esa-sceva/satcom-chunk-collection huggingface dataset:
# Phtyoremediation and Bioremediation of Pesticide-Contaminated Soil
## 7 Remediation of Pesticides
### In Situ Bioremediation and Phtoremediation
#### 7.3.3 Soil Amendments  
Many beneficial amendments have been added to pesticide-contaminated soil that have increased biodegradation rates and extent while also reducing toxicity. Manure, biosolids, and compost provide nutrients and organisms with enzymes that may be beneficial for biodegradation in vegetated soil [189]. Nutrients and carbon and energy sources for microbial populations should be included in the plan. Bioaugmentation with microbial populations that have enzymes to degrade the pesticide of interest is helpful [277]. Arbuscular mycorrhizal fungi have been reported to be beneficial in phytoremediation because they have beneficial enzymes, and they stimulate microbial activity in the rhizosphere [12; 85]. Substances that are not toxic and enhance solubility in the aqueous phase are beneficial because of the low water solubility of many pesticides. Since one goal of phytoremediation is to have land that can be used for its intended purposes, amendments that improve the land should be given a higher priority [29; 59; 100]. The pH that is desired for the phytoremediation and future use of the land should be included in developing the amendment plan.  
The addition of enzymes with the capability to serve as catalysts for biodegradation of the pesticide of interest may be beneficial [87; 111; 201; 227]. It is important for the pesticide and enzyme to find each other in the multiphase mix that includes soil, plant matter, and amendments. The water phase is a good place for this to happen if the pesticide has significant water solubility.  
Water is important for phytoremediation and for agricultural productivity. In climates where low rainfall may be limiting productivity, soil amendments that improve the water-holding capacity for phytoremediation and future land use are expected to be beneficial. Adding organic carbon and/or biochar may improve water holding capacity, microbial numbers and microbial diversity [10; 15; 20; 23; 27; 28; 29; 37; 59; 97; 100; 128; 130; 225; 226; 251]. Adsorption to the carbon should be expected, which makes the pesticides less available [35; 217]. The population numbers and diversity of organismspresent may be used to evaluate the quality of the soil [59,100]. Activated carbon additions to soil enhance adsorption and increase the fraction of a pesticide that is adsorbed. This reduces pesticide bioavailability [27,35,37,43,64,76,89,166,178,183,211,217,224,233,237,249,265,266,267,268], which is beneficial when there are pesticide residues from past applications. The addition of biochar and/or activated carbon may reduce nutrient and trace metal availability [166,266].
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 3 Overview of Drones
### Drone Architecture
#### 3.1.2 Datalink  
The datalink is the wireless connection between UAV and GCS that carries control and data signals. The UAV's operating range determines the communication link chosen. UAV operations are divided into Line-Of-Sight (LOS) missions, in which control signals are sent and received via direct radio waves, and Beyond Line-Of-Sight (BLOS) missions, in which the drone is controlled via satellite systems or a relaying aircraft, which could be a drone itself, based on their distance from the GCS.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Network Security Design
#### 4.3.1 Subnets and Segmentation  
Network segmentation is a fundamental security principle that minimizes lateral movement in the event of a breach. In this architecture, the Virtual Network (VNet) is defined with multiple subnets, detailed as follows, to separate workloads logically:  
* Application subnet, which hosts containerized workloads (Azure Container Apps);
* Data subnet, which is reserved for stateful services such as PostgreSQL Flexible Server;
* Private Endpoint subnet, which is dedicated to Private Endpoints.  
This separation enables granular control over network security rules and supports future enhancements, such as Network Security Groups (NSGs) and route tables. By isolating the app layer from the data layer, potential vulnerabilities in one component do not automatically compromise other components.
# Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography
## 2 Principle and Methods
### Generation and Recovery of OAM Modes
The contributions of \(\mathbf{r}\) dependent phase factors \(exp\left(i\frac{k|\hat{\mathbf{r}}_{1}|^{2}}{2}\right)\) and \(exp\left(ik\frac{|\mathbf{r}+\Delta\hat{\mathbf{r}}_{2}^{\perp}}{2z}\right)\) were not represented in Equation (5) as these terms were canceled out while estimating the fourth order correlation using Equation (4). For a fixed wavelength \(\lambda\) and at a fixed propagation distance \(z\), by considering \(\hat{\mathbf{r}}_{2}=\hat{\mathbf{r}}_{1}=\hat{\mathbf{r}}\) the recovered complex cross-correlation function is transformed to,  
\[W(\Delta\mathbf{r})\propto\int I_{0}(\hat{\mathbf{r}})V_{0}^{al}(\hat{\mathbf{r}})\ exp \left[-i\frac{2\pi}{\lambda z}\Delta\mathbf{r}\cdot\hat{\mathbf{r}}\right]d\hat{\mathbf{r}} \tag{6}\]  
where \(I_{0}(\hat{\mathbf{r}})=\psi_{0}^{*}(\hat{\mathbf{r}})\psi_{0}(\hat{\mathbf{r}})\) represents the non-stochastic intensity distribution at the diffuser plane and this appears in Equation (6) without a trace of the random phase \(\varphi_{g}(\hat{\mathbf{r}})\). Equation (6) indicates that the correlation function \(W(\Delta\mathbf{r})\) is directly related to the \(I_{0}(\hat{\mathbf{r}})\) and the encoded vortex phase information \(V_{0}^{el}(\hat{\mathbf{r}})\) at the exit plane of the diffuser. Additionally, the reference correlation function \(W^{R}(\Delta\mathbf{r})\) is considered as, (see Supplementary Materials)  
\[W^{R}(\Delta\mathbf{r})\propto\int I_{R}(\hat{\mathbf{r}})\ exp\left[-i\frac{2\pi}{ \lambda z}\Delta\mathbf{r}\cdot\hat{\mathbf{r}}\right]d\hat{\mathbf{r}} \tag{7}\]  
where \(I_{r}(\hat{\mathbf{r}})=circ\left(\frac{k-\mathbf{r}_{g}}{a}\right)\) is utilized in the proposed approach for the generation of \(W^{R}(\Delta\mathbf{r})\) i.e., the intensity of the reference field at off-axis position \(\prime\mathbf{r}_{g}\) and size \(ta^{\prime}\) of the circular aperture. The reference random field generated by such source creates a constant reference correlation function \(W^{R}(\Delta\mathbf{r})\) with a linear phase to record the correlation hologram. The complex correlation function \(W(\Delta\mathbf{r})\) is recovered from other redundant terms in Equation (4) by utilizing a Fourier domain filtering approach to the retrieved correlation hologram [38]. Therefore, the recovery of the complex correlation function offers a new method to directly detect OAM modes scrambled in the speckle pattern as explained by Equation (6). The retrieval of phase distribution along with the amplitude of the vortex modes delivers a direct scheme for the simultaneous determination of sign and magnitude of the topological charge of specific helicity.
## 7 Routing Protocols proposed by Spanish Universities
### Energy Aware Routing Protocols for Underwater Sensor Networks  
Underwater wireless sensor networks consist of a certain number of sensors and vehicles that interact to collect data and perform collaborative tasks. Designing energy-efficient routing protocols for this type of networks is essential and challenging because sensor nodes are powered by batteries, which are difficult to replace or recharge, and because underwater communications are severely affected by network dynamics, large propagation delays and high error probability of acoustic channels. In [31] the authors analyze the total energy consumption in underwater acoustic sensor networks considering two different scenarios: shallow water and deep water. Specifically, the direct transmission, the relaying scheme and the clustering structure are compared. They conclude that the worst performance is obtained by the direct communications. For shallow water, the clustering scheme is the best option in terms of network overhead, the end-to-end delay and overhead. Additionally, its scalability in terms of number of nodes and distance is demonstrated.
## 3 Results  
This section examines the concrete responses to the situations realized in the scenarios determined within the scope of the study. It is also desirable to make certain comparisons and inferences thanks to the numerical data obtained as a result of the simulations. In the first simulation, the effectiveness of these algorithms is compared according to the data obtained by solving different optimization algorithms developed for TSP of \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**(a) Parameters for Numerical Example**} \\ \hline Parameter & Numerical value \\ \hline N: Number of nodes (a, b, c, d, e, f) & 6 \\ \hline D: Number of drones & 2 \\ \hline C: Number of customers & 3 \\ \hline t: The amount of time a drone can stay at & 4 min \\ the node for deployment & \\ \hline b: Battery capacity of the drones & 1.8 kWh \\ \hline w: Weight capacities of drones & 5 kg and 10 kg \\ \hline \multicolumn{3}{c}{**(b) Numerical results**} \\ \hline  & Route & Arrival time (minutes) & Leaving time (minutes) \\ \hline Truck & a–c–a & 99 (for a)–11 (for c) & 0–88 \\ \hline Drone 1 & c–e–c & 11 (for c)–42 (for e) & 88–45 \\ \hline Drone 2 & c–d–f–c & 11 (for c)–23 (for d)–60 (for f) & 76–26–63 \\ \hline \end{tabular}
\end{table} 
Table 2: **(a) Parameter values used in numerical example, (b) Numerical results for solving a delivery scenario. **  
Figure 5: Schematic representation of the numerical solution of the delivery scenario. Figure 6: The graphical results for delivery time for increasing the specifications of drones. different routes and the GA proposed in the method. In the first column of Table 3, there are six different test routes taken from the TSPLIB library [37]. After the names of the test routes, the optimum result for the relevant route is given in parentheses [38]. These routes include a minimum of 30 and a maximum of 100 cities. Parameters calculated in the second column; Particle Swarm Optimization (PSO) in the third column; Gray Wolf Optimizer (GWO) in the fourth column; and in the fifth column, there are data obtained as a result of solving the TSP of the current route with the GA, which is the method proposed in this study. While filling out the table, the current problem has been solved with the given algorithm 10 times, and as a result, the best result, worst result, average result, average algorithm run time, and error rate values have been entered into the table. The calculation of the error rate is seen in Equation (9) [38]. \[Error\ Rate=\frac{\frac{\text{Best Result}+\text{Worst Result}}{2}-Optimal\ Value}{Optimal\ Value} \tag{9}\]  
Considering the values given in the table, Figures 7 and 8 for the average result are examined, and it is seen that the average result parameter obtained by GA is close to the optimum value. In PSO and GWO algorithms, values close to each other are obtained, but the success of these values is worse than in GA. On the other hand, as seen in Figure 9, a lower error rate is obtained with GA compared to other methods. When the algorithms are
# Study on the Wellbore Flow for Carbon Dioxide Fracturing
## 5 Conclusions  
A coupled model is proposed to predict the flow field of CO\({}_{2}\) fracturing in this paper. The model is based on compressible fluid flow equations and solved by coupling temperature and pressure with an iterative method. According to the calculated results, wellbore pressure, temperature, CO\({}_{2}\) properties, and phase state along depth are gained and a sensitivity study is conducted to analyze the controlling factors for CO\({}_{2}\) fracturing. The conclusions are as follows:  
(1) The bottom hole pressure increases first and then decreases, and the bottom hole temperature decreases with an increase in displacement. The bottom hole temperature tends to keep a lower level under sustained high displacement, which has been verified in field tests.  
(2) Both the pressure and temperature are highly coupled with physical properties of CO\({}_{2}\). CO\({}_{2}\) turns from liquid state into supercritical state under low displacement, while it tends to maintain liquid state in the whole wellbore under high displacement.  
(3) Under the temperature-pressure condition of CO\({}_{2}\) fracturing, the injection temperature has a greater impact on the wellbore flow behavior of CO\({}_{2}\) compared to the injection pressure, whereas excess injection temperature is adverse to pressure rising in the wellbore. Besides, the influence of thermal gradient on wellbore flow is not obvious within one block during CO\({}_{2}\) fracturing.  
(4) Displacement is the key factor affecting CO\({}_{2}\) flow behavior in the wellbore during CO\({}_{2}\) fracturing.  
Because of the good compressibility and high permeability of CO\({}_{2}\), it is easy for CO\({}_{2}\) to intrude into the reservoir rock and cause rock damage. And the pressure and temperature distributions in  
Figure 13: Effect of geothermal gradient: (**a**) CO\({}_{2}\) pressure profile; (**b**) CO\({}_{2}\) temperature profile.  
the wellbore significantly affect the fracture initiation and propagation in the reservoir. According to CO\({}_{2}\) flow behavior in the wellbore, the fracture initiation and propagation under the real bottom hole temperature-pressure condition can be analyzed for further research.  
Model developed and solved, [PERSON] and [PERSON]; field measurement data acquisition and processing, [PERSON] and C.M.; calculated results analysis, [PERSON], [PERSON], and C.M.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], C.M., and L.Z.; funding acquisition, [PERSON] and C.M.  
This research was funded by [the Wellbore Sand-Carrying and Flow of CO\({}_{2}\) Fracturing] grant number [COYT-SLGYJZX-2017-JS-1513].  
The financial supports from the Wellbore Sand-Carrying and Flow of CO\({}_{2}\) Fracturing Project of Oil and Gas Research Institute in Changqing Oilfield. We sincerely thank our colleagues at the Water Jet Research Centre in China University of Petroleum (East China) for collaborating the theory research.  
The authors declare no conflict of interest.
## 1 Introduction
The WPD presented in [28] uses complementary splitting resonators (CSRRs) for harmonic suppression. The work presented in [29] proposes a dual-band filtering power divider that employs dual-resonance resonators (DRRs) to create multiple transmission zeros, resulting in two passbands with high-frequency selectivity, good isolation, and an ultra-wide stopband. A tri-band WPD is designed in [30], using resonators that replace each quarter-wavelength transformer in the conventional WPD. Additionally, the WPD presented in [31] uses quasi-elliptic filters to replace each quarter-wavelength transformer for improved out-of-band rejection. Moreover, due to the higher frequency limitations of microstrip substrates, crystal photonics have been widely applied in high-frequency designs [32; 33; 34; 35]. This method [36; 37] can be used for passive design structures, such as power dividers, in higher frequency ranges. Additionally, in some designs [38; 39; 40; 41; 42; 43], lumped components (L and C) have been used to create compact dividers with harmonics suppression that then cause a limited frequency response. The work presented in [40] discusses the design and implementation of a five-way lumped-element WPD to investigate multifrequency polarimetric SAR applications. the use of flexible and miniaturized lumped elements in the design of unequal Wilkinson power dividers is studied in [41], utilizing closed-form design methods for frequency characteristics' simulation. A lumped-element WPD using LC-ladder circuits composed of a capacitor and an inductor, alongside a series LR/CR circuit, is designed in [42]; this theoretically shows that broadband WPDs can be accomplished. Additionally,in [43], a compact structure for a dual-band WPD is proposed; it consists of a single line, two coupled lines, and four lumped elements. Overall, these designs demonstrate the effectiveness of using several techniques, such as incorporating resonators, open stubs, and lumped components, in the design of WPD and couplers to achieve desirable circuit performance in modern communication systems. However, in all of the mentioned approaches, there is no straightforward algorithm to ease the design procedures of the device's structure using desirable parameters; this task is very complicated. In this paper, a new design algorithm is presented to help in the design of complicated devices in microwave and communication systems, such as filters and power dividers, with easy steps. In this paper, two filters and a single open stub are used together to shape the proposed divider at 1.5 GHz, using the proposed design algorithm. With this algorithm, more than 12 unwanted harmonics (2 nd-13 th) are suppressed, and a wide suppression band from 2 GHz to 20 GHz is provided.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### Ablation Experiments
#### 4.4.3 Visualization of ECA Performance in Tumor Identification  
To better observe the areas of focus of ECA, heatmap results are generated from the first 50 epochs of training on the meningioma dataset, as shown in Figure 8. In this figure, the red region indicates where the model's attention is focused, reflecting the important region that the model considers relevant to the segmentation target; the more obvious the red color is, the more attention the model pays to that region. The six subgraphs in the figure correspond to different epochs in the training process, namely, epochs 0, 10, 20, 30, 40, and 50. It can be seen that, in the initial stage of training, the heatmap color is mainly concentrated in the image's edge region, which indicates that the model's attention  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Meningioma Dataset**} & \multicolumn{4}{c}{**BraTS2021 Dataset**} \\ \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Ratio**} & \multirow{2}{*}{**DSC**} & \multirow{2}{*}{**HD95**} & \multicolumn{4}{c}{**DSC**} & \multicolumn{4}{c}{**HD95**} \\ \cline{5-10}  & & & & **WT** & & **TC** & **ET** & **WT** & **TC** & **ET** \\ \hline \multirow{3}{*}{E} & 0.0 & 81.42 & 15.62 & 82.03 & 85.98 & 86.22 & 3.89 & 3.90 & 2.41 \\  & 0.2 & 83.23 & 7.10 & 83.02 & 86.84 & 86.53 & 3.74 & 3.77 & 2.56 \\ \cline{1-1}  & 0.4 & **89.22** & **2.88** & **83.07** & **87.04** & **87.15** & 3.69 & 3.73 & **2.26** \\ \cline{1-1}  & 0.6 & 84.73 & 5.41 & 82.16 & 86.92 & 86.62 & 3.87 & **3.62** & 2.41 \\ \cline{1-1}  & 0.8 & 77.80 & 17.09 & 82.50 & 86.67 & 86.01 & **3.58** & 3.76 & 2.48 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of region selection ratios of EGA. The bold is the best result.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multicolumn{4}{c}{**Meningioma Dataset**} & \multicolumn{4}{c}{**BraTS2021 Dataset**} \\ \cline{2-10}  & & \multirow{2}{*}{**Dice**} & \multirow{2}{*}{**HD95**} & \multicolumn{4}{c}{**Dice**} & \multicolumn{4}{c}{**HD95**} \\ \cline{5-10}  & & & & **WT** & & **TC** & **ET** & **WT** & **TC** & **ET** \\ \hline \multirow{3}{*}{nnUNet} & - & 86.81 & 5.03 & 81.47 & 86.81 & 85.83 & 3.96 & 3.43 & 3.02 \\  & +EGA & 81.42 & 15.62 & 82.03 & 85.98 & 86.22 & 3.89 & 3.90 & 2.41 \\ \cline{1-1}  & +CFA & 88.00 & 3.20 & 81.80 & 86.37 & 85.72 & 4.23 & 3.84 & 2.77 \\ \cline{1-1}  & +ECA & 89.22 & 2.88 & 83.07 & 87.04 & 87.15 & 3.69 & 3.73 & 2.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of different modules.  
distribution is more dispersed in the initial stage, and it pays less attention to the key regions of the image. As the number of epochs increases, ECA accurately locates the tumor region, which fully demonstrates that ECA is able to quickly locate the tumor region for segmentation operations when segmenting meningioma.
# Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles
## 3 System Design of Intelligent Retrieval of Fault Information
### The Framework Design of the Fault Data Retrieval System  
The primary goal of developing an intelligent retrieval system for NEV fault data is to provide support for various users in aspects such as fault-related information, intelligent diagnostic analysis, fault repair and maintenance, equipment operation, and comprehensive knowledge services. Therefore, the overall design of the system requires a high level of fault information retrieval capability and sufficient knowledge capacity.  
This study's intelligent retrieval system for NEV fault information uses a large language model as the reasoning engine and a knowledge graph as the knowledge base to analyze fault information and provide diagnostic and retrieval results. Its key features include leveraging the powerful reasoning and generation capabilities of LLMs, utilizing fine-tuning transfer learning techniques to handle and complete specific downstream tasks, and integrating the Neo4j database to store knowledge triples related to NEV faults. Based on the fault knowledge model, the system provides fault information retrieval and analysis for users.  
Figure 3: LLMs combined with knowledge graph retrieval.  
During the system's operation, when a vehicle experiences an abnormality, the user can input fault-related information through the front-end interface. After acquiring this information, the system processes it using a fault classification model to determine the fault type preliminarily. Depending on the user's specific situation, the system selects an appropriate query template and generates a corresponding Cypher query statement. It then performs query operations in the knowledge graph database, retrieves the results, and feeds them back to the fault reasoning and analysis model. The large language model performs fault reasoning on the fault information, determines the fault condition, and provides relevant suggestions, completing the entire fault data retrieval and diagnostic process.  
The framework structure of the intelligent retrieval system for NEV faults data includes a large language model module, a knowledge graph module, an integrated reasoning module, and a human-computer interaction module, as shown in Figure 4.
## 4 Discussion
### Potential for SIF to Monitor Changes in NPP Dynamics under Drought Events
Our study comprehensively analyzed the relationship between SIF and NPP during two long-term drought events in the HHH Plain at various aspects and scales, discussing and demonstrating the potential of SIF for monitoring dynamic changes in NPP induced by drought. When large-scale NPP data cannot be updated promptly, or real-time monitoring of drought-induced NPP dynamics is required, SIF can be used to monitor NPP dynamics. This provides a new perspective for real-time monitoring of drought-induced NPP dynamics and offers fresh insights into the relationship between SIF and NPP.