esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 QS Inhibition in the Context of Fish Preservation  
As researchers correlated metabolic activities with the QS mechanism [13; 54], the search for QS inhibiting agents as an alternative approach for fish preservation has been extensively studied [55]. Disrupting the bacterial QS network is a reverse bactericidal strategy that will not exert selection pressure, leading, as with conventional preservatives, to the development of resistance [56]. Moreover, blocking QS renders the bacteria less virulent [9]. In general, QS inhibitors (QSIs) and quorum quenching enzymes (QQ) can be successfully used for food safety control. QS inhibiting agents can target AIs, and QS receptors and interfere with signaling cascades [54]. All of these mechanisms are present in _Pseudomonas_ spp.; a detailed description is given below.
# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 5 Discussion  
The proposed method in this study aims to generate maps from aerial imagery by preserving high accuracy and detailed geographic information, suggesting practical applications across various scientific fields. By examining the key characteristics of this approach and its potential applications, we can explore how this research may contribute not only to map generation but also to other diverse areas.  
The proposed method combines Residual Blocks and Canny edge detection to maximize detail preservation during map generation. Residual Blocks effectively retain essential geographic information at each layer, while the Canny edge detection is applied as a loss function to emphasize detailed features, such as primary terrain boundaries. These features enable the model to excel in generating maps of complex terrains, achieving more precise boundary representation compared to conventional map generation models [6; 7; 16] which often struggle with such detail.  
This method demonstrates substantial potential for applications in geology. Geological mapping plays a crucial role in analyzing structures such as faults, folds, and stratigraphic layers, which are essential for understanding the characteristics and formation processes of specific regions. The proposed approach significantly enhances the generation of high-resolution maps, enabling precise identification of key geological features, including fault lines, layer boundaries, and structural deformations. The method's adaptability to aerial imagery and 3D models further improves the development of detailed Digital Elevation Models (DEMs). For instance, applying these methods to studies like those of [PERSON] et al. [29], UAV imagery captured after catastrophic events such as earthquakes could greatly enhance coseismic fracture mapping, providing higher spatial resolution and detail. Similarly, in monitoring vertical rock faces or identifying fractures and joints within rock masses as in [PERSON] et al. [30], our approach could further refine image quality, improving results and aiding in landslide and rockfall risk mitigation. Furthermore, the  
Figure 3: Examples of map images generated using the proposed method.  
method extends its applications to environmental monitoring, providing valuable tools for tracking natural changes. By enhancing the extraction of precise, detailed data, the proposed approach adds an additional layer of analysis that can improve risk mitigation strategies, significantly reducing the impact of geological hazards and other environmental challenges. The proposed method's strength in detail preservation and accurate terrain boundary representation makes it a versatile tool with potential applications not only in mapping but also in broader fields where high-resolution and precise geographic data are critical.
# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Comparison of Detection Technologies  
Earlier sections have discussed different techniques for detecting, identifying, and localizing UAVs. Each technique's performance varies according to equipment complexity and cost, coverage range and distance, operation efficiency, accuracy and precision measurements, etc. Table 6 summarizes the techniques cited in this study with their main features and affected factors. Combining the different techniques and integrating different sensors can increase the accuracy and reliability of the UAV detection systems, reduce the possibility of errors, and improve the system's ability to adapt.  
Figure 7: RF sensor direction finding reported in [47].
###### Abstract  
During the horizontal freezing construction of a subway tunnel, the delay of the closure of the frozen wall occurs frequently due to the existence of groundwater seepage, which can be directly reflected by a freezing temperature field. Accordingly, the distribution of ground surface frost heaving displacement under seepage conditions will be different from that under hydrostatic conditions. In view of this, this paper uses COMSOL to realize the hydro-thermal coupling in frozen stratum under seepage conditions, then, the frost heaving distribution of seepage stratum in tunnel construction using horizontal freezing technique is researched considering the ice-water phase transition and orthotropic deformation characteristics of frozen-thawed soil by ABAQUS. The results show that the expansion speed of upstream frozen wall is obviously slower than that of the downstream frozen wall, and the freezing temperature field is symmetrical along the seepage direction. In addition, the ground frost heaving displacement field is asymmetrically distributed along the tunnel center line, which is manifested in that the vertical frost heaving displacement of the upstream stratum is less than that of the downstream stratum. The vertical frost heaving displacement of the ground surface decreases with the increase in tunnel buried depth, but the position of the maximum value remains unchanged as the tunnel buried depth increases. The numerical simulation method established in this paper can provide a theoretical basis and design reference for the construction of a subway tunnel in a water-rich stratum under different seepage using the artificial freezing technique.  
Keywords:frost heaving; seepage stratum; horizontal freezing technique; COMSOL; ABAQUS +
Footnote †: journal: Applied Sciences
## 5 Case Study
### Analysis of SESS Capacity Configuration and RIES Operation Cost  
In order to study the influence of different RES uncertainty calculation methods on SESS capacity configuration and the role of the SESS in RIES optimal scheduling, SO is introduced as a comparison algorithm for solving RES uncertainty, and the following four modes are set up.  
Mode 1: Calculation of RES output power using SO without considering the participation of the SESS in the optimized scheduling.  
Mode 2: Calculation of RES output power using SO, and the SESS is considered to participate in optimal scheduling.  
Mode 3: Calculation of RES output power using ROOT-TSE-PCCG without considering the participation of the SESS in the optimized scheduling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Fitness Threshold**} & \multicolumn{2}{c}{**Time Window**} \\ \cline{2-5}  & **40** & **50** & **2** & **6** \\ \hline Jin’s ROOT & 1.54 & 0.69 & 25.95 & 18.64 \\ [PERSON]’s ROOT & 3.03 & 1.72 & 53.58 & 9.17 \\ ROOT-TSE-PCCG & 3.97 & 2.39 & 68.71 & 44.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of average ST and AF of different ROOT algorithms under mMPB.  
Figure 10: The average robustness of different ROOT algorithms with different setting of fitness thresholds and time windows.  
It can be seen from Figure 11 that the SOC curves of the SESS under mode 2 and mode 4 are similar, because whether it is SO or ROOT-TSE-PCCG, the above algorithms calculate the robust output based on the uncertainty of historical data of RESs and do not produce large offset changes. At the same time, due to the different robust solutions obtained by different algorithms, the interaction strategy between each region and the SESS in the 15:00-20:00 time period is different. Combined with Figure 9, it can be seen that the OA mainly charges power into the SESS because it contains abundant RESs, and its own load is small. The RA, IA and CA are mainly powered from the SESS. It is worth noting that due to the large power load demand of the CA in the evening peak period, the CA purchases a large amount of power from the SESS during the 18:00-23:00 period. While meeting its own power load, it reduces the interaction with the EPG to achieve the purpose of reducing operating costs.
## 3 Results and Discussion
### Impact of Acoustic Transmission Characteristics on Acoustic Signal Propagation
#### 3.4.1 Acoustic Ray Bending
The maximum service range on the seafloor gradually decreased as the depth increased, and the ray was refracted upward when the base station was on the seafloor, for which the maximum service range on the seafloor was 0 m and not plotted. In general, the effect of ray bending on the service range can be reduced by lifting the station height. For the seafloor base station, increasing the erection height is costly and laborious, and weakens the stability of the station. Therefore, subsurface buoys can be an alternative station to the seafloor base station to increase the service range. In addition, this study only considered the case of flat seafloor, and the effects of seafloor topographic features, such as steep slopes and sea hills, will be considered in future studies.
## 3 System Architecture  
During the postharvest handling stages, the commodity is located in several places where Wi-Fi access points belonging to logistic companies that commercialize the product are available; thus, the system could be programmed with the lists of SSIDs and passwords to which it would connect in each stage. To solve the problems of lack of connection, the sensor nodes have storage capacity. As mentioned before, the difference between the gateway and the slave sensor nodes lies in the communication tasks they can perform. The gateway node acts as sink and coordinator, managing the information measured by itself and the information sent by the slave nodes. The combined information is transmitted to the servers through the Internet using commercial data communication networks (GPRS, SigFox or LTE). In addition, unlike a traditional wireless sensor network (WSN), the slave nodes can send the measured and recorded information without being connected to the gateway since they comply with the 802.11 (Wi-Fi) standard and this allows them, if properly configured and if Wi-Fi infrastructure is available, to send the information to the servers without a gateway node. In this way, the slave nodes can be located among the commodities, inside the card boxes or pallets, monitoring the conservation variables during the whole supply chain. The two working scenarios of the system are shown in Figure 1. Both scenarios manage multi-point measurement: (a) configuration is used during land transport, where Wi-Fi connection is not available; (b) configuration is used in a logistic warehouse, where Wi-Fi infrastructure is available. The combination of both types of nodes allows for the use of the system in the different situations of commodities handling.  
Figure 1: (a) Two slave nodes are communicating with one gateway node, which provides the cloud access; (b) two slave nodes are sending the measured information to the cloud servers through an existing Wi-Fi infrastructure.
### Functionally Graded Structures
#### 1.2.2 FGM Material Models  
A typical FGM represents a particulate composite with a prescribed distribution of volume fractions of constituent phases. In the case of beams, the material properties are assumed to vary continuously through the beam height. Several models are available in the literature, but the Voigt (or power-law) and Mori-Tanaka schemes [12] have been generally used for the study of FGM structures.  
The advantage of the Voigt scheme is the simplicity of implementation and the ease of computation. According to the Voigt scheme, the effective properties are the arithmetic average of constituent property (\(P\)) and are given by (see, e.g., [11, 13])  
\[P(z)=(P_{1}-P_{2})f(z)+P_{2},\quad f(z)=\left(\frac{1}{2}+\frac{z}{h}\right)^{n} \tag{4}\]
## 2 Materials and Methods
### Groundwater Vulnerability Using DRASTIC Method  
The DRASTIC method was proposed by the EPA (Environmental Protection Agency) in the United States in 1985 and was developed by [PERSON] et al. [15]. The methodology assesses the vertical vulnerability based on seven parameters: depth of water (D), net recharge (R), aquifer media (A), soil media (S), topography (T), the impact of the vadose zone (I) and hydraulic conductivity (C). A weight was assigned to each parameter, the values of which vary between 1 and 5 depending on the degree of influence. The highest weight (5) was attributed to the depth of water and the impact of the vadose zone. The lowest weights (2 and 1) were attributed, respectively, to the soil media and the topography. Each parameter was mapped with an index, called a \"rating\", typically ranging from 1 to 10, where the lowest rate represents the conditions of a lower vulnerability to contamination. The DRASTIC vulnerability index was calculated as follows:  
\[\text{DRASTIC Index}=\text{Dw Dr}+\text{Rw}\;\text{Rr}+\text{Aw}\;\text{Ar}+ \text{Sw}\;\text{Sr}+\text{Tw}\;\text{Tr}+\text{Iw}\;\text{Ir}+\text{Cw}\; \text{Cr} \tag{1}\]  
where: D, R, A, S, T, I and C are the abbreviations of the seven parameters, and \"r\" and \"w\" are the corresponding rating and weights, respectively.  
Table 1 shows the final DRASTIC classifications according to [41], while Table S1 shows the ratings and the weighting values assigned to each parameter in the DRASTIC model. The obtained map presents the relative vulnerability degree of a sector (of the study area). The potential pollution increases in the same direction as the DRASTIC vulnerability index, which represents the evolution of the contamination risk of an aquifer formation.  
Figure 3: Methodological steps adopted for the mapping of groundwater vulnerability in the Tebessa-Morsot alluvial plain.
## 3 Results and Discussion
### Effect of Different Frequencies
#### 3.1.2 X-Direction Specimens  
Similarly, the stress-strain relationship of \(X_{0}^{0}\_i\_,\), \(X_{100}^{0.1}\_i\_,\), \(X_{200}^{0.1}\_i\_,\), \(X_{300}^{0.1}\_i\_,\), \(X_{400}^{0.1}\_i\_,\), \(X_{500}^{0.1}\_i\_,\), \(X_{600}^{0.1}\_i\_,\), \(X_{700}^{0.1}\_i\_,\), \(X_{800}^{0.1}\_i\_,\), and \(X_{900}^{0.1}\_i\) (\(i\) = 1-9) is compared in Figure 6. The influence of the applied vibrations of the same amplitude (0.1 g) but different frequencies (0-900 Hz) on the tensile properties of the X-direction specimens can be determined. It is shown that the tensile properties (tensile strength and plasticity) of the specimens have only been slightly changed with applied vibration, and they are almost unchanged with the increase in frequency. Table 3 shows the details.  
Figure 5: SEM micrographs of specimens’ cross sections. (**a**) Without applied vibration; (**b**) effect of 100 Hz and 0.1 g vibration applied; (**c**) effect of 300 Hz and 0.1 g vibration applied; (**d**) effect of 500 Hz and 0.1 g vibration applied; (**e**) effect of 700 Hz and 0.1 g vibration applied; and (**f**) effect of 900 Hz and 0.1 g vibration applied.  
As can be seen, the average tensile strength of \(X_{0}^{0}\_i\) is 53.85 MPa and the average plasticity is 10.1%; when vibration is introduced, the tensile properties are only slightly changed. For example, the average tensile strength and plasticity of \(X_{100}^{0,1}\_i\) are, respectively, 50.08 MPa and 9.65%, and they are changed by \(-7\%\) and \(-4.46\%\), respectively. In addition, these values of \(X_{900}^{0,1}\_i\) are, respectively, 55.99 MPa and 10.2%, with the growth being 3.97% and 0.99%, respectively. The main reason is that the X-direction specimens' fiber layers are parallel to the applied load during the test and the tensile properties are mainly dependent on the strength of the material fibers themselves. Although applied vibration can significantly improve the specimens' forming quality, it has little effect on the fibers' property. Therefore, the applied vibrations' influence on the tensile properties of X-direction specimens is limited, which can be ignored [19].
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 3 Problem Description  
### Problem Description  
During the autonomous operation of intelligent satellites, observation tasks will arrive at any time, and their sources can mainly be divided into the following three types:  
1. When the iSAT satellite detects natural disasters, such as floods, mudslides, forest fires or ground targets that are covered by clouds, new observation requests are autonomously generated;
2. The iSAT satellite receives cooperative observation requests, sent by other iSAT satellites, such as the joint observation of multiple types of sensors;
3. The iSAT satellite receives a user observation request, uploaded by the ground control center or the ground user terminal.  
The autonomous task planning problem for satellites is essentially a problem of maximizing the satisfaction of customer observation needs.  
According to the needs of different customers, the optimization objectives have multiple manifestations, such as maximizing the observation gain, maximizing the number of observation tasks, maximizing resource utilization, etc. It may also consider multiple optimization objectives at the same time. In this paper, the commonly used maximization observation gain is used as the optimization goal, and the profit is maximized by reasonably selecting the observation task within the specified time range.  
For a single iSAT satellite, we define the interval between two consecutive star-ground communication links as the entire task scheduling range. At the beginning of the scheduling range, the satellite already has several tasks to execute, and there may be new tasks to join the sequence that is to be planned. This paper does not differentiate between the two and treats them in accordance with the tasks to be planned. The total number of tasks in the two groups is N, and the two groups are collectively awaiting task planning and scheduling. The existing tasks and the newly added tasks are shown in Figure 2.
## Appendix A Supplementary Data of Total Chlorophyll and Carotenoid Determination  
During pigment measurement, the extract (\(\sim\)5 mg) was dissolved in the aq: acetone, 90% \(v/v\), The equations provided for the determination of total chlorophyll (a, b and c) and carotenoid content [29; 30] are presented as follows:  
\[\mathrm{c_{a}} =11.85\ \mathrm{Abs}_{664}-1.54\ \mathrm{Abs}_{647}-0.08\ \mathrm{Abs}_{630} \tag{1}\] \[\mathrm{c_{b}} =21.03\ \mathrm{Abs}_{647}-5.43\ \mathrm{Abs}_{664}-2.66\ \mathrm{Abs}_{630}\] (2) \[\mathrm{c_{c}} =24.52\ \mathrm{Abs}_{630}-1.67\ \mathrm{Abs}_{664}-7.60\ \mathrm{Abs}_{647}\] (3) \[\mathrm{c_{CHL}} =\mathrm{c_{a}}+\mathrm{c_{b}}+\mathrm{c_{c}}\] (4) \[\mathrm{c_{CAR}} =7.60\ \mathrm{Abs}_{480}-1.49\ \mathrm{Abs}_{510} \tag{5}\]  
where \(\mathrm{c_{a}}\), \(\mathrm{c_{b}}\), \(\mathrm{c_{c}}\), \(\mathrm{c_{CHL}}\), and \(\mathrm{c_{CAR}}\) stand for the concentration of chlorophyll a, b, c, total chlorophylls and total carotenoids, respectively (\(\mathrm{\mu g}/\mathrm{mL}\)).  
The known extract concentration dissolved in the aq. acetone, 90% \(v/v\), was used for the expression of total chlorophyll (CHL) and carotenoid (CAR) in the mass ratio of the corresponding compound to extract (mg/gextr).  
Moreover, RP-HPLC was performed for the determination of the selected carotenoids of astaxanthin, lutein and \(\beta\)-carotene. The calibration curves of each carotenoid are presented through Equations (1)-(2), followed by its coefficient of variation (R\({}^{2}\)), the limit of detection (LOD) and the limit of quantification (LOQ). Moreover, Equation (2) determines the concentration of the selected carotenoid content. It is noted that the calibration curves are considered applicable for the specific column and HPLC apparatus used, as well as the time period in which the experimental study was conducted.  
\[\mathrm{Abs}_{450} =117,582\ \mathrm{c_{\beta-CAR}}-84,356\] \[\mathrm{R}^{2} =0.9983\] \[\mathrm{LOD} =0.0561\ \mathrm{mg}/\mathrm{L} \tag{6}\] \[\mathrm{LOQ} =0.1700\ \mathrm{mg}/\mathrm{L}\] \[\mathrm{Abs}_{474} =222,356\ \mathrm{c_{ASTX}}-79,159\] \[\mathrm{R}^{2} =0.9997\] (7) \[\mathrm{LOD} =0.0321\ \mathrm{mg}/\mathrm{L}\] \[\mathrm{LOQ} =0.0971\ \mathrm{mg}/\mathrm{L}\] \[\mathrm{Abs}_{446} =302,773\ \mathrm{c_{LUT}}-65,866\] \[\mathrm{R}^{2} =0.9999\] (8) \[\mathrm{LOD} =0.0224\ \mathrm{mg}/\mathrm{L}\] \[\mathrm{LOQ} =0.0678\ \mathrm{mg}/\mathrm{L}\] \[\mathrm{c_{sel.\ CAR}} =\mathrm{c_{\beta-CAR}}+\mathrm{c_{ASTX}}+\mathrm{c_{LUT}} \tag{9}\]  
where \(\mathrm{Abs}_{450}\), \(\mathrm{Abs}_{474}\), and \(\mathrm{Abs}_{447}\) stand for the absorbance value at 450, 474 and 447 nm, respectively, and \(\mathrm{c_{ASTX}}\), \(\mathrm{c_{LUT}}\), \(\mathrm{c_{\beta-CAR}}\), and \(\mathrm{c_{sel.\ CAR}}\) stand for the concentration of astaxanthin, lutein, \(\beta\)-carotene and selected carotenoid content, respectively (mg/L).  
The known extract concentration dissolved in acetone was used for the expression of each carotenoid as well as the selected carotenoid content (sel. CAR) in the mass ratio of the corresponding compound to extract (mg/gextr).
# Distributed Dynamic Load Identification of Beam Structures Using a Bayesian Method
## 2 The Bayesian Formulation of the Distributed Dynamic Load Identification
### The Prior Probability Distribution and the Bayesian Model  
The prior model \(p(\theta)\), which is a measure of the prior knowledge of the experimenter reflecting the no-determinacy relevant to the unknown knowledge \(\theta\), plays an important role in dealing with the inverse problems. In fact, the appropriate prior model, containing almost all the knowledge of the unknown parameters, can accurately solve the statistical inversion problem in the Bayesian framework. Here, the Markov random field is employed as the prior model, while the prior probability distribution is written as Equation (14):  
\[p(\theta)\propto\exp(-\sum_{i\sim j}W_{ij}\phi(\gamma(\theta_{i}-\theta_{j}))) \tag{14}\]  
where \(\gamma,\phi(\gamma(\theta_{i}-\theta_{j}))\) are the amplification coefficient and the non-negative function, respectively. \(W_{ij}\) is a non-negative weight. Assuming \(\phi(t)=1/2t^{2}\), Equation (14) is rewritten as Equation (15):  
\[p(\theta)\propto\lambda^{m/2}\exp(-\frac{1}{2}\lambda\theta^{T}\mathbf{W}\theta) \tag{15}\]  
where \(\theta,W\) are the vector of \(m\times 1\) and the symmetric matrix of \(m\times m\), respectively. The parameter \(\lambda\) is the scaling parameter. Substituting Equations (13) and (15) into Equation (10), the posterior probability is transformed into Equation (16) and the maximum a posteriori probability estimation of \(\theta\) is obtained as Equation (17).  
\[p(\theta|x)\propto(\sigma^{2})^{-\frac{\mu}{2}}\exp(-\frac{\|\mathbf{\hat{x}}( \theta)-\mathbf{x}\|_{2}^{2}}{2\sigma^{2}})\lambda^{\frac{\mu}{2}}\exp(-\frac{1}{2 }\lambda\theta^{T}\mathbf{W}\theta) \tag{16}\]  
\[\hat{\theta}_{map}=\underset{\theta}{\text{argmax}}p(\theta|x)=\underset{ \theta}{\text{argmin}}\Big{\{}\|\mathbf{\hat{x}}(\theta)-\mathbf{x}\|_{2}^{2}+\lambda \sigma^{2}\|\theta\|_{2}^{2}\Big{\}} \tag{17}\]Compared with the Tikhonov regularization, the MAP is equivalent to the Tikhonov minimization functional:  
\[L(\mathbf{\theta})=\|\mathbf{\dot{x}}(\mathbf{\theta})-\mathbf{x}\|_{2}^{2}+\alpha^{2}\|\mathbf{ \theta}\|_{2}^{2} \tag{18}\]  
where \(\alpha^{2}=\lambda\sigma^{2}\). Therefore, the scaling parameter \(\lambda\) and the standard deviation \(\sigma\) assume the crucial role of the regularization parameters, which affect substantially posterior probability density function and the posterior point estimates.