esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Meshing Strategies for Curvilinear Meshes Aligned with the Preferential Directions of the Problem
### Meshing Algorithms  
A Mesh Generator has been specifically built to tackle the generation of Magnetic Field Aligned Meshes. The tasks performed by the Mesh Generator are as follows. First, the magnetic field in the selected simulation domain is obtained. Then, the magnetic variables \(\lambda\) and \(\sigma\) are obtained by integrating over an initial structured Cartesian auxiliary mesh. Once the values of the magnetic variables are computed, the Generator selects a number (chosen by the user) of iso-levels (contour levels). Finally the MFAM mesh information is stored by elements, facets, nodes, etc.  
Two main concerns arise when building the MFAM. First, as the magnetic field is not constant in the domain, picking the contour levels of \(\lambda\) and \(\sigma\) to ensure that lines are properly spaced requires careful consideration. This is specially true whenever singular points (\(\left|\overrightarrow{B}\right|=0\)) or local minima appear in the domain. At those points, because of low magnetic field strength, the variation of \(\lambda\) and \(\sigma\) around these regions is smaller than in regions of high magnetic field strength leading to large elements. The solutions proposed in this work is to add dedicated contour levels in order to avoid excessively large mesh elements. Second, the mesh is determined by the topology of the magnetic field, with the user only being able to adjust the number and which contour levels are picked. This implies that there is little control over the points of intersection of the isolines, and in particular in the intersection of the isolines with the domain contours. This may lead to low quality boundary elements if it is not carefully treated.
## 2 Comparison between Active and Passive Networks
### Planning, Design, and Operation of Active Distribution Networks  
The need to integrate DERs into distribution networks (as opposed to just connecting them thereto) can be singled out as a pivotal driver for active distribution networks. As the penetration of DERs in the distribution network grows, so does the need to consider their impact on the network, from the planning right through to the design and operation of the network [2].  
While the operational objectives of the distribution network (in the traditional sense, as discussed above) have hardly changed, the environment within which they have to be realized has done so, and this is a key motivating factor behind the concept of active distribution networks. Present and future distribution network planning, design, and operation (amongst others) are expected to be influenced by the liberalization of the electricity markets, technological advancements, new requirements for efficiency, security, and quality of power supply, changing consumer needs and expectations and distributed energy resources (DER) integration. Table 1 below shows the major differences between passive and active distribution networks as they relate to their degree of automation, controls, communications, reliability, security, and a few others. It is seen that the ADN possesses quite a lot of advantages when it comes to all the factors considered.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors Considered** & **Passive Distribution Network** & **Active Distribution Network** \\ \hline Degree of automation & Very little & Omnipresent \\ \hline Control Philosophy & Local control & Integrated \\  & & Hierarchical \\ \hline Advanced distribution & Not applicable & Ability to analyze multiple applications in parallel. \\  & & \\ \hline Modeling communication & & \\ networks & Not applicable & Assessment of dependence on different telecom \\  & & performance \\ \hline Reliability & Predictable & Several points of failure on DG and equipment. \\  & & \\  & & \\ \hline Demand-side integration & Contribution of large customers to & Probabilistic based models \\  & system peak & Multiple participation \\ \hline  & & Multiple DG models \\  & & Energy forecast \\  & & Several control models \\  & & Accurate short circuit model \\ \hline Security & Little security and privacy concerns & Multiple security and privacy challenges \\ \hline Scalability & Little option for network expansion & It can expand fast and support a large number of \\  & & connectivities \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major differences between passive and active distribution networks [18].
# Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment
## 2 Image Guided Medical Procedures
### Image Guided Surgical Interventions  
Intraoperative imaging has fashioned a necessity to elaborate medical tools that satisfy the requests of diverse imaging techniques. Imaging backgrounds are demanding and they intensely influence the shape of the utensils used there. Advances in image resolution and disjunction capability have made interventions possible during the imaging procedure. 3D-imaging technique provides actual faithful descriptions of the human tissues while the instrument is being operated within a specified space, by tracking the coordinates of the image. In IG intraoperative procedures and MI-NI SI, MRI is increasingly more utilized due to its consistency, precision, and security. Due to its superior ability to differentiate tumor tissue from normal tissue, MRI is employed in SI for biopsies or tumor abstractions, for example [72; 73; 74; 75; 76; 77; 78]. The employed tools should be compatible with MRI. This compatibility has steered advancement of novel materials adapted for such tools. The use of an MRI-compatible robot to facilitate the approach to the body tissues inside the imaging scaffold permits the patient to remain within the scaffold during the entire SI time. Such IG association permits an important reduction of the SI duration, a higher SI accuracy, and faster recovery progression. Currently, the more accurate the IG association, the more recognizable the MRI-compatible strategies will be. Figure 3 schematically summarizes the representation of IG-SI, involving the interventional device in the MRI scaffold, the surgical data (action and location), the MRI imaging processing, and the device control.
## 4 Feasibility Test of the API Protocol via Simulations
### Performance Analysis of the API Protocol
#### 4.2.3 Message Overhead  
The one-to-one initialization between a master and a slave node takes up to two messages during one sub-round. The number of messages of a sub-round is 1 when the master node sends an initialization request message, but the slave node cannot receive the message. On the other hand, the number of messages is 2 when the slave node successfully receives the initialization request message, and it sends back an initialization response message to the master node. If the channel condition is good, two messages are transmitted in a sub-round. Otherwise, only one message is sent.  
As there are \(Q_{k}\) sub-rounds in \(R_{k}\), the maximum number of messages that can occur in the round \(R_{k}\) is \(2\times Q_{k}\), and the minimum number of messages in \(R_{k}\) is \(Q_{k}\). We define the message overhead as the number of all messages sent until the initialization is finished. Thus, the message overhead can be obtained by adding up all the generated messages up to the round of successful initialization. If this is generalized to the case where the initialization is completed in the \(k_{th}\) round \(R_{k}\), the message overhead can be calculated by \(\sum_{j=1}^{k}\sum_{i=1}^{Q_{i}}M_{ji}\), where \(M_{ji}\) is the number of messages sent in the \(i_{th}\) sub-round in the \(j_{th}\) round such that \(M_{ji}\) is 1 or 2.  
Using the message overhead calculation formula, the message overhead can also be expressed as the number of sub-rounds. This implies that the linearity of the initialization time according to the initialization rate can also be applied to the message overhead performance. The linearity is verified, as shown in Figure 10, where the API shows the linearity of performance according to the initialization rate, provided that the BER is not high enough to cause communication failures. From the results in Figure 10, it can be concluded that the performance pattern of the initialization time and message overhead according to the initialization rate and BER is similar.  
After comparing the message overhead of the API to that of the FI, it is confirmed that, as the initialization rate increases, the message overhead of the API approaches that of the FI. In both the API and the FI, the message overhead performance deteriorates as the BER increases, and the performance gap between the API and the FI also becomes larger. This implies that the larger the BER and the higher the initialization rate, the more messages occur during initialization.  
In conclusion, it is verified through our simulations that the API outperforms the FI with respect to both initialization time and message overhead. Furthermore, the performance difference between  
Figure 10: The message overhead of the API and FI according to BER and the initialization rate.  
the API and the FI becomes remarkable when an application is run with a low initialization rate in a poor channel condition.
## 2 Descriptions of HEAGV with Series Hybrid Powertrain  
This study was conducted on a HEAGV with a series hybrid electric propulsion system, as shown in Figure 1. HEAGV has two modes (flight mode and ground mode), which can be switched freely. The series hybrid power system comprises a turboshaft engine, battery packs, a generator, four hub motors, and sixteen rotor motors. The turboshaft engine is completely decoupled from the drive system and can run under its best working conditions to achieve high efficiency. The engine generator set and battery packs are power sources for the hub and rotor motors. Eight pairs of coaxial rotors are used to generate lift in the flight mode of the HEAGV. In ground mode, the HEAGV is propelled by hub motors like a vehicle. The parameters of HEAGV are shown in Table 1.  
Figure 1: Structure of the studied HEAGV and powertrain system.
## 3 Results and Discussion  
### Purslane Powder Characterization  
#### 3.1.1 Surface Microstructure  
Figure 2a-e shows the surface microstructure of freeze-dried unground purslane (leaves and stems) (a,b) and powders obtained after grinding with a knife mill (c), a ball mill (d), and a planetary ball mill (e), as determined via scanning electron microscopy (SEM). The sieves that retained the largest amount of powder were selected for Figure 2.  
Micrographs of the surface microstructure of the unground freeze-dried plant material (leaf and stem) show, as expected, the compact, unbroken structure of the plant material. The micrograph showing the surface microstructure of the stem is visually interesting because it shows a large number of porous, honeycomb-like structures that are certainly formed during the drying process of the plant material. The surface structure of a freeze-dried leaf is rough and uneven, with small clusters and occasional grooves. Micrographs of the surface microstructure of purslane powder show broken, coarse structures with particles of different diameters, depending on the type of grinding technique. The surface microstructure of purslane powder after the knife mill contains sharp cuts with clearly visible edges of the particles and the rather broken structure (shredded). After grinding with a ball mill, the surface microstructure is less fragmented than after grinding with a knife mill, the edges are not as clearly defined, and the plant material is lumpy with some round formations (Figure 2c). The micrograph showing the powder obtained after grinding with a planetary ball mill differs at first glance from the surface structure shown for the other two types of powder. The planetary ball mill produced a powder whose surface structure appears to consist of many fine particles with smooth edges and a very irregular shape corresponding to the bimodal particle distribution in Figure 3.  
Figure 2: Micrographs of the surface microstructure of freeze-dried, unground purslane (**a**) leaf (FPL) and (**b**) stem (FPS), (**c**,**d**) purslane powder after grinding with a knife mill (PPK) on 90 and 710 \(\upmu\)m sieves, (**e**,**f**) purslane powder after grinding with ball mill (PPB) on 63 and 250 \(\upmu\)m sieves, and (**g**) purslane powder after grinding with a planetary ball mill (PPP) on \(\upmu\)m 250 sieve scanned with SEM.
## 4 Simulation, Results, and Discussion  
To evaluate the performance of the VLC system in conjunction with the PSO-enhanced localization mechanism, an indoor hospital scenario was simulated by using computer software(Python 3.7.0). This environment is built based on the graphical sketch shown in Figure 1. The object to be placed in the hospital room, which could be considered a stretcher, for example, is located in the indoor environment that measures (\(5\times 5\times 3\)) m. Within this hospital room, there are three LEDs at known positions, where LED\({}_{1}\) is located at the coordinates (1, 2, 3) m, LED\({}_{2}\) at (1, 4, 3) m, and LED\({}_{3}\) at (4, 3, 3) m. The receiver is positioned above the target, as depicted in Figure 1, and consists of three PDs that point directly to the ceiling, following the suggestion in Equation (12) where \(\phi_{i}=\psi_{i}\). Moreover, these are considered as if they were in the same location within the space. It is important to note that each PD receives power from a single LED.  
On the other hand, the selection of the PSO parameters was performed through an iterative process that involved the extensive testing of the algorithm and its performance. This involves evaluating various parameter settings to identify the one that produces the best results. This methodological approach involves systematic experimentation with different combinations of parameter values to optimize the behavior of the algorithm based on specific performance metrics and to select the configuration that demonstrates the highest degree of effectiveness according to the predefined criteria to be evaluated [47; 48].  
To verify the system, a height is defined for the receiver and the XY plane is divided into 10 \(\times\) 10 points. This implies dividing the 5 m length of the room into 10 points that are spaced 50 cm apart. The same division is then performed for the width of the room in each of these divisions. This means that 100 points are tested for one specific height. The decision to divide the space into 100 points, each of which positions a particle, is based on the balance between spatial resolution and computational complexity. This methodology allows for uniform coverage of the area of interest while maintaining a manageable number of particles.  
The evaluation of these points is repeated for the four localization methods to be compared: solving Equation (17), which is the conventional method, by using the proposed PSO algorithm, without the PSO algorithm, and a localization algorithm with VLC-enabling technology based on multilevel modulation [49]. Then, this process is repeated by varying the height between 0 and 2.5 m with an increment of 0.1 m for each test.  
To assess the performance of the localization algorithms used, the error is measured as the distance between the estimated and actual positions. This is expressed as follows:  
\[d_{error}=\sqrt{(x_{t}-x_{r})^{2}+(y_{t}-y_{r})^{2}}. \tag{19}\]  
Finally, the technical parameters used for the simulation of the hospital scenario, the VLC channel, and the PSO algorithm are shown in Table 2[42; 43].
_Applied_  
_sciences_  
_Article_  
**The Influence of Effective Microorganisms**  
**on Microbes and Nutrients in Kiwifruit Planting Soil**  
**[PERSON] 1,2,\(\dagger\), [PERSON] 2,3,\(\dagger\), [PERSON] 4,\(\dagger\), [PERSON] 1,2, [PERSON] 5, [PERSON] 5,**  
**[PERSON] 1,2, [PERSON] 5, [PERSON] 5 and [PERSON] 2,5,***  
College of Civil Engineering, Sichuan Agricultural University, Dujiangyan 611830, China;  
[EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (L.J.); [EMAIL_ADDRESS] (L.C.)  
Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village Construction, Sichuan Agricultural University, Dujiangyan 611830, China;  
[EMAIL_ADDRESS]  
College of Environmental Sciences, Sichuan Agricultural University, Chengdu 611130, China  
College of Forestry, Henan Agricultural University, Zhengzhou 450000, China; [EMAIL_ADDRESS]  
Laboratory of Microbiology, Dujiangyan Campus, Sichuan Agricultural University, Dujiangyan 611830, China; [EMAIL_ADDRESS] (G.W.); [EMAIL_ADDRESS] (B.L.); [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (W.D.)  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-28-8712-7472  
These authors contributed equally to this work.  
Academic Editor: [PERSON]  
Received: 5 May 2016; Accepted: 25 May 2016; Published: 2 June 2016  
**Abstract:** To understand the effects of effective microorganisms (EMs) containing multiple strains on microbes and nutrients in kiwifruit planting soil, EMs prepared with four different strains were added to kiwifruit planting soil monthly from April to August. The counts of bacteria, fungi, actinomycetes, and total microbes were determined. The pH, total nitrogen (TN), alkali-hydrolyzable nitrogen (A-N), organic matter (OM), available potassium (A-K), and available phosphorus (A-P) of the soil were measured. Results indicated that the counts of bacteria, fungi, actinomycetes, and total microbes reached 60.33 \(\times\) 10\({}^{5}\), 4.00 \(\times\) 10\({}^{5}\), 0.92 \(\times\) 10\({}^{5}\), and 65.25 \(\times\) 10\({}^{5}\) CFU/g, respectively, in August, all of which were higher than those of the control group (CK). The bacterial count of the experimental group (EG) was higher than that of the CK in August. The pH-values of the EG were always lower than those of the CK. In August, the TN content of the EG was 1.52 g/kg, which was higher than that of the CK (1.35 g/kg). A significant negative association between the actinomycetes count and TN (\(p\) < 0.05) was found. For A-N and OM, the content of the EG (A-N, 125.18 mg/kg; OM, 49.84 mg/kg) was roughly the same as that of the CK (A-N, 112.51 mg/kg; OM, 53.11 mg/kg) in August. However, the A-K and A-P contents of the EG (A-K, 145.25 mg/kg; A-P, 111.25 mg/kg) were lower than those of the CK (A-K, 182.52 mg/kg; A-P, 202.19 mg/kg) in August. Results show that application of EMs in kiwifruit planting soil can increase the counts of soil microbes and might promote the absorption of major nutrients for kiwifruit tree.  
**Keywords:** effective microorganisms; kiwifruit; planting soil; nutrients
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Real Data
#### 3.2.4 Evaluation of Position Estimation  
The performance of the position determination step was evaluated by using a checkerboard pattern as a target and full-resolution images. We used a state-of-the-art checkerboard detection algorithm [52], which is considered to be very accurate. In this manner, we attempted to limit, to the extent possible, the effect of the developed target and marker detection methods on the results.  
This experiment was run on approximately the same camera poses as described above. The distance between the camera and the target was increased to fit the larger checkerboard target in the cameras' fields of view. The results are presented in Figure 17.  
3.2.5 Study of the Effect of Camera Slant in the Original Position on the Overall System Performance  
For the sake of completeness, an extensive analysis was conducted over a more comprehensive set of positions outside of the limits established in Section 3.1. We analyzed a set of 296 images taken at random initial camera positions and orientations.  
In Figures 18-20, we present the results as a function of the characteristics of the initial position: total rotation between camera and target and the horizontal and vertical deviation (Euclidean distance) from the central position and distance between the camera and the target
## 4 Simulation Design of NR-ODBR-DCSK System Based on USRP  
The software versions used in this design are LabVIEW 2021 and MATLAB 2022a, and the USRP device models used are the NI USRP-2920 and the NI USRP-2954R. In order for the LabVIEW software to be able to control the USRP devices, it is also necessary to install the NI-USRP Driver, the LabVIEW-FPGA modulation program and the NI Modulation Toolkit. The host computer communicates with the USRP device over a Gigabit Ethernet cable, and information transmission between the two USRP devices can be performed wirelessly or wired. Wireless transmission is used in this experiment, and the communication distance between the devices is set to one meter considering the limitation of the experimental site. The transmitter and receiver are equipped with two RF antennas of model VERT900, which are connected to TX1 of RF0 port and RX2 of RF1 port of USRP-2920, and TX1 of RF0 port and RX2 of RF1 port of USRP-2954R. Figure 6 shows a schematic of this experiment.  
Figure 6: Schematic diagram of USRP experiment.  
As shown in the figure, the basic flow of the experiment is as follows: first, the modulation side uses MATLAB software to generate the NR-ODBR-DCSK modulation data and saves it as a text file. Subsequently, the saved modulation data are transmitted to LabVIEW software, which processes the received data and sends the signal through the USRP-2954R device at the transmitter side. The signal is transmitted over a wireless channel, adding noise during transmission, and is finally received by the USRP-2920 device at the receiving end. The LabVIEW software at the receiving end is responsible for receiving the signal from the USRP-2920 device and generating a text file. Finally, the text file is transferred to the MATLAB software at the demodulation end for signal demodulation. Figure 7 shows the setup of the experimental scenario.