esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Different Responses of GPP and SIF to the Environment
When environmental conditions are not suitable for vegetation growth (low temperature or drought), the vegetation adjusts its strategy of light energy utilization by adjusting light energy partitioning to avoid light damage in the photosystem, which could lead to the relative changes in the efficiency of converting per unit photon into GPP and SIF [4; 51]. Consistent with our results, the slopes of SIF light response curves were higher than those of GPP in lower EF and Ta bins (SIF increased faster than GPP in lower EF and Ta), which indicated that the efficiency of converting per unit photon into SIF was higher than that of GPP, in order to dissipate light energy quickly. With the improvement of EF and Ta, temperature and water status became increasingly suitable for vegetation growth. As shown in our results, the slopes of GPP light response curves were close to or higher than those of SIF, which indicated that vegetation may try to seek the maximum photosynthesis efficiency under suitable conditions. The above-mentioned points illustrate that the dynamics in the slopes of SIF light response curves may be induced by the regulation of photosynthesis, related to EF and Ta to some degree. Consistent with our hypothesis, we found that the importance of GPP/PAR (SW) ratio was higher than that of Ta and EF in the SIF/PAR (SW) ratio (Figures 8 and 9), which indicated that the response of SIF to incident radiation was more regulated by photosynthesis, rather than directly influenced by environmental parameters (EF and Ta). The divergent and unstable relationship between the light use efficiency of GPP and SIF has been reported in many studies [52; 53; 54; 55; 56; 35], largely because SIF is not the only pathway of energy excitation regulated by photosynthesis [18; 19; 20]. Non-photochemical quenching (NPQ) is another important energy excitation pathway regulated by photosynthesis, which may be the reason why the importance of the predicator-SIF/PAR (SW) ratio in the GPP/PAR(SW) ratio prediction was lower. The byproducts of photosynthesis regulation include not only SIF but also NPQ, which further implies that only using SIF to characterize the light use efficiency of photosynthesis may not be a better option.
# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
###### Abstract  
Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance.  
multi-UAV; area coverage; flight path planning; Voronoi graph; channel and spatial attention mechanism +
Footnote †: journal: Applied Sciences  
**2024, 14**, 7844. [[https://doi.org/10.3390/app14177844](https://doi.org/10.3390/app14177844)]([https://doi.org/10.3390/app14177844](https://doi.org/10.3390/app14177844))
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 3 \(\mu\)TSN-CP Architecture
### ILP Calculator Microservice  
The ILP Calculator microservice is in charge of the Integer Linear Programming (ILP) solver. ILP is a mathematical optimization and feasibility programming method in which a set of constraints (represented as linear inequalities) and a linear optimization function describe a problem [22]. The variables used in the definition of the linear constraints create a space of solution that is filled with the set of possible solutions that respect the constraints. The larger the number of variables and constraints are, the more complex the solution space is and the harder it is to get a solution to the scheduling problem. In \(\mu\)TSN-CP, all of the characteristics of the TAS scheduler are included in the mathematical model. We have adopted the ILP model described in [2], considering the values of \(c_{1}=1\) and \(c_{2}=0\) in the objective function, thus taking into account only the optimization of the excess queues.  
In an ILP program, there are two pieces, namely the model itself (implemented with Python3 using Pyomo [23], in our case) and the mathematical solver (installed in the same Docker container as the model). There is a vast amount of solvers available, and some of them are open source. Table 1 lists some of the most used ILP solvers available, including a small description and the type of license.  
It is important to remark that the solver does not maintain any relationship with the Pyomo code, and changing it is as easy as changing a line in the code. We decided to use GLPK as it is an open-source implementation, and Gurobi due to the transient nature of microservices and Docker containers (i.e., they can be replaced for another container when they present an error). Both solvers are used in the performance tests, as described later.  
Regarding the architecture of the microservice itself, shown in Figure 6, its main parts are the ILP module, described above, and the Solution Visualizer. The Solution Visualizer is a module whose principal task is to facilitate the exploration of the solution that the ILP has provided to the current scheduler problem. Some examples of the visualizer are shown in Section 4.  
Figure 6: Overview of the ILP Calculator microservice.
## 2 Methodology
### Case Studies  
In this study, the novel system has been tested numerically considering the registered weather conditions during November 1 st to 10 th 2017 (Figure 3) in the experimental set-up of Puigverd de Lleida [32] which is classified as Csa according to Koppen-Geiger [33]. Furthermore, the performance of the dynamic PCM technology has been analyzed in the south wall of the following different constructive systems:  
* Wall 1: Typical Mediterranean constructive system based on two layers of bricks and 5 cm of mineral wool in between.
* Wall 2: Case-600 building prototype from ASHRAE Standard 140-2011 [29].
* Wall 3: Ideal envelope, which is based on a single layer of insulation surrounded by a polymeric sheet with and without PCM.  
The three selected morphologies of the building envelope represent different levels of thermal storage capacity and thermal resistance. Without considering the PCM layer, Wall 2 presents a storage capacity of 156.1 kJ/m\({}^{2}\cdot\)K, while Wall 1 presents more thermal mass and can store 200.7 kJ/m\({}^{2}\cdot\)K. On the other hand, Wall 3 is analyzed as a case study with minimum storage capacity (10,4 kJ/m\({}^{2}\cdot\)K) other than the one provided by the use of PCM. Regarding the thermal resistance, Wall 2 presents a thermal transmittance in steady state (U-value) of 0.22 W/m\({}^{2}\cdot\)K, while U-value of both Wall 1 and Wall 3 is 0.66 W/m\({}^{2}\cdot\)K. The layer distribution, thickness of each layer, and thermo-physical properties of used materials are given in Table 1 for each tested constructive system.  
Furthermore, the specific heat capacity of the PCM layer presents a heat storage capacity of 2,000 J/kg\(\cdot\)K outside the phase change range (solid and liquid sensible region), and an enthalpy of fusion of 200 J/g, similar to those available in the market. It might occur that the amount of PCM able to be added in the polymeric sheet is limited, therefore this study will evaluate the influence that the amount  
Figure 3: Weather conditions during the analyzed period.  
of PCM (0%, 25%, 50% 75%, and 100% w/w) has in the performance of the static (conventional system in which PCM is always placed in the layer facing indoors) and the proposed dynamic system.  
For the proposed amount of PCM (5.6 kg of PCM/m\({}^{2}\)), the current design suggests the use of an electrical motor (100 W) for 2 s for changing the position of the PCM layer, which means a daily electrical consumption of 400 J.
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 2 Failure Mechanism of High-Speed Solenoid Valve and Modeling
### Failure Mechanism  
There are many factors that cause failure of the solenoid valve, such as failure of sealing parts and aging of the coil and the sealing parts [28]. In fact, the high-speed solenoid valve in the braking  
Figure 1: The structure of the high-speed switching solenoid valve.  
system of high-speed train mainly has two kinds of failure: aging of coil and drying up of lubricant. Their influences on solenoid valves are shown in Table 1.  
Aging of the coil will reduce the coil resistance of the solenoid valve and increase the stable current. Dried lubricant will increase the resistance to the spool and slow down the movement speed.
# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
## 2 Introduction to the Instruments  
FY-3E MWHS-2 has 98 fields of view (FOVs), with a horizontal spatial resolution of 16 km at nadir points and a swath width of 2700 km. The instrument adopts the cross-track scanning mode. The Earth sense scanning angle is between \(\pm\)53.35\({}^{\circ}\)\(\pm\) 0.1\({}^{\circ}\) with a scan cycle of 8/3 s. The antenna pointing accuracy is \(\pm\)0.8\({}^{\circ}\), and the nadir pointing error is less than 0.2\({}^{\circ}\). The instrument adopts 360\({}^{\circ}\) variable-speed scanning by default. Moreover, uniform scanning and fixed-angle observation according to command are also achievable. The periodic two-point calibration method is still adopted for on-orbit calibration. The parameters of the 15 channels are listed in Table 1. Channels 1 and 10 are the two window channels for monitoring background microwave radiation and precipitation. Channels 2 to 9, situated near the oxygen absorption line of 118.75 GHz (hereafter referred to as the 118-GHz channel for simplicity), are the unique channels for MWHS-2. In contrast to the oxygen absorption line near 60 GHz, the 118-GHz oxygen absorption line is more sensitive to clouds and precipitation and can be used for vertical temperature detection as well as the retrieval of cloud and precipitation parameters [26]. Channels 11 to 15 centered on the water vapor absorption line at 183.31 GHz are designed to obtain the distributions of atmospheric humidity at different layers of the troposphere. Both the 118-GHz channels and water vapor absorption channels are set as double-sideband channels to improve detection sensitivity [27]. Although inheriting the technical system of the Batch 2 Type II microwave humidity sounders, FY-3E MWHS-2 is different from the humidity sounder, also named MWHS-2, that is carried by FY-3C/3D in channel setting, sensitivity and calibration accuracy. Specifically, the frequency is changed from 150 GHz to 166 GHz for Window Channel 10; except for Channel 2, the other channels have a nearly twofold increase in sensitivity, and the calibration accuracy (the expectation for sensitivity after calibration in instrument design, representing the minimum temperature difference that can be resolved by the instrument) is improved as well [1]. More technical details of the instrument can be found on the website of Fengyun meteorological satellite [28].
## 3 The Impact of the Electromagnetic Interference on Drones  
The electromagnetic environment in which UAV systems of different purposes perform their prescribed tasks is different. Therefore, the electromagnetic environment is the basis for proposing and determining the electromagnetic compatibility requirements of UAV systems. The sources of electromagnetic interference suffered by UAV systems can be divided into electromagnetic interference within the system and electromagnetic interference outside the system [16]. Table 1 shows the sources of UAV electromagnetic interference and coupling methods.  
For the electromagnetic coupling between the low-frequency analog signal port and the power port, an appropriate electromagnetic interference filter can generally be effectively suppressed. The electromagnetic coupling of radio frequency ports and digital signal ports is very complicated.
# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 3 Results and Analysis
### Navigation Based on Multiple Travel Modes and Predicted Pedestrians' Positions  
#### 3.3.1. Comparison of Navigation with Multiple Travel Modes and with the Travelable Mode Only  
In this scenario, two cases are compared, as follows: A simulated robot with and without the follow mode. In the case without the follow mode, the robot selects a trajectory that bypasses the front pedestrian (as shown in Figure 11a). Since the velocity of the robot is close to that of the pedestrians, the robot gradually approaches the goal position along the detour, and cannot reach the goal position until the crowd passes the goal position (as shown in Figure 11c). In the case with the follow mode, the robot applies our proposed method to select the pedestrian wearing the blue costume to follow (as shown in Figure 11a), and keeps following him until reaching the goal position (as shown in Figure 11c). In the first case, the average velocity of the robot is 1.02 m/s and the evaluation function value is 50.1. In the second case, the average velocity of the robot is 1.2 m/s and the evaluation function value is 60. This proves the necessity and effectiveness of the following mode, which is included in our approach.  
Figure 10: The robot’s navigation process of avoiding two pedestrians with TEB and CGP planners: (**a,b**) the robot selects the trajectory of \(G\)/\((p_{1})\) and \(G\)/\((p_{2})\); (**c,d**) a sharp return happens with TEB planner, however, no sharp return happens with CGP planner.  
In the second scenario, two cases are compared: The Patrobot navigation with multiple travel modes and with the travelable mode only. The PatrolBot is commanded to navigate to the goal position and it encounters two pedestrians with walking directions similar to its path. Figure 12 shows the robot's navigation process. When navigating with multiple travel modes, the robot cannot find a safe travelable trajectory at first. Then, it selects one pedestrian as the target to be followed after the following evaluation, and switches to the low-speed-follow mode. Finally, it reaches to the goal position. By contrast, when navigating with the travel mode only, the robot continuously searches for the safe travelable trajectory. If there is no safe travelable trajectory, it will explore other trajectories randomly to detour the pedestrians, as shown in Figure 12c. The PatrolBot collides with the wall and adjusts its trajectory again in the exploration process, as shown in Figure 12d-f. Finally, it reaches the goal position when the pedestrians disappear, Figure 12h. The average velocity of the robot with multiple travel modes is 0.45 m/s and the evaluation function value is 22.5. While the average velocity of the robot with the travelable mode is only 0.31 m/s and the evaluation function value is \(-\)84.5. This proves the necessity and effectiveness of the proposed multiple travel mode.
# Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
## 2 Materials and Methods
### Datasets
#### 2.1.2 Caliop/calipso  
CALIPSO is a joint NASA-Centre National D'etudes Spatiales (CNES) satellite mission designed to provide measurements aimed at improving our understanding of the role of aerosols and clouds in the climate system. The CALIPSO satellite was launched in April 2006 together with the CloudSat satellite. CALIPSO and CloudSat fly in close formation as part of the A-Train satellite constellation. The satellite carries CALIOP as a primary instrument, which provides detailed profile information of cloud and aerosol particles and their corresponding physical parameters. CALIOP measures high-resolution profiles (1/3 km in the horizontal and 30 m in the vertical at 532 nm in low and middle troposphere) of the attenuated backscatter by aerosols and clouds at visible (532 nm) and near-infrared (1064 nm) wavelengths along with polarized backscatter in the visible channel [20]. Accurate aerosol/cloud heights and extinction coefficient profiles are derived from the total backscatter measurements. The depolarization measurements enable the discrimination between ice clouds and water clouds and the identification of non-spherical aerosol particles. Additional information, such as the estimates of aerosol particle size, is obtained from the ratios of the signals obtained at the two wavelengths.  
CALIOP provides information for layers down to the level in which the LiDAR signal is fully attenuated even in the case of multi-layer clouds, which occurs at the cumulative optical depth of 3-5. However, the interpretation of CALIOP measurements is often limited by a low signal to noise ratio when using the original high-resolution profiles. Therefore, measurements are usually averaged over several LiDAR shots to allow for a higher confidence in the correct detection and identification of cloud layers. CALIOP products are retrieved from the NASA Langley Atmospheric Science Data Center (ASDC: Hampton, VA, USA; [[http://eosweb.larc.nasa.gov](http://eosweb.larc.nasa.gov)]([http://eosweb.larc.nasa.gov](http://eosweb.larc.nasa.gov))) and defined in three different versions with respect to the along-track resolution: 333 m (individual FOVs), 1 km, and 5 km. In this study, LiDAR Level-2 5-km Cloud Layer information (Version 3) was used considering the confidence in addition to the corresponding SEVIRI product resolution, which is 3 km at the nadir and becomes larger towards higher latitude regions. The highest level CTHs were used in this comparison study, although CALIOP provided the cloud layer top altitudes in the case of multi-layer clouds.
# Unidirectional Invisibility Induced by Complex Anti-Parity-Time Symmetric Periodic Lattices
## 2 Complex Anti-Parity-Time Symmetric Lattices  
We considered a one-dimensional complex anti-PT symmetric lattice consisting of 10 periodic unit cells, embedded in a homogeneous background, such as in air, shown in Figure 1. The dielectrics A and B were arranged alternately, forming a periodic structure. The structure can also be noted by (AB) \({}^{N}\), where the periodic number is \(N=10\). The thickness of dielectrics A and B were \(L=125\)\(\upmu\)m. The permittivities of the dielectrics were \(\varepsilon_{a}=0.1+0.01i\) and \(\varepsilon_{b}=-0.1+0.01i\), respectively. Based on the relation \(n=\varepsilon^{1/2}\), the corresponding refractive indices were \(n_{a}=0.3166+0.0158i\) and \(n_{b}=0.0158+0.3166i\), respectively.  
Figure 1: Schematic of the one-dimension complex anti-parity–time (PT) symmetric photonic crystals (PCs), where the complex refractive indices were distributed in \(n(z)=-in^{\star}(-z)\). For the primitive unit-cell layers A and B, the thickness was \(L=125\)\(\upmu\)m and the complex refractive indices were \(n_{a}=0.3166+0.0158i\) and \(n_{b}=0.0158+0.3166i\), respectively. The imaginary part was positive for gain material and negative for loss material.  
Here we assumed that the input intensity of light was low enough and ignored the nonlinear effect. The materials were treated to be uniform and isotropic. As a light beam impinged upon the system, the reflection and transmission coefficients were denoted by \(r\) and \(t\), respectively. The two coefficients could be obtained by the transfer matrix method (TMM) [37; 38]. The transmittance \(T\) was identified as \(T=tt^{*}=I_{l}I_{l_{l}}\), where \(I_{t}\) and \(I_{i}\) are the transmitted and incident intensities, respectively. The reflectance \(R\) was identified as \(R=rr^{*}\).
# Cooperative Reception of Multiple Satellite Downlinks
## 3 Results and Discussion
### The Best Stream Replacing the Worst One  
The best stream replacing the worst stream could be also implemented to improve the results even more. After calculating the BER of all received streams, the highest BER stream was replaced with the lowest BER stream (i.e., the best quality stream replacing the worst quality stream as demonstrated in Figure 8b). Eventually, the combining algorithm combined all versions based on doubled detection weight for the best quality stream and no weight for the worst version which improved the results even further.  
Figure 8: Illustrations of the SP algorithms: (**a**) worst excluded, (**b**) best replacing the worst, (**c**) best always tailed.  
Figure 7: BER receive diversity combining of 14 sites with SNRs ranging between (0–13) dB: (**a**) linear scale, (**b**) logarithmic scale.