esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Changes in Water Use Efficiency to Understand Drought Induced Tree Mortality
## 3 Results
### Understanding Drought Induced Tree Mortality  
Drought frequency, WUE\({}_{\text{BASELINE}}\), and \(\Delta\)WUE were important for explaining drought induced maximum tree mortality rates at the ecosystem scale (Figure 7). Tree mortality was classified into six classes: 14, 15, 48, 49, 68, and 91 TPH. The majority of forested area had an estimated mortality rate of 14 TPH (63%), and just 2% of all forested land had mortality rates as high as 91 TPH. California forest with a drought frequency <4.5 years had the lowest average tree mortality (14 TPH).  
Figure 5: (**a**) Forest Inventory Analysis (FIA) plots with elevated tree mortality rates (red) denote the patchy nature of mortality within forested areas. (**b**) Maximum post drought tree mortality in elevated FIA plots peaked in 2015. Latitude and longitude are shown along the axes.  
Figure 6: Maximum tree mortality rates (2013–2016) (trees per hectare; TPH). Mortality data was derived from aerial detection surveys of California forest (USFS Region 5) 2013–2016 ([[http://egis.fire.ca.gov/TreeMortalityViewer/Help.html](http://egis.fire.ca.gov/TreeMortalityViewer/Help.html)]([http://egis.fire.ca.gov/TreeMortalityViewer/Help.html](http://egis.fire.ca.gov/TreeMortalityViewer/Help.html))). Latitude and longitude are shown along the axes.  
Where drought frequency was >4.5 years, WUE\({}_{\text{BASELINE}}\) was important for understanding the level of enhanced drought induced mortality rates. Even under high severe drought frequencies (>4.5 years), a WUE\({}_{\text{BASELINE}}\geq\)2.35 g C/mm H\({}_{2}\)O was associated with a very small increase in tree mortality rates (15 TPH). Where WUE\({}_{\text{BASELINE}}\) was <2.35 g C/mm H\({}_{2}\)O, dramatic increases in tree mortality co-occurred with the interaction of \(\Delta\)WUE and the frequency of severe drought. Surprisingly, lower values of \(\Delta\)WUE were associated with lower tree mortality rates when severe drought frequency was high. Variable importance, the reduction in mean squared error attributed to each variable, was greatest for drought frequency (0.28), followed by WUE\({}_{\text{BASELINE}}\) (0.21), and \(\Delta\)WUE (0.1). Together, drought frequency, WUE\({}_{\text{BASELINE}}\), and \(\Delta\)WUE accounted for 23% of the variation in drought induced tree mortality rates.
## 2 Materials and Methods
### DTF and STF Differences in Energy and Angular Distributions
#### 2.2.1 Energy Distributions  
The ternary fragment kinetic energies and emission angles in DTF and STF mechanisms are reported in Figures 4 and 5. They are calculated according to the equations  
Figure 3: Potential Energy of a ternary fragmentation involving two out of three double magic nuclei in collinear and equatorial configurations.  
Figure 2: Dalitz plot of the potential energies for collinear (**a**) and equatorial (**b**) ternary fragmentations of \({}^{248}\)Fm (\(Z=100\)). The potential energies are calculated as a function of the charge numbers with the constraint for the fragment masses \(A_{1}\geq A_{2}\geq A_{3}\).  
described in Section 2.1. As input parameters, we considered the following: (i) All mass and atomic numbers of the above-discussed three-fragment combinations to calculate nuclear masses and reaction Q-values; (ii) Kinetic energies ranging from 1 to 350 MeV with 1 MeV step and emission angles ranging from 0 to \(-180^{\circ}\) with \(5^{\circ}\) step for the heavy fragment \(A_{1}\); (iii) Emission angles ranging from 0 to \(180^{\circ}\) with \(5^{\circ}\) step for the medium fragment \(A_{2}\). Only configurations with fragments \(A_{1}\) and \(A_{2}\) emitted on opposite sides with respect to the beam direction have been selected. This constraint is chosen to simulate the detector geometry of a typical setup (see later in Section 2.4).  
As shown in Figure 4, kinetic energies larger than 270 MeV appear only in DTF mechanisms. Since the kinetic energy is mainly of Coulomb origin, this effect is related to the strength of the repulsion at the specific stage at which the repulsion occurs and on the atomic number of the fragments. Clearly, for DTF the highest energy of the third fragment is consistent with the repulsion of the medium and heavy fragments with lowest kinetic energies.  
For the STF, Coulomb repulsion acts during two steps. In the first step, Coulomb repulsion is responsible for the velocity of the fragments \(A_{1}\) and \(A_{23}\). The second step produces a repulsion of nearly equal strength. The configuration that guarantees very low energy of \(A_{2}\) and \(A_{3}\) is therefore not possible. The overlapped kinematics imposes further constraints. Consequently, the heavier fragment can reach energies up to 350 MeV, and the configurations with low \(E_{1}\) and \(E_{2}\) are not possible.  
Figure 4: Kinetic energies of light fragments \(E_{3}\) presented as a function of \(E_{1}\) and \(E_{2}\) by assuming DTF (**a**) and STF (**b**) mechanisms. The calculations have been performed with the conditions: \(A_{1}\geq A_{2}\geq A_{3}\), emission of \(A_{1}\) and \(A_{2}\) on the opposite side with respect to the beam direction and wide range for the \(A_{1}\) energies. See the text for more details.
## 1 Introduction
Some studies adopted the Additive Manufactured (AM) technology [4; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 39], which can effectively design and manufacture metal objects with highly complex geometric shapes, to customize and build high-performance heat transfer units in a confined space. AM technology and material properties can also consider the weaker structural properties induced by a lower metal content in the material. The structural strength of the materials of the parts and components manufactured by AM can be enhanced by a complex heat treatment process, but the post-process is time consuming and costly. As observed from previous studies, the cold plate can be used in high-performance and high-power cooling systems. In addition, to reduce the development costs, CFD and Finite Element Method (FEM) software, including ANSYS, COMSOL, and FloEFD software, can be used for the simulation analysis. In addition, if the shape of the cold plate hard product is too complex, then it can be manufactured by using advanced AM technology. In order to develop an integrated heat dissipation system that is suitable for high-power electronic equipment, where the size and weight of the system are limited, this study integrated a fluid circulation-type cold plate structure with a T/R module. Additionally, it adopted the optimal design of the liquid flow channel inside the cold plate and the active heat dissipation assembly of the thermoelectric cooler. The FEM was used as the COMSOL fluid-solid coupling heat transfer analysis software for the structure to analyze the heat dissipation performance of the proposed fluid circulation-type cold plate. The Taguchi method was used to optimize the designed fluid circulation-type cold plate. According to the simulation and experimental validation results, the proposed integrated heat dissipation mechanism can control the temperature equalization of the system within the specified requirements. The system can operate under the energy-saving and performance specifications and requirements. Moreover, the effectiveness and practicality of the heat dissipation mechanism proposed in this paper can be verified, and a reference can be provided for the design of high-power electronic equipment in a heat dissipation system.
## 3 System Resource Optimization Algorithm
### Optimal Equation  
We choose the expected total discount reward optimality criterion as our objective function to maximize the expected total reward and obtain the optimal policy. Then, v(s) can be expressed as follows:  
\[v(s)=\underset{\pi\in\Pi}{\max}v^{\pi}(s), \tag{46}\]  
where \(v^{\pi}(s)\) is the expected total reward when the policy \(\pi\) with an initial state \(s\) is given.  
Notably, when the terminal equipment takes the most beneficial action \(a\), the expected total reward can be maximized, and such an optimal action \(a\) can be obtained in each state by solving the objective function. The optimality equation is given as follows:  
\[v(s)=\underset{a\in A}{\max}\Bigg{\{}q(s,a)+\sum\limits_{s^{\prime}\in S} \lambda P[s^{\prime}|s,a]v(s^{\prime})\Bigg{\}}, \tag{47}\]  
where \(\lambda\) is a discount factor in the MDP model, and \(\lambda\) closer to 1 gives greater weight to future rewards. The algorithm aims to identify the best resource optimization strategy in the proposed multilevel offloading model, that is, the UAV relay. To solve the optimization problem and identify the best decision mentioned above, this study applies Algorithm 2.  
```
1:Set \(v^{0}(s)=0\) for each state \(s\). Specify \(\varepsilon>0\). \(\chi=0\)
2:Input:\(q(s,a)\), P[s\({}^{\prime}\)|s, a]
3:Step 1: For each state \(s\), Calculate \(v^{\chi+1}(s)\)
4:\(v^{\chi+1}(s)=\underset{a\in A}{\max}\Bigg{\{}r(s,a)+\sum\limits_{s^{\prime} \in S}\lambda P[s^{\prime}|s,a]v^{\chi}(s^{\prime})\Bigg{\}}\)
5:Step 2:If \(\left|v^{\chi+1}(S)-v^{\chi}(S)\right|<\varepsilon\left(1-\lambda\right)/2\lambda\)
6:then, Go to Step 3
7:else
8:\(\chi=\chi+1\)
9: Go to Step 1
10:endif
11:Step 3: For each state \(s\), compute the stationary optimal policy
12:\(\delta(s)=\underset{a\in A}{\operatorname{argmax}}\Bigg{\{}r(s,a)+\sum \limits_{s^{\prime}\in S}\lambda P[s^{\prime}|s,a]v^{\chi}(s^{\prime})\Bigg{\}}\)
13:Stop.
```  
**Algorithm 2** Resource optimization algorithm  
Given the complexity of the algorithm, the decision-making time cannot be ignored. Supposing the channel is quasistatic, the terminal equipment can use a table to store the best offloading strategy for the tasks computed by the MDP algorithm. This table includes the best decision for each state of the computing task, which can be obtained by pre-calculation. Hence, the decision time of this algorithm is short and will not affect the task calculation [27].
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 4 Cloud-Edge Collaborative Online Scheduling Algorithm
### Prioritization Strategy  
#### 4.1.1 Prioritization of Microservices  
A power task consists of a series of microservices, and prioritizing microservices is crucial for task scheduling [18]. If there are dependencies between microservices, the execution order cannot be exchanged. For example, the sampling class microservices must be executed first before the later analysis and control class microservices can be executed. In addition, the execution order of parallel microservices affects the total delay of task processing [19]. In this paper, Equation (1) is used to calculate the priority of each microservice in the power task.  
\[\mathrm{rank(i)}=\overline{\mathrm{r_{i}^{m}}}+\max_{\mathrm{v_{i}^{m}\in \mathrm{Succ}(v_{i}^{m})}}(\mathrm{w_{ij}^{m}}+\mathrm{rank(j)}) \tag{1}\]  
where \(\mathrm{rank(i)}\) is the scheduling priority of \(\mathrm{v_{i}^{m}}\), \(\overline{\mathrm{r_{i}^{m}}}\) is the average processing time of \(\mathrm{v_{i}^{m}}\), as shown in Equation (2), which is the ratio of the sum of the processing time of the tasks at each device to the number of devices. The average processing time of the central cloud is set to 0.8 times of the processing time of the edge devices. According to Equation (1), the scheduling priority of all microservices will be lower than their predecessor microservices, and the parallel microservices with high communication cost will get scheduled first.  
\[\overline{\mathrm{r_{i}^{m}}}=(\sum_{n=1}^{n=N}\mathrm{r_{i,n}^{m}})/N \tag{2}\]
## Appendix A Derivation of Differential Scattering Cross Section
In Equation (17), \(\mathbf{k}_{0}/\mathbf{k}\) are the initial/final wave vectors for the scattered particles, respectively, and\[f_{nm}(\theta)=-\frac{Me^{i\pi/4}}{\sqrt{2\pi k\hbar^{2}}}\int d\mathbf{r}_{A}d \mathbf{r}_{B}d\mathbf{R}^{\prime}\,e^{i(\mathbf{k}_{0}-\mathbf{k})\cdot\mathbf{ R}^{\prime}}V\big{(}\mathbf{r}_{A},\mathbf{r}_{B},\mathbf{R}^{\prime}\big{)} \chi^{*}_{A,n}(\mathbf{r}_{A})\chi_{A,1}(\mathbf{r}_{A})\chi^{*}_{B,m}(\mathbf{ r}_{B})\chi_{B,1}(\mathbf{r}_{B}). \tag{107}\]  
The 2D differential scattering cross section is, analogously to that of a 3D system, given by [41]  
\[ \begin{split} I_{n,m}(\theta)&=\frac{k}{k_{0}}|f_{ nm}(\theta)|^{2}\\ &=\frac{M^{2}}{2\pi k_{0}\hbar^{4}}\bigg{|}\int d\mathbf{r}_{A}d \mathbf{r}_{B}d\mathbf{R}^{\prime}\,e^{i\mathbf{q}\cdot\mathbf{R}^{\prime}}V \big{(}\mathbf{r}_{A},\mathbf{r}_{B},\mathbf{R}^{\prime}\big{)}\chi^{*}_{A, n}(\mathbf{r}_{A})\chi_{A,1}(\mathbf{r}_{A})\chi^{*}_{B,m}(\mathbf{r}_{B}) \chi_{B,1}(\mathbf{r}_{B})\bigg{|}^{2},\end{split}  \tag{108}\]  
with \(\mathbf{q}=\mathbf{k}_{0}-\mathbf{k}\). Finally, recalling that the free carrier (electron or hole) has no internal structure, the sum of the internal energies of the system reduces to that of the exciton. Furthermore, Equation (108) can be simplified further by assuming a central field approximation, reading  
\[I_{n,m}(\theta)=\frac{M^{2}}{2\pi k_{0}\hbar^{4}}\bigg{|}\int d\mathbf{r}d \mathbf{R}^{\prime}\,e^{i\mathbf{q}\cdot\mathbf{R}^{\prime}}V\big{(}\mathbf{ r},\mathbf{R}^{\prime}\big{)}\chi^{*}_{n}(\mathbf{r})\chi_{1}(\mathbf{r}) \bigg{|}^{2}, \tag{109}\]  
where \(\chi\) is now the wave function of the exciton, \(\mathbf{r}\) is the relative position of the electron and hole in the exciton, and \(\mathbf{R}^{\prime}\) is the relative position from the free carrier to the center of mass of the exciton.
## 2 Materials and Methods
### Graph Convolutional Networks (GCNs)  
Both topological and biological features require node embedding computation to generate graph-level embeddings, which are obtained by aggregating node-level embeddings. Among the various existing node embedding algorithms, we choose to use graph convolutional networks (GCNs) because they are invariant to graph representation, provided that the initialization is carefully designed [35]. GCNs are also inductive, allowing us to compute node embeddings for any unseen graph by applying the GCN operation. GCNs have become one of the most popular models for node embeddings and are part of the family of neighbor aggregation-based methods. Their key operation graph convolution is defined as [36]  
\[conv(u_{n})=f_{l}(\sum_{m\in\mathcal{N}(n)}\frac{1}{\sqrt{d_{n}d_{m}}}u_{m}W_{l} ^{l}+b_{l}^{l}), \tag{4}\]  
where \(\mathcal{N}(n)\) is the set of first-order neighbors of node \(n\), which includes all nodes that are directly connected to node \(n\). \(\frac{1}{\sqrt{d_{n}d_{m}}}\) is a normalization factor, \(d_{n}\) is the degree of node \(n\), and \(d_{m}\) is the degree of node \(m\). \(u_{m}\) is the feature vector of a neighbor node \(m\) and \(W_{1}^{l}\) is the weight matrix used in the \(l\)-th GCN; it transforms the feature vectors of the neighbors. \(b_{l}^{l}\) is the bias added during the transformation in the \(l\)-th layer, and \(f_{l}(\cdot)\) is an activation function applied element-wise to the result of the linear transformation and aggregation such as \(ReLU(x)=max(0,x)\).
## Materials and Methods  
### Study Area  
The study area is in the middle reaches of the HRB (100.23\({}^{\circ}\) E, 39.06\({}^{\circ}\) N), which is located in Zhangye City, Gansu Province, in northwestern China (see Figure 1). HRB is China's second largest inland river, covering an area of about \(1.3\times 105\) km\({}^{2}\)[27], with a total population in the middle reaches of \(82.55\times 10^{4}\) in 2010 [28]. The study area exhibits a continental dry temperate climate, with a mean annual temperature (MAT) of approximately 6 to 8 \({}^{\circ}\)C and a mean annual precipitation (MAP) of 150 mm [29]. The elevation of the area is between 1300 and 1700 m above sea level. Major land use types are cultivated land, grassland, and barren land, and the main crops include corn and spring wheat. Precipitation in the middle reaches of the HRB is limited, with farmland accounting for about 86.85% of water consumption in the basin [30]. Gray-brown desert soil and gray desert soil are the zonal soil types in the study area [31].  
Figure 1: Location of the study area, and soil samples are superimposed on the synthetic aperture radar (SAR) and optical composite images of the study area. (**a**) The Sentinel-1 composite image (R: 25 September 2015, G: 21 June 2015, B: 28 February 2015); (**b**) The Landsat-8 OLI composite image (RGB = 432).
# Dynamics of Microbial Community Structure and Metabolites during Mulberry Ripening
## 3 Results and Discussion
### Dynamic Changes in Organic Acids in WHIDS during Ripening  
Organic acids are important components in determining the acidity of mulberry fruit, and their content and type have a direct impact on the flavour and texture of mulberries. A total of 10 organic acids--tartaric acid, pyruvate, malic acid, lactic acid, fumaric acid, citric acid, succinic acid, oxalic acid, acetic acid, and \(\alpha\)-ketoglutaric acids--were tested to determine the changes in WHDS during maturation using high-performance liquid chromatography (HPLC), and the results are shown in Table 2. Tartaric acid is an important organic acid found in mulberry fruits and is also the most abundant organic acid in WHDS, which not only has strong chemical stability but also exhibits certain antibacterial properties [37]. The tartaric acid content of WHDS showed a decreasing trend with the increase in maturity and was highest in S2 at 16.45 \(\pm\) 0.24 mg/L and lowest in S5 at 9.24 \(\pm\) 0.14 mg/L. Lactic acid and acetic acid were placed second and third in organic acid content, and both reached their maximum values at S2 with 10.34 \(\pm\) 0.13 mg/L and 9.94 \(\pm\) 0.16 mg/L, respectively. Lactic and acetic acids are common organic acids found in fruits, and applying acetic acid to the skin of growing fruits also improves post-harvest fruit quality [38]. Malic acid and fumaric acid are the main organic acids in mulberry, with maximum contents of 2.73 \(\pm\) 0.04 mg/L and 1.34 \(\pm\) 0.12 mg/L, respectively, and the total of the two accounts for about 10% of the organic acids. Oxalic acid levels ranged from 0.50 \(\pm\) 0.10 mg/L to 0.95 \(\pm\) 0.02 mg/L with little overall variation. \(\alpha\)-Ketoglutaric acid, citric acid, and pyruvate acid are the main contributors to the acidity of the fruit [39; 40], and their levels during ripening in WHDS did not differ much, with maxima of 0.47 \(\pm\) 0.01 mg/L, 0.47 \(\pm\) 0.01 mg/L, and 0.43 \(\pm\) 0.02 mg/L, respectively, whereas succinic acid was only detected in S1, at 0.11 \(\pm\) 0.01 mg/L.  
It is worth noting that, except for succinic acid and citric acid, the trends in the contents of the other eight organic acids increased and then decreased, with the peaks occurring at S2, which is basically consistent with the results of a study on the ripening stage of plum fruits [41]. It can be seen that there are large differences in the composition of organic acids at different maturity stages of WHDS, and the adulteration of WHDS with different maturity levels in the same production process may lead to an imbalance in the flavour of fruit wines, fruit vinegars, and other beverages, affecting the quality of the products.  
Figure 2: Alterations in ABTS RSA (**a**) and DPPH RSA (**b**) at different maturity stages of WHDS.
# GNSS Interference Monitoring and Detection (GIMAD) System +
###### Abstract  
In the last few years, gradually more systems have relied on the Global Navigation Satellite System (GNSS) for their correct functioning. These systems include safety-critical applications such as airports or emergency services. Given the wide number of GNSS applications and the current availability of affordable and easily configurable Software-Defined Radio (SDR) devices, GNSS has become the target of numerous Radio Frequency Interference (RFI) attacks. Thus, RFI has become a real threat for GNSS and, hence, for those systems relying on it. With the purpose of detecting, characterizing, and localizing RFI not only in GNSS frequency bands but also in other daily-used frequency bands, a GNSS Interference Monitoring and Detection (GIMAD) prototype has been developed, with special emphasis on its deployment in safety-critical environments such as airports. GIMAD contemplates European Geostationary Navigation Overlay Service (EGNOS) V3 and International Civil Aviation Organization (ICAO) Ground Based Augmentation System (GBAS) RFI masks covering both in-band and out-band RFIs. In addition, GIMAD was tested in a real-field scenario.  
GNSS; GPS; Galileo; RFI; Jamming; RFI detection; RFI characterization; AoA; ICAO; EGNOS; GBAS +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 3 Establishment of the Smart Highway Evaluation Framework
### Definition of Evaluation Indicators
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Guideline Level B** & & **Indicator Level C** \\ \hline \multirow{8}{*}{Efficiency (B311)} & \multirow{8}{*}{Traffic service (B3111)} & Peak saturation (C66) \\  & & Traffic service volume (C67) \\ \cline{3-3}  & & Average speed (C68) \\ \cline{2-3}  & \multirow{4}{*}{Toll service (B3112)} & Percentage of ETC lanes (C69) \\ \cline{3-3}  & & Toll collection status (C70) \\ \cline{3-3}  & & Manual lane service level (C71) \\ \cline{3-3}  & \multirow{2}{*}{Emergency dispatch (B3113)} & Average arrival time (C72) \\ \cline{3-3}  & & Traction service satisfaction (C73) \\ \hline \multirow{8}{*}{Safety (B312)} & \multirow{8}{*}{Road safety (B3121)} & Monitoring facility coverage rate (C74) \\ \cline{3-3}  & & Accuracy of overload and over limit monitoring \\ \cline{3-3}  & & C75) \\ \cline{3-3}  & & Structural sensing facility coverage rate (C76) \\ \cline{2-3}  & \multirow{2}{*}{Vehicle and user safety (B3122)} & Traffic accident rate (C77) \\ \cline{3-3}  & & At-fault accidents during traffic operations (C78) \\ \hline \multirow{8}{*}{Intelligence (B313)} & Network information sharing (B3131) & Highway network coverage rate (C79) \\ \cline{3-3}  & & Meteorological traffic information transmission \\ \cline{3-3}  & \multirow{2}{*}{Lane-level control (B3132)} & Meteorological traffic information transmission \\ \cline{3-3}  & & Vehicle range extension service (C85) \\ \cline{3-3}  & & Green energy share (C86) \\ \cline{3-3}  & & Carbon emission level (C87) \\ \cline{3-3}  & & Electric power consumption (C88) \\ \hline \multirow{8}{*}{Sustainability (B314)} & \multirow{2}{*}{Energy consumption (B3141)} & Percentage execution \\ \cline{3-3}  
The indicators for \"Operation services\" are all innovative indicators. Their definitions and evaluation explanations are provided in Table S1 (Supplementary Materials).