esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Computing Approaches for Mission Covering Optimization
## 4 Algorithm Implementation
### Scenario 1 (One Constraint)
#### 4.1.2 Qaoah  
In the last section, Lagrange multipliers are used to encode constraints into the QUBO problem. Alternatively, by choosing the appropriate mixing Hamiltonian and initial state, we can constrain the solution space outside of the QUBO formulation in QA  
This initial state used is:  
\[|\psi\rangle=\bigotimes_{(m,r)\in\mathbf{M}\times\mathbf{R}}\begin{cases}|1\rangle&m= U\\ |0\rangle&m\
eq U\end{cases}\quad. \tag{15}\]  
The mixing Hamiltonian describes how to move from the starting state, as well as all subsequent states, such that they are also in the constrained space. To be in the constrained space, only one qubit per column must be active. The Hamiltonian should describe how to cycle a qubit in an active state throughout the column so that it can reach every possible combination of configurations that still satisfy the constraint. The identity operator and the SWAP gates are used for this cycling action. For the three mission examples, we can confirm that an individual column can have each of its possible states reached using three swap gates (see Figure 4).  
Thus, a single resource mixing \(\mathsf{MIX}(r)\) is:  
\[\mathsf{MIX}(r)=\sum_{\begin{subarray}{c}m\in\mathbf{M}\\ m\
eq U\end{subarray}}\mathsf{SWAP}_{(U,r),(m,r)}, \tag{16}\]  
where \((m,r)\) encodes the index of the qubit representing a mapping of resource \(r\) and mission \(m\). The \(U\) in \((U,r)\) represents the unallocated mission. The total mixing operator \(H_{m}\) is the sum of \(\mathsf{MIX}(r)\) on each resource with the identity operation:  
Figure 4: Resource 1’s Mixing Operators: The leftmost segment details the starting state before applying the mixing operator. The second segment from the left shows an identity mixing operator that allows the current state to remain unchanged. The three right-most segments show an operation that uses one swap to cycle the qubit’s on state to missions 1, 2, and 3.  
Figure 3: Example Initial State: All resources are initialized to be unallocated, or allocate to mission \(U\), the last row.  
\[H_{m}=I^{\otimes n}+\sum_{r\in\mathbf{R}}\mathsf{MIX}(r). \tag{17}\]  
In order to embed the mixing operation as a Hamiltonian to run on the IBM machines, it must be described as a composition of tensored Pauli gates. Each SWAP gate can be decomposed in terms of Pauli gates:  
\[\mathsf{SWAP}=\frac{1}{2}(I\otimes I+X\otimes X+Y\otimes Y+Z\otimes Z). \tag{18}\]  
When SWAP is indexed by \(i\) and \(j\), the Pauli gates fall on the \(i\)th and \(j\)th, qubits respectively:  
\[\mathsf{SWAP}_{i,j}=\frac{1}{2}\big{(}I^{\otimes n}+X_{i,j}+Y_{i,j}+Z_{i,j} \big{)}, \tag{19}\]  
\[X_{i,j}=\begin{array}{ccccccc}I&\otimes\cdots\otimes&X&\otimes\cdots \otimes&X&\otimes\cdots\otimes&I&\\ 1&&i&&j&&n&\end{array}. \tag{20}\]  
Each Pauli gate used in Equation (19) (\(X_{i,j}\), \(Y_{i,j}\), \(Z_{i,j}\)) is defined similarly to what is defined in Equation (20). For clarity, Equation (20) places the corresponding Pauli gate only at the specified indices \(i\) and \(j\) in a tensor product of identities \(I\).
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 4 Discussion
### Drone versus Sentinel Sensors and Perspectives  
The Sentinel-1 and 2 satellite constellations in operation provide multispectral imagery at a very high spatial resolution of 10 m. This opens new possibilities for the mapping and multiple annual monitoring of land use and land cover [12; 44]. As shown in Figure 17, these individual sensors may have different potentials for monitoring forest ecosystems. Sentinel-1 radar images do not offer the possibility to distinguish clearings in the middle of a tropical forest, as the radar signal is saturated. Sentinel-2 images can be used to monitor large areas of forest clearing, but their quality is dependent on cloud cover, which is almost always present in tropical forests. Aerial images acquired by drone have strong potential for monitoring forest disturbances. The combination of these three systems should be used in the estimation of forest degradation activities in the reducing emissions from deforestation and forest degradation (REDD+) project.  
The drone-based approach discussed in this study can be improved by integrating Lidar solutions and solutions related to the new generation of planet satellites. Indeed, despite the constraints related to cloud cover, the daily revisit rate of planet microsatellites allows us to obtain cloud-free mosaics with a spatial resolution of 3 m to 5 m [45]. The use of planet images would allow for the refinement of forest disturbance early detection using drone data. Lidar solutions should be tested for the early detection of forest understory loss [46]. This is a major limitation of current remote sensing tools and methods to detect cocoa plots under shade. This would allow for intervention on the ground in the early stages of illegal clearings by cocoa farmers infiltrating classified forests and protected areas. The technologies mentioned above would allow Cote d'Ivoire to have innovative tools adapted to its agroforestry context and for the implementation of its current ambitious sustainable development objectives.
## 4 Concluding Remarks and Future Perspectives  
This review updates the eco-friendly approaches of PGPR application in sustainable agroecosystems as well as provides new insights into the direct or indirect mechanisms of action of these beneficial rhizobacteria involved in biological nitrogen fixation, the solubilization of insoluble minerals, biological control of soil-borne pathogens, stimulation of phytohormone synthesis (e.g., auxins, cytokins, gibberellins, etc.) in plants, the promotion of enzyme activity involved in reactive oxygen species (ROS)-scavenging, and the biosynthesis of 1-amino cyclopropane-1-carboxylate deaminase (ACC deaminase), hydrogen cyanide, antibiotics, siderophore, and volatile organic compounds. Consequently, these mechanisms provide a bridge between PGPR, mainly LAB, and environmentally friendly crop productivity, which leads to sustainable production systems by reducing agrochemical use, improving soil quality, and minimizing environmental pollution. All these beneficial aspects of LAB need to be addressed in future research to plan methodologies to utilize them and/or to combine the use of these PGPR along with other organic or inorganic inputs in sustainable production systems.  
Further work is needed to investigate the environmental sensitivity of LAB to determine how limiting they can be in widespread use. Moreover, a research question that could be asked includes how the competition of LAB with indigenous microorganisms can affect their survival in soils after inoculation. Satisfactory results can be achieved by keeping the bacterial load of the inoculum constant over time. Therefore, future research should focus on the development of efficient microbial formulations that are compatible with conventional techniques, including seed disinfection and pesticide use, to be efficient under various field conditions and soil types and be safe for humans, animals, and plants. Solid and liquid carriers that support the growth and longer viability of microorganisms as alternatives to expensive lyophilization processes have been identified, but each microorganism requires specific growth conditions, and the path to a solution that suits each of them is still a long one.  
Conceptualization, C.C., F.M. and M.Y.K.; writing--original draft preparation, M.Y.K. and S.S.; writing--review and editing, C.C., F.M. and P.F. All authors have read and agreed to the published version of the manuscript.  
This study was carried out within the Agritech National Research Center and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)--MISSIONe 4 COMPONENTE 2, INVESTIMENTO 1.4--D.D. 1032 17/06/2022, CN00000022). This manuscript reflects only the authors' views and opinions; neither the European Union nor the European Commission can be considered responsible for them.  
Not applicable.  
The authors declare no conflicts of interest.
# Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
## 3 Path Loss Measurements and Models
### Path Loss Propagation Models  
There is a general classification of models that requires minimal site or path details and that counts hindrances or obstructions as components of the distance-dependent losses, whereas site-specific models assess the losses due to each hindrance separately. These models are taken into account by placing the measured variables into a generic phrase. Approximately four major path loss propagation models are frequently used: two are single-frequency models, while the other two are multi-frequency models. Closed-in (CI), floating intercept (FI), CI model with a weighted frequency (CIF), and alpha-beta-gamma (ABG) are the acronyms [52; 54; 55; 56; 57; 58].  
Figure 7: Floor plan of the indoor corridor environment [14].  
Models in propagation path loss can be used to reflect the effects of path loss on the signal at the receiving end on a wide scale. It is a useful tool for calculating signal attenuation as it travels from the transmitter to the receiver, taking into account the propagation distance and other factors. The models differ in that some specify the topographical profile for easy signal analysis, while others just use the carrier frequency and distance to determine their targets [69; 70]. The CI, CIF, and ABG (alpha-beta-gamma) path loss models are stochastic in nature, yet they capture the phenomenon of large-scale propagation across a given distance, keeping in mind that they can work at all appropriate frequencies in a given environment. The CI and CIF models are found to be equivalent to the standard forms of 3 GPP path loss models (the FI and ABG models). Only the floating constant and the free space constant, which are dependent on propagation frequency and observance of the free space reference distance of 1 m [71; 72; 40; 73; 74; 75], are relevant in this case.
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Applying a Genetic Algorithm to Elect a Path
#### 3.8.1 Crossover Operation  
We used ordered crossover (OX) in our genetic implementation which was used in BERLIN52, the best known program for TSP so far [11, 38]. Given two parent chromosomes, two random crossover points are selected partitioning them into left, middle and right portions. Then child inherits its middle section from parent1, and its left and right portions is determined by their order and position from the second parent. An example of ordered crossover is shown below:  
Given the following two paths:  
1 st Path = (1 2 3 4 5 6 7 8 9)  
2 nd Path = (4 5 2 1 8 7 6 9 3)  
Based on the used OX genetic implementation [11, 38], the following steps are performed:  
1. Partition each path into three segments (left, middle, right)  
1 st Path = (1 2 3 | 4 5 6 7 | 8 9)  
2 nd Path = (4 5 2 | 1 8 7 6 | 9 3)  
2. Copy the middle segment of both paths, the two candidate paths become as follow:  
1 st candidate path = (- - | 4 5 6 7 | - -)  
2 nd candidate path = (- - | 1 8 7 6 | - -)  
3. Reorder each of the sequences starting from the right segments according to their order in the second path without repeating the already copied numbers (9-3-2-1-8). As 4, 5 7 and 6 are already copied.  
4. Place the ordered sequence into the path starting from the right segment.  
1 st candidate path = (2 1 8 | 4 5 6 7 | 9 3)  
5. Generate new candidate paths as:  
1 st candidate path = (2 1 8 | 4 5 6 7 | 9 3)  
2 nd candidate path = (3 4 5 | 1 8 7 6 | 9 2)
## 2 Materials and Methods
### Dye Decolorization  
Full wavelength (325 nm to 800 nm) scanning of five synthesized dyes was performed using a UV/Vis spectrophotometer to record their maximal adsorbent peaks (Figure S2), which were used as the wavelength values for dye absorbance measurement. Decolorization by bacCTS-MB1 was assessed using a previously described method with some modifications. First, 2 g of each bacCTS-MB1 complex was added to a 10-mL reaction system containing 8.5 mL of 0.1 mol L\({}^{-1}\) sodium acetate buffer (pH 2.5), 0.5 mL of 2 mmol L\({}^{-1}\) CuCl\({}_{2}\) and 1.0 mL of dye at the final concentration of 1.0 g L\({}^{-1}\). Second, the absorbance of each reaction mixture supernatant was spectrophotometrically measured at the maximal absorbance wavelength value of each dye (\(\lambda_{\text{max}}\)) (Table S1 and Figure S2). Activity was expressed as the relative decolorization value, which was calculated as follows:  
\[\text{Relative decolorization value}(\%)=\frac{(A_{0}-A_{f})}{A_{0}}\times 100\% \tag{1}\]  
where \(A_{0}\) denotes the initial absorbance value and \(A_{f}\) denotes the final absorbance value.  
For the ten-round repeated decolorization experiments, 2 g of each prepared bacCTS-MB1 complex was tested for AG25 decolorizing activity (final concentration of 1 g L\({}^{-1}\) AG25) in shake-flask trials in 10 mL of reaction solution at pH 2.5 and 25 \({}^{\circ}\)C with 180 rpm shaking. After each decolorization reaction round, the complex was harvested by centrifugation, repeatedly washed with ddH\({}_{2}\)O and then directly subjected to the next round of decolorization under similar decolorization reaction condition.  
To examine the decolorizing activity of bacCTS-MB1 after a round of decolorization in conjunction with culturing for a generation, the supernatant was removed via centrifugation after a decolorization reaction, repeatedly washed with ddH\({}_{2}\)O until the supernatant no longer discolored, then 100 mL of LB broth were added directly into the flask, following by incubation at 25 \({}^{\circ}\)C for 4 h with 200 rpm of shaking. This procedure was not conducted under strict aseptic conditions. A next-round decolorization reaction was then carried out under similar reaction conditions. A total of six rounds of decolorization with five rounds of culturing for a generation were conducted. The decolorization activity of the bacCTS-MB1 complex with or without culturing for a generation was measured according to the procedures described above and the relative decolorization value of each reaction was calculated according to Formula (1).
# Design of a CW BISOL RFQ for Three Kinds of High-Charge-State Ions Simultaneous Acceleration
## 1 Introduction  
The BISOL Facility [1] is a large-scale scientific facility jointly applied by Peking University and the China Institute of Atomic Energy Science (CIAES). Its goal is to generate high-intensity radioactive neutron-rich nuclear beams for innovative physics research by using a combination of Isotope Separation On-Line (ISOL) and the Projectile Fragmentation (PF) [2] method. As shown in Figure 1, the facility is based on an accelerator-driven system and a reactor system relying on the existing China Advanced Research Reactor (CARR) at the CIAES. BISOL consists of two main accelerator systems, the high-current deuterium ion accelerator system and the post-accelerator system. The main components of the post-accelerator include the stable beam ion source (SBIS), charge breeder (CB), Low-energy Beam Transport (LEBT), RFQ, MEBT, a superconducting linear accelerator with Quarter-wavelength Resonator (QWR) and Half-wavelength Resonator (HWR), and High-energy Beam Transport (HEBT).  
The post-accelerator accelerates primary radioactive nuclear beams generated by reactor systems or driver deuterium-ion accelerator systems to hit the target to produce secondary extremely neutron-rich nuclear beams for frontier scientific research in nuclear physics. The acceleration efficiency of the primary radioactive beam can be improved by increasing the charge state. However, increasing the charge state by secondary stripping usually results in a decrease of two orders of magnitude in the beam intensity. In order torecover the beam intensity, the post-accelerator plans to accelerate three kinds of ions with different charge-to-mass ratios simultaneously in the low-energy section and 3-5 kinds of ions with different charge-to-mass ratios simultaneously in the high-energy section.  
In order to reduce the output emittance of the RFQ and shorten the vane length, a new design idea is proposed: redesigning the RFQ with a multiple harmonic buncher (MHB) in LEBT and comparing it to the scheme without an MHB proposed in 2018 [3]. Now, several heavy-ion CW RFQs have been designed, constructed, commissioned and even put into operation internationally. Although good results have been obtained for high-power testing and beam commissioning, such as SSC-LINAC RFQ [4], RISP RFQ [5], ATLAS RFQ [6] and FRIB RFQ [7], compared with the pulsed RFQs, long-term stability of the CW RFQs requires low sparking frequency and small deformation of the structure, which is a higher requirement for dynamics and structural design. The BISOL post-accelerator CW RFQ must have a higher shunt impedance to lower energy consumption and a better water-cooling structure to take away the heat lost in the cavity in time to avoid large thermal deformation. In this paper, we will present the new design scheme for the RFQ accelerator.
## 8 Discussion
### Gap 3: Stakeholders and Standardization  
The interviews indicated two main stakeholders for a buy-in regarding CLC and other novel security technologies: upper management and standardization bodies. The management segment must have a buy-in in order for the organization to consider evaluating new technology, and the standardization bodies are crucial for the market since they seem to be very dependent on these bodies. Therefore, even if adequate cryptography competence is gained within the organization, the persuasion phase is crucial--as can be seen from previous studies where top management support has been shown to be key for managing information security in practice [80]. No data from our study indicate whether there is a natural dependency between adequate competence in cryptography and strong buy-in of stakeholders; but, drawing on previous studies on differing risk perceptions, we can suspect these are independent since a buy-in may be more volatile due to individual preferences. Or put differently, knowing about does not necessarily translate into an intent to do [81]. Moreover, the literature analysis suggests that the nomenclature, notation, and grouping of schemes is not fully harmonized; thus, potentially leading to even slower adoption due to a lower level of comprehension. As pointed out by [PERSON] et al. [78] from the literature analysis, future work in investigating notation standards as in what type of CLC solutions there are with regards to type of application scenario, is suggested. Indeed, lacking a common language among information security practitioners have been noted as a major factor that slows down progression within the field [82]. Similar to cybersecurity in general, the importance of harmonization and standardization is crucial [83], we thus hypothesise that an increase in harmonization activities could help to minimize this gap.
# Comprehensive Effects of Atmosphere and Soil Drying on
###### Abstract  
The soil water supply and atmospheric humidity conditions are crucial in controlling plants' stomatal behavior and water use efficiency. When there is water stress caused by an increase in saturated water vapor pressure (VPD) and a decrease in soil water content (SWC), plants tend to close stomata to reduce water loss. This affects the gross primary productivity (GPP) and evapotranspiration (ET), subsequently leading to changes in water use efficiency (WUE) and carbon use efficiency (CUE) in plants. However, land-atmosphere interactions mean that water vapor in the atmosphere and soil moisture content causing water stress for plants are closely related. This study aims to compare and estimate the effects of VPD and SWC on the carbon cycle and water cycle for different plant functional types. Based on the fluxnet2015 dataset from around the world, the WUE and CUE of five plant functional types (PFTs) were estimated under varying levels of VPD and SWC. The results showed that high VPD and low SWC limit the stomatal conductance (Gs) and gross primary productivity (GPP) of plants. However, certain types of vegetation (crops, broad-leaved forests) could partially offset the negative effects of high VPD with higher SWC. Notably, higher SWC could even alleviate limitations and partially promote the increase in GPP and net primary production (NPP) with increasing VPD. WUE and CUE were directly affected by Gs and productivity. In general, the increase in VPD in the five PFTs was the dominant factor in changing WUE and CUE. The impact of SWC limitations on CUE was minimal, with an overall impact of only \(-0.05\mu\)mol/\(\mu\)mol on the four PFTs. However, the CUE of savanna plants changed differently from the other four PFTs. The rise in VPD dominated the changes in CUE, and there was an upward trend as SWC declined, indicating that the increase in VPD and decrease in SWC promote the increase in the CUE of savanna plants to some extent.  
stomatal behavior; water use efficiency; carbon use efficiency; water vapor pressure; soil water content 2023 2023 15 1675. [[https://doi.org/10.3390/w15091675](https://doi.org/10.3390/w15091675)]([https://doi.org/10.3390/w15091675](https://doi.org/10.3390/w15091675))  
1
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 3 Energy Efficiency Analysis of the Proposed Nanosecond Marx Generator
### Charging Loss  
The isolation resistors \(R_{\mathrm{c}}\) consume energy whenever there is current flowing through them, as shown in Figure 2. The energy loss comes mainly from these isolation resistors. The energy loss during charging can be calculated as Equation (5).  
\[P_{\mathrm{Rc}}\ =\ \frac{N}{T}\int_{0}^{T}\Bigl{(}R_{\mathrm{c}}I^{2}\Bigr{)}dt \tag{5}\]  
\[I^{\prime}=\frac{|I(\mathrm{t})|}{N\cdot n} \tag{6}\]  
where \(T\) is the charging time. \(N\) is the number of stages in the Marx generator and the number of storage capacitors. \(N\) is the turns ratio of the transformer. \(I^{\prime}\) represents the branch current flowing through each storage capacitor \(C_{\mathrm{N}}\) during the charging period.  
The average current \(I_{\mathrm{a}}\) is used to compare the energy \(W_{\mathrm{Rc}}\) consumed by \(R_{\mathrm{c}}\) and the energy \(W_{\mathrm{c}}\) stored in capacitors \(C_{\mathrm{N}}\) in the charging period. Ignore \(R_{\mathrm{L}}\) (\(R_{\mathrm{L}}\)\(<<\)\(R_{\mathrm{c}}\)), at this time, \(I_{\mathrm{a}}\) represents the average charging current on each energy storage capacitor \(C_{\mathrm{N}}\).  
\[I_{\mathrm{a}}\ =\ \frac{I_{\mathrm{avg}}^{*}}{N}\ =\ C\frac{du_{\mathrm{c}}}{dt} \tag{7}\]  
\(W_{\mathrm{Rc}}\) and \(W_{\mathrm{c}}\) can be calculated by Equations (8) and (9). When \(T>2R_{\mathrm{c}}C\), \(W_{\mathrm{c}}\) will be higher than \(W_{\mathrm{Rc}}\), and the charging energy efficiency of the storage capacitor will be higher than 50%. In this case, the charging efficiency is greater than that of the DC source. However, many practical problems were not considered, so the specific situation needs further experimental exploration.  
\[W_{\mathrm{Rc}}=\ Nl_{\mathrm{a}}^{2}R_{\mathrm{c}}T \tag{8}\]  
\[W_{\mathrm{c}}\ =\ \frac{1}{2}Ncu_{\mathrm{c}}^{2}\ =\ \frac{Nl_{\mathrm{a}}^{2}T^{2}} {2C} \tag{9}\]  
Figure 5: The nanosecond pulse generator using isolating inductors (circuit II).