and polarization angle in the detected light can be determined, as shown in Equations (5) and (6).  
\[p=\frac{\sqrt{{S_{1}}^{2}+{S_{2}}^{2}}}{S}\quad(0<p<1) \tag{5}\]  
\[\theta= \frac{1}{2}\arctan\frac{S_{2}}{S_{1}} \tag{6}\] \[(S2>0,S1>0)\quad\theta\in(0,\frac{\pi}{4})\] \[(S2>0,S1<0)\quad\theta\in(\frac{\pi}{4},\frac{\pi}{2})\] \[(S2<0,S1<0)\quad\theta\in(\frac{\pi}{2},\frac{3\pi}{4})\] \[(S2<0,S1>0)\quad\theta\in(\frac{3\pi}{4},\pi)\]
## 1 Introduction
[42] used capital recovery factor (CRF) as the indicator to conduct an exergoeconomic comparison of TLC (trilateral Rankine cycle), ORC and Kalina cycle using a low-grade heat source. They found that, although the TLC can achieve a higher net output power compared with ORC and Kalina (KCS11 (Kalina cycle system 11)) systems, its product cost is greatly affected by the expander isentropic efficiency. They also observed that, for both ORC and Kalina systems, the optimum operating condition for maximum net output power differs from that for minimum product cost. [PERSON] et al. [43] compared economic performance of ORC processes at different scales using purchased equipment costs (PEC), maintenance and operating costs (MO), and capital investment cost (CIC) as the indicators. [PERSON] et al. [44] used the annual cost method to make a technoeconomic comparison of ground-coupled and air-coupled heat pump system for space cooling. Purchase cost and payback period [45] were used by [PERSON] and [PERSON] as the indicators to conduct a techno-economic evaluation of waste heat recovery by organic Rankine cycle using pure light hydrocarbons and their mixtures as working fluid in a crude oil refinery. [PERSON] et al. [46] used total capital investment as the indicator to make a thermo-economic analysis and optimization of a zootropic fluid organic Rankine cycle with liquid-vapor separation during condensation. Based on the reviewed papers, it can be seen that different indicators are used to evaluate the economic performance of different thermodynamic systems. However, many indicators are proposed for a particular process or system or, in most work, only one or two indicators are used. Therefore, in this work, five traditional and classical indicators, namely total cost, net earnings, payback period, return on investment, and levelized energy cost, were selected for thermo-economic analysis of ORC using R123 and R245 fa as working fluid. Considering that the above five indicators mainly focus on the present economic performance evaluation, a new indicator, which is called present value of total profit in system service life, is proposed. This new indicator can be used to evaluate the economic performance of engineering technology solution throughout its service life. Using these six indicators, the thermo-economic evaluation model of ORC with R123 and R245 fa was established and the variation effect of influencing factor on system economic performance was calculated and analyzed. Herein, the economic performance of an ORC demonstration project is reported.
## 2 System Model
### Network Model  
The algorithms proposed in this paper not only need to adapt to the dynamic network with changing network topology, but also need to obtain better network performance and lower average end-to-end network delay. Therefore, it is necessary to comprehensively study the network topology as well as the network characteristics of links and nodes. However, GraphSAGE can only deal with the network topology and the network characteristics of nodes and cannot analyze the network characteristics of links. To solve this problem, the graph structure is reconstructed by aggregating link information into nodes.  
The specific implementation of this is shown in Algorithm 1 (a reconfiguration map of data-specific processes). The input is the network topology information, which is represented by an undirected graph \(G=(\mathrm{V},\mathrm{E})\), where \(\mathrm{V}=\left\{v_{i}\right\}_{i=1:\mathrm{N}}\), \(\mathrm{E}=\left\{\left(e_{k},r_{k},s_{k}\right)\right\}_{k=1:L}\) denote the set of edges and the set of nodes, respectively. The variable \(e_{k}\) on each edge is a one-dimensional vector containing information such as link capacity and delay. The output is a new graph structure \(G\big{(}\mathrm{V}^{\prime},\mathrm{E}^{\prime}\big{)}\) with the features of the edges related to this node aggregated in \(v_{i}^{\prime}\).  
```
1:Initializing the network \(G=(\mathrm{V},\mathrm{E})\)
2:for\(k\in\{1\cdots L\}\)do
3:\(\sigma_{k}^{\prime}\leftarrow\varnothing^{\varepsilon}(e_{k},r_{k},s_{k})\)//Update Edge Properties
4:endfor
5:for\(i\in\{1\cdots N\}\)do
6:\(\mathrm{E}_{i}=\left\{\left(\left(e_{k}^{\prime},r^{i},s^{k}\right)\right)_{j =i,j=1:L}\)//Find edges adjacent to node \(i\)
7:\(\bar{z}_{i}\leftarrow\rho(E_{i})\)//Aggregate edge attributes per node
8:\(v_{i}^{\prime}\leftarrow\varnothing^{\varepsilon}(\bar{e}_{i},v_{i})\)//Update node attributes
9:endfor
10:return\(G(V^{\prime},E^{\prime})\)
```  
**Algorithm 1** Reconfiguring Graph Data  
The network characteristics of each node are represented by a feature vector \(x_{i}(i=0,1\cdots N-1)\), \(x_{i}=\left[q_{i},v_{i}^{\prime}\right]\), where \(q_{i}\) denotes the traffic flow. \(v_{i}^{\prime}\) indicates the aggre  
Figure 1: Algorithm diagram based on GNN-DRL.  
gated characteristics of the edges related to this node, including information such as link capacity and delay.  
Then the network feature matrix \(X\) of the whole network topology can be expressed as follows:  
\[X=\begin{bmatrix}x_{0}\\ x_{1}\\ \vdots\\ x_{N-1}\end{bmatrix} \tag{1}\]
# Maximum Safe Parameters of Ships in Complex Systems of Port Waterways
## 4 Discussion  
The main objective of the research presented in the article is to develop a general method for determining the maximum parameters of ships in complex port water systems. This goal was achieved by developing a computational procedure (algorithm) that takes into account various constraints specific to individual sections of the fairway. The developed method stands out from the methods known in the literature (PIANC, ROM, Japanese, CIRM), which focus on determining the maximum parameters of a ship on individual types of waterways, but do not include the procedure for their use for complex waterways.  
The developed method was used to determine the conditions for the safe operation of ships on the modernized Szczecin-Swinoujscie fairway. The obtained results showed that the deepening and widening of the waterway will, obviously, allow for the passage of larger ships. However, the increase in ship dimensions relates more to the draft and length of the ship than to its breadth. For example, for the port of Szczecin, the maximum vessel draft increased from 9.15 m to 11.0 m and the length from 215 m to 260 m, and the width of the maximum vessel only increased from 31 m to 33 m. This is due to the fact that the horizontal dimensions of the safe maneuvering area depend not only on the width of the vessel, but also on its length, the draft ratio and the available depth.  
The proposed method does not introduce new dependencies allowing for the determination of safe ship parameters, but, thanks to the systematization of calculations, it allows for the determination of these parameters on complex waterways. The limitations of its use result from the limitations of the detailed methods used; however, due to the possibility of using various methods (both empirical and simulation), it can be treated as a universal method allowing for the determination of safe parameters of ships on complex waterways. In the future, work on the development of this method will focus on the partial automation of calculations, which will allow us to shorten the time necessary to obtain results.  
In conclusion, the developed method allows us to determine the maximum safe parameters of ships in complex port waters, which has been practically confirmed by defining the conditions for the safe operation of ships on the modernized Szczecin-Swinoujscie fairway.
applied sciences  
Article  
Influence of Non-Thermal Plasma on the Quality and Nutritional Content of Palm Dates  
[PERSON] 1,2,*(c), [PERSON] 1(c), [PERSON] 1(c), [PERSON] 1,3(c), [PERSON] 4, [PERSON] 4 and [PERSON] 4,5(c)  
1 Department of Biology, Faculty of Science, Branch of Tayma, University of Tabuk, Tabuk 71491, Saudi Arabia 12 King Marriott Higher Institute of Engineering & Technology, Alexandria 23713, Egypt 2  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
of bioactive components is dependent on non-thermal plasma parameters and the food matrix [13]. Since it is performed at low temperatures, non-thermal plasma may maintain bioactive components, thus overcoming the effect of heat on food quality [12].  
Non-thermal plasma technology has played a crucial role in reducing oxygen and increasing carbon dioxide concentrations in the gaseous environment when treating fresh apples, resulting in modifications to the respiratory pathway [14]. This alteration inactivates the indigenous enzymes of fruits and vegetables, resulting in a slowed maturation rate [15].  
Palm date is one of the most consumed fruits in the Middle East, particularly in Saudi Arabia, due to its high nutritional content and delicious flavor. Palm dates have a variety of bioactive compounds, including antimutagenic, antioxidants, carotenoids, flavonoids, and phenolics [16]. Unfortunately, palm dates are susceptible to several funguses in the field, during transit, and during marketing. Moreover, palm dates are susceptible to several diseases and pests that degrade this strategic crop. In many instances, most dates are consumed without treatment to reduce microbial contamination. _A. niger_ is the most common form of fungus that infects dates in Saudi Arabia [17]. The contamination of dates with _A. niger_ is a major concern owing to the creation of poisonous chemicals that spread throughout the fruit's flesh, creating severe health issues for humans. One of the most significant sources of mycotoxins is black fungus [18].  
Different techniques, including ozone, UV, gamma radiation, and antifungal drugs, have been used to suppress fungus and limit the development of mycotoxins in products [17]. Although these procedures may avoid surface contamination, they have unfavorable side effects, such as lowering product quality and altering nutrients, which lowers customer demand for foods treated with these methods [18]. In addition, these techniques are considered impractical due to their high cost or slow speed [19].  
In this study, surface dielectric barrier discharges (SDBD) were used to inhibit the growth of black fungi (_A. niger_). In addition, _A. niger_ has been extracted from some palm date cultivars. The impact of SDBD plasma on the bioactive material, chemical content, and hydroxymethylfurfural (HMF) levels of some palm date cultivars has been evaluated. According to our knowledge, no previous studies have examined the impact of non-thermal plasma on the chemical composition of palm date fruits.
# High-Frequency and Spectrum-Clean Shear-Horizontal Acoustic Wave Resonators with AlN Overlay
## 3 Propagation Characteristics of SH Wave in LiNbO\({}_{3}\)/Si
### Cut Angle  
As the propagation loss is no longer the main concern and dictates the cut-angle selection for the nonleaky SH wave in bonded wafers, the optimal cut angle can be chosen to optimize \(v_{\rm p}\) and \(k_{\rm eff}\)\({}^{2}\). Figure 2a,b depicts the open-surface phase velocities \(v_{\rm p,o}\) and \(k_{\rm eff}\)\({}^{2}\) for LiNbO\({}_{3}\)/Si across all rotation angles \(\theta\) from the \(Y\)X-LiNbO\({}_{3}\) with varied thicknesses of LiNbO\({}_{3}\) for both the SH main mode and the Rayleigh spurious mode.  
Figure 2: Simulated (**a**) open-surface phase velocities \(v_{\rm p,o}\) and (**b**) effective coupling coefficient \(k_{\rm eff}\)\({}^{2}\) for LiNbO\({}_{3}\)/Si across all rotation angles from the \(Y\)X-LiNbO\({}_{3}\) with varied thicknesses of LiNbO\({}_{3}\) for the SH main mode and Rayleigh spurious mode.  
The impact of the rotation angle on the propagation characteristics of SH mode in LiNbO\({}_{3}\)/Si layered substrate is similar to on the LiNbO\({}_{3}\) substrate [20], as depicted in Figure 2a. Fortunately, a low rotation angle from _YX_-LiNbO\({}_{3}\) enables high \(v_{\rm p,o}\), large \(k_{\rm eff}\)2 for the SH main mode, and low \(k_{\rm eff}\)2 for Rayleigh mode simultaneously. Therefore, the design range of \(\theta\) is 10-40\({}^{\circ}\) for the LiNbO\({}_{3}\)/Si-based nonleaky SH wave devices for the high frequency, wide band, and clean spectrum, respectively.  
In Figure 2b, it can also be noted that \(h_{\rm LiNbO3}\) = 0.25\(\lambda\) enables higher \(v_{\rm p,o}\) than \(h_{\rm LiNbO3}\) = 0.5\(\lambda\) of LiNbO\({}_{3}\)/Si or LiNbO\({}_{3}\) substrate, and the optimized rotation angle also shifts up a bit for the optimum \(k_{\rm eff}\)2. These indicate weak dispersion in the layered substrate due to the sub-wavelength-thick piezo thin film.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 2 Wildfire Management Needs Assessment  
Wildfire management agencies generally employ a risk-based approach where the potential impact(s), likelihood, and resulting expected loss or benefit are assessed at the appropriate scale according to the complexity of the wildfire situation [26; 52; 53]. Decisions often involve multiple decision-makers and stakeholders with varying perspectives concerning risk [23; 26]. Decisions are not static and are frequently updated through an iterative process of determining and taking actions, monitoring outcomes, and revising actions until the situation is resolved [53]. When assessing progress in the decision-making cycle, decision-makers require different types of intelligence.  
We define wildfire intelligence as information which is collected to support wildfire management activities. This may include current or forecasted information such as: Wildfire behavior, location, size, shape, spatial context (e.g., fuels, topography, proximity to areas of concern, etc.), firefighting resource allocation and use, and wildfire effects and impacts (e.g., social, economic). The type, precision, accuracy, and timeliness of intelligence required varies depending on whether tactical or strategic planning is being conducted.
## 2 Materials and Methods
### Evaluation Index  
In this study, precision, recall, and mean average precision (mAP) are employed to evaluate the model's performance. Precision is defined as the ratio of correctly predicted positive samples to the total number of samples predicted as positive. Recall refers to the ratio of correctly predicted positive samples to the total number of actual positive samples. The mean average precision (mAP) is calculated as the average of the average precision (AP) across all categories, providing a comprehensive measure of the model's overall detection accuracy. The corresponding formulas for these evaluation metrics are presented in Equations (7)-(10).  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
\[\text{AP}=\int_{0}^{1}\text{P(r)dr} \tag{9}\]  
\[\text{mAP}=\frac{\sum_{\text{I}=1}^{n}\text{AP}_{\text{I}}}{\text{n}} \tag{10}\]  
Here, TP (true positive) denotes the number of sugarcane instances correctly detected by the model, FP (false positive) refers to the number of non-sugarcane instances incorrectly identified as sugarcane, and FN (false negative) indicates the number of sugarcane that the model failed to detect. The average precision (AP) represents an approximation of the area under the precision-recall (PR) curve and serves as a summary measure of the model's performance in detecting a specific category. The mAP is computed as the mean AP across all n categories. mAP\({}_{50}\) refers to the mean average precision calculated at an IoU threshold of 0.5, primarily evaluating the overlap between predicted and ground truth bounding boxes. In contrast, mAP\({}_{50-95}\) is computed over multiple IoU thresholds ranging from 0.5 to 0.95 in increments of 0.05. This metric imposes stricter evaluation criteria, requiring not only sufficient overlap but also high spatial consistency between the predicted and ground truth boxes in terms of location and shape.