esa-sceva/satcom-chunk-collection huggingface dataset:
# The Net Influence of Drought on Grassland Productivity over the Past 50 Years
###### Abstract
The purpose of this paper is to analyze the net impact of drought on a grassland ecosystem and reasonably evaluate the net contribution of drought to ecosystem productivity. The Inner Mongolia grassland is the main body of temperate grassland in China, but there are few studies on the impact of drought on grassland productivity, especially the impact of different levels of drought. Research on the carbon cycle process of temperate grassland in China has mainly focuses on the response of grassland primary productivity, biomass dynamics, and soil organic carbon dynamics to climate change. For the Inner Mongolia grassland, the grassland ecosystem has been mainly affected by climate change, and the impact of other global change factors has been weak. [PERSON] found that the nitrogen deposition level of grassland in Inner Mongolia was relatively low (about 1\(\sim\)2 GN m\({}^{-2}\)) ([PERSON] et al., 2011), and the response level of grassland carbon sequestration to nitrogen deposition in China was less than 10 \(\mathrm{gc/m^{2}/yr}\)[34]. Based on the ecological process model, [PERSON] et al. designed sensitivity tests of different factors to study the sensitivity of the carbon budget of temperate grasslands in China to climate and CO\({}_{2}\) concentration changes. Their study found that the fertilization effect of CO\({}_{2}\) contributed 1.4% to an increase in grassland carbon storage, which could not offset the serious negative effect of climate change on carbon storage (\(-\)15.3%) ([PERSON] and [PERSON], 2013). [PERSON] et al. also reported that the interannual fluctuation of grassland carbon flux in China was mainly affected by climate change. Based on domestic and foreign literature and previous research, precipitation has been the main influence factor on the NPP change in Inner Mongolia. Thus, we pay attention to the net effect of drought caused by precipitation deficit on the productivity of the Inner Mongolia grassland over the past 50 years. Based on an analysis of the impactsof drought events on grassland productivity, due to the lag impact from drought and the interference of climate change factors, we could not obtain the total impact of drought events on grassland productivity from historical information. Therefore, we investigated the total impact of drought on grassland productivity using the Biome-BGC model as a tool based on different scenario sensitivity simulation experiments and attempted to separate the interaction influence between drought and climate change factors, to clarify the net influence caused by precipitation deficit (drought) on grassland ecosystem productivity over the past 50 years.
## 4 Discussion
The difference between the OTL found in this study, and the goal suggested by the AAPM-TG132, underlines the pressing need for a universally available, comprehensive library of site-specific phantom datasets with the ground-truth deformation data to determine a more robust operative tolerance level to test the DIR software. The registrations that pass the OTL for spatial accuracy can still result in a low percentage passing the dose difference criterion (as reported in Figures 4 and 5), indicating quite a complex relationship between the dose error and TRE accuracy. One limitation, shared by the spatial DIR metrics, is the indirect relationship between the quantification of the DIR error and the effect that error has on a given dose distribution. This is a complicated issue akin to invoking the gamma criterion for an IMRT QA analysis [32]. The TG-132 report did not suggest any tolerance goal for dose differences. For this reason, in the dosimetric analysis workflow reported in Figure 1, we have suggested a clinical evaluation criterion for the dose difference results. A moderate, as classified in [30], but significant correlation on a voxel basis for both the dose gradient and the TRE with a dose difference was found. [PERSON] et al. [17] observed a moderate correlation between the dose gradient and the resulting dose difference, in agreement with our findings. [PERSON] et al. have reported the effect of the DVF on dose-mapping and concluded that there is a complex correlation that depends on the TRE and the voxel location, relative to the dose gradient [31]. The Pearson correlation test results were always significant (\(p<0.05\)) for the voxel basis approach (Section 3.5). As reported by [PERSON] et al. [33], the \(p\)-value derived from the test provides no information on how strongly the two variables are related. With large datasets, as they are in the voxel-based analysis, very small correlation coefficients can be \"statistically significant.\" Therefore, a statistically significant correlation must not be confused with a clinically relevant correlation. It is important to underline that this study represents a Proof of Principle, and further work will be undertaken to explore the behaviour of the algorithm in other situations. In our experience, the clinical workflow is composed of several tasks and can be time-consuming. Using the application conditions described (software and hardware), it can require up to 30 min for a registration evaluation. However, this time depends on several factors, such as the software involved, the type of registration DIR selected, the region of interest chosen, the VMAT plan parameters and the hardware characteristics for the registration software, the dose calculation and the experience of the operator. Moreover, a separate time allowance is required for the analysis of the DVFs and dose distribution at the voxel level, using the in-house software.
## 4 Case Study  
To validate the scientific robustness and practical applicability of the proposed integrated 3S monitoring framework, this study selected the representative SDAs located in the Guangdong (GD) section of the Ganzhou-Shenzhen (GS)-HSR as the case study. The monitoring focuses on three key aspects during the construction process: identification of disturbance boundaries, extraction of soil and water conservation measures, and estimation of dynamic spoil volume changes. This case study comprehensively demonstrates the application methods, technical processes, and practical effectiveness of the monitoring framework across various scenarios. Additionally, it evaluates the framework's capacity to enhance monitoring accuracy and efficiency.
# Terahertz Rotational Spectroscopy of Greenhouse Gases Using Long Interaction Path-Lengths
## 2 Materials and Methods
### Submm-Wave Spectroscopy with Amplified Multiplier Chains  
We used two AMC from the Virginia Diodes Inc. as electronic sources for the submm-wave measurements coupled to CHARME (see Section 3.2) and to the THz FP cavity (see Section 3.3). They are based on the up-conversion of a synthesized microwave frequency using a cascade of Schottky diode frequency multipliers mounted in rectangular waveguide blocks. The guided radiation is launched into free space using a horn antenna and propagated through the interaction cell. Frequencies from 100 GHz to 1 THz can be conveniently produced and easily modulated in either amplitude or frequency as desired [16]. A typical power of 50 uW is available at 600 GHz.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 2 Model Description and Problem Formulation
### Scheduling Scheme Based on the MP Index Policy  
Sections 3.5 and 3.6 discussed the MP-based indices for a bandit process with scalar and multi-dimensional state spaces, respectively. In general, the MP-based index policy prioritizes tracking specific targets based on their state-dependent MP indices, regardless of the dimensions of the state, in descending order. At each time \(t\), the \(K\) targets are selected with the largest indexes, and the action \(a_{n,t}\), \(n=1,\ldots,N\), is generated. Then the TEC and dynamic states of \(N\) targets are updated depending on actions and a non-linear filter. Finally, calculate the cost in time \(t\). The pseudo-code of implementing the MP index policy with given indices is provided in Algorithm 1, which is applicable to both cases with one-dimensional and multi-dimensional state spaces.  
```
// Initialization
1 The model parameters of the problem, i.e., \(N\), \(K\), \(T\), \(p_{\text{d}}\), \(\beta\), \(\mathbb{Q}\triangleq(\mathbf{Q}_{n})_{n=1}^{N}\), and the target parameters, i.e., \(\mathbb{P}_{0}\), \(\mathbb{X}_{0}\), \(\mathbb{U}\). // Main loop
2 for\(t\gets 1\)to\(T\)do
3 for\(n\gets 1\)to\(N\)do
4 Calculate the index of the states \(\mathbf{P}_{n,t}\) and \(\hat{\mathbf{x}}_{n,t}\) of arm \(n\) in (47);
5 end for
6 Select the \(K\) targets with the largest indexes and generate the \(a_{n,t}\), \(n=1,\ldots,N\), where for the multi-target with the same indexes, they are randomly selected;
7 for\(n\gets 1\)to\(N\)do
8 Update the TEC state \(\mathbf{P}_{n,t+1}\) of arm \(n\) depending on actions and detections, in (7), (8), and (10);
9 Update the dynamic state \(\hat{\mathbf{x}}_{n,t+1}\) of arm \(n\) depending on the non-linear filter;
10 end for
11 Calculate the cost in time \(t\).
12 end for
```  
**Algorithm 1**The scheduling scheme based on the MP index policy  
In the Whittle index policy, for the case with scalar bandit states, if the actions of each target across \(\tau\) time horizon are all assumed as 1, the computational complexity of (42) is \(O(N2^{\tau+1})\). Note that due to the combination of actions, i.e., 0 and 1, across \(\tau\) time horizon, the computational complexity of the Whittle index is less than \(O(N2^{\tau+1})\). For the case with multi-dimensional state space for each bandit process, the truncated time horizon \(\tau\) is the recursive horizon in (44) and (45). In general, the computational complexity of the non-linear filter is \(O(L^{2})\). The complexity for computing the indices in (47) is \(O(N2^{\tau+1}L^{2})\).  
For non-predetermined complex dynamic models, data-driven approaches can learn the dynamic features from the tracking data. Based on this learned information, the proposed policy can introduce new dynamic models and modify the model probability matrix \(\mathbf{U}\). Meanwhile, the adaptive filtering technology helps prevent tracking performance degradation during the learning process [36; 37].
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### GCS-Routing Power Consumption  
Generally, the major power consumption in a drone is throttling by the motors for flight, as shown in Figure 19. Power consumption increases rapidly as the drone starts flying (throttling up) and falls quickly when the drone stops flying (throttling down). Table 13 shows that throttling up the motors consumed more than 97% of the overall drone consumption. Since flight takes most drone power consumption, it is meaningless to consider power consumption caused by routing operations.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Non-Throttling Phase** & **Throttling Phase** \\ \hline Average power consumption (W) & 6.98 & 242.27 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Drone average power consumption for different phases.  
Figure 19: Drone power consumption during throttling phase.  
Figure 18: Simplified view of large scale network scenario.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Planar Primitive Detection
#### 3.2.1 Initial Planar Primitive Extraction  
First, we perform the region-growing method to obtain the initial planar primitives from the filtered point clouds. The region-growing technique aims at finding a maximum set of points in the neighborhood. There are two key conditions of region-growing: (1) Propagation criterion denoting that the growing process is supposed to satisfy a criterion according to the features such as the normal, curvature, and point distributions, and (2) termination criterion, which is the ending condition of the propagation. This technique is widely used in point clouds segmentation by testing every neighboring point for the angle difference between the normals of the seed points and neighboring points.  
In our case, we set up the growing criterion as the following equation:  
\[cos^{-1}(|(N_{S_{c}},N_{P_{c}})|)<\theta_{thr}, \tag{1}\]  
where \(N_{S_{c}}\) represents the normal of the current seed point, and \(N_{P_{c}}\) denotes the points' normals in the neighborhood. \(\theta_{thr}\) is the threshold to determine whether two points belong to the same plane (e.g., \(10^{\circ}\)). In practice, we use the Fast Library for Approximate Nearest Neighbors (FLANN) [20] to speed up the neighboring points search (e.g., 20 nearest points). Since the selection of seed points affects the quality and the speed of detection, we choose the points with minimal curvature in the unassigned point set.  
Figure 3 shows the result of the region-growing. Although the region-growing method can obtain most of the correct planar primitives, some points belonging to the same wall are assigned to different labels. As shown in Figure 3, there are two situations of wrong segmentation: Overlapping and separating. The left section in Figure 3 illustrates points belonging to the same wall separated into several overlapping pieces. This is mainly caused from by scanning and registration errors. Another situation of the erroneous detection is that the single wall is divided into several non-overlapping parts. As shown in the right of Figure 3, the interested plane has been divided into three parts (i.e., blue, red, and green). This is because the indoor walls are not absolutely smooth, which gives rise to the normal inconsistency. Therefore, we merely utilize the result of region-growing as the initial assignment of point clouds and apply a refinement process to solve aforementioned problems.  
Figure 2: The flowchart of the proposed approach involving four major steps. (**a**) Planar primitive detection; (**b**) point clouds partition using boundary points; (**c**) layer contour extraction in the 2D space; (**d**) building assembly.
# Biochar Affects Heavy Metal Uptake in Plants through Interactions in the Rhizosphere
## 2 Materials and Methods  
A pot trial was set up using a full factorial design with three soils, two levels of biochar addition (none or 2% _w/w_) and three replicates. Two polluted soils were collected from a field site at different distances from a copper smelter located in the Lower Silesia region, Poland. Sixty years of copper smelter operation and atmospheric deposition of heavy metals onto near-by arable soil had caused high concentrations of Cd, Cu, Pb and Zn [24; 25]. Highly polluted (HP) soil was collected from a field 0.3 km from the smelter (51\({}^{\circ}\)18\({}^{\prime}\)60.1\({}^{\prime\prime}\) N, 16\({}^{\circ}\)10\({}^{\prime}\)55.8\({}^{\prime\prime}\) W) and weakly polluted soil (WP) was collected 2 km from the smelter (51\({}^{\circ}\)19\({}^{\prime}\)55.9\({}^{\prime\prime}\) N, 16\({}^{\circ}\)10\({}^{\prime}\)31.8\({}^{\prime\prime}\) W). Soil with similar properties (pH, particle size distribution, C, N and clay content) was chosen as an unpolluted soil and collected at Dal, Viken County, SE Norway (60\({}^{\circ}\)14\({}^{\prime}\)39.2\({}^{\prime\prime}\) N, 11\({}^{\circ}\)11\({}^{\prime}\)0.60\({}^{\prime\prime}\) E), where the experiment was conducted. All soils were Cutanic Luivisols (FAO-WRB 2007 soil classification), containing about 75% of silt, <20% of clay and <5% of sand, having a pH in distilled water from 6.16 in control soil, 7.35 in highly polluted soil to 7.53 in weakly polluted soil. As all soils were cultivated, high pH values can be explained by application of calcium fertilizers, as Polish and Norwegian soils have low pH and liming is a standard procedure before cultivation. Another potential explanation for the high pH of HP and WP soils is the input of CaO and MgO from the copper smelter. In this area, intensive soil alkalization is observed due to alkaline dust emission from the smelter [26]. Basic characteristics of biochar and soil, as well as total concentrations of tested metals in both substrates are presented in Table 1. The biochar used was produced from _Miscanthus_ in a Pyreg pyrolysis oven at 500-700 \({}^{\circ}\)C and characterized regarding pH, C, H and N content (using a CHN-1000, LECO, St. Joseph, MI, USA). The content of heavy metals was measured after digestion in concentrated HNO\({}_{3}\) using microwave-heated pressurized vials (Milestone Ultraclave III, Sorisole, Italy) followed by elemental analysis by inductively coupled plasma-mass spectroscopy 8800 ICP-MS triple Quad (Agilent Technologies, Santa Clara, CA, USA) [27].
## 3 Results and Discussion
### Composition Analysis  
The infrared spectrum of 0.67:1 microcapsules and water-based topcoat paint films is shown in Figure 6. In the infrared spectrum of microcapsules, 3360 cm\({}^{-1}\) was the N-H absorption peak; 2929 cm\({}^{-1}\) and 2865 cm\({}^{-1}\) were C-H stretching vibration peaks; 1639 cm\({}^{-1}\) was attributed to the C=O stretching vibration of urea formaldehyde resin in wall material; and 1730 cm\({}^{-1}\) was the C=O characteristic peak, which proved the existence of water-based acrylic resin in microcapsules. In the infrared spectrum of the water-based topcoat paint film without microcapsules, 2929 cm\({}^{-1}\), 2865 cm\({}^{-1}\) and 1447 cm\({}^{-1}\) were the stretching vibration peaks of \(-\)CH\({}_{2}\), 1730 cm\({}^{-1}\) was the vibration absorption peaks of C=O. The main component of the coating is waterborne acrylic acid copolymer dispersion. Because the main components of microcapsules and coatings with microcapsules were very similar, there was not much difference between the functional groups in the spectrum [31; 32].  
Figure 3: Microstructure of 0.67:1 microcapsules: (**A,B**) SEM of microcapsules, (**C,D**) OM of microcapsules.  
Figure 4: SEM images of waterborne topcoat paint film with different contents of 0.67:1 microcapsules: (A) 0; (B) 5.0%; (C) 10.0%; (D) 25.0%.  
Figure 5: Self-healing test of water-based topcoat paint film (0.67:1 microcapsules): (**A**) for one day, (**B**) for five days.  
Figure 6: Infrared spectrogram of 0.67:1 microcapsules and water-based topcoat paint film.
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 5 Experiment
### Field Experiment
#### 5.2.3 Experiment Results  
The BERs of the destination node and the eavesdroppers, respectively, were measured when the source node sent the data. The average BER was obtained by measuring the BER 1000 times repeatedly. In every case, the average BER of the destination node was 0, which means that the data sent by the source node were all received. Figure 7 shows a graph applying the average BER results of the eavesdroppers to the ILR metrics defined in Section 3.  
In Figure 4a, the ILR of the eavesdroppers, except for eve1 and eve3, is 1. The ILR of eve1 and eve3 is 0.639 and 0.819, respectively. Therefore, if the devices have no mobility and the friendly jamming technique is not used, the eavesdropping probability of the eavesdroppers is high. Furthermore, it was found that, as the distance between the eavesdropper and the source node increases, the eavesdropping probability of the eavesdropper decreases.  
In Figure 4b, the ILR of every eavesdropper decreases because the cooperative jamming signals affect the communication quality of the eavesdroppers. The ILR of both eve2 and eve5, which are within the cooperative jamming signal range of the drones, is zero. However, the ILR of the eavesdroppers outside the cooperative jamming signal range of the drones is greater than or equal to 0.5. Therefore, if the friendly jamming technique is used without the mobility of the devices, the eavesdropping probability of the eavesdroppers outside the cooperative jamming signal range is high.  
In Figure 4c, the transmission signal strength is reduced because the distance between the source and destination nodes is close. As a result, the ILR of eve1 and eve3 is 0.180 and 0.121, respectively. However, the ILR of eve2, eve4, eve5, and eve6, which are relatively close to the source node, is 0.732, 0.380, 0.433, and 0.338, respectively. Although the ILR of every eavesdropper decreases compared to that of Figure 4a, the ILR of some eavesdroppers is high. Therefore, if the devices have mobility but the friendly jamming technique is not used, the eavesdropping probability of the eavesdroppers is high.  
Figure 7: Field experiment results; conventional schemes (**a**–**c**) and proposed scheme (**d**).  
In Figure 4d, which is the case of the CFJ-DMZ model, the ILR of every eavesdropper is zero. In other words, when the mobility of the devices and the friendly jamming technique are used together, the communication quality of the eavesdroppers decreases, reducing the eavesdropping probability. Therefore, secure communication is facilitated in the secure zone.
## 3 Material and Methods
### AdaBoost Algorithm  
Adaptive Boosting, also known as AdaBoost, is a boosting algorithm that attempts to use weighted derivatives of the same testing dataset rather than sub-samples [72]. The benefit of this approach is that the algorithm does not need a large amount of data because it uses the same training dataset twice [73]. The algorithm is well-known for producing good results when constructing ensemble classifiers [74]. To get a classification model of the ensemble prediction function H:X \(\rightarrow\) (\(-\)1, +1) shown in Equation (6), the AdaBoost machine learns using a series of weak learners or classifiers.  
\[H(x)=sign\Bigg{(}\sum_{m=1}^{M}a_{m}H_{m}(x)\Bigg{)} \tag{6}\]  
where \(H(x)\) is the output of the developed ensemble classifier, \(a_{1}\),..., \(a_{m}\), are a set of weights, and \(Hm(x)\) is the performance of the weak learners \(m\)\(\in\)(1,..., \(M\)) that are combined to get \(H(x)\). In each round of the algorithm, the weights allocated to the training dataset are determined by how previous classifiers behaved. The algorithm then works on the specimens or data sets that have already been mistakenly classified in this case. Figure 5 shows the flowchart of procedures for AdaBoost modelling. More information on the AdaBoost concept can be found in the other studies ([75,76]).