esa-sceva/satcom-chunk-collection huggingface dataset:
# Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
## 4 The Proposed Algorithm
### Calculating the Similarity Matrix  
In the similarity matrix, each element holds a singular metric quantifying its resemblance to the other elements in the adjacent vicinity (Algorithm 2). The degree of similarity between any two elements augments in direct proportion with the increase in the similarity value. It warrants highlighting that, due to the intrinsic characteristics of the AP method, the similarity function \(S(p_{1},p_{2})\) may not necessarily be identical to \(S(p_{2},p_{1})\). Although this directional feature is not incorporated in the current implementation of our proposed methodology (Figure 4), potential future adaptations of the algorithm may consider its integration to facilitate directional clustering of cells and other specific elements. Utilizing the AP methodology, we classified data points into distinct sub-regions for each robot, following an assessment of the similarity quotient between each pair of data points. The AP algorithm operates through the transmission of messages that denote a data point's proclivity towards a particular cluster. Subsequent to the resolution on the number of clusters and the allocation of data points to respective clusters, these messages are subject to iterative refinement.  
```
1. Input:
2. Environment matrices: binary \(A\), weight \(W\), elevation \(E\), floor \(F\)
3. Significance multipliers: \(t\), \(q\), \(r\)
4. Output: similarity matrix \(S\)
5. Function similarity_calculation\((A,\ X,\ Y,\ W)\):
6. Step 1: Initialize an \(X\) by \(Y\) matrix \(S\)
7. Step 2: Loop through each pair of data points \((x_{1},y_{1})\) and \((x_{2},y_{2})\) in the environment \(A\):
8. Step 2.1: Calculate the distance between the data points:
9. \(d=4\_{\text{neighbor\_actual\_distance}}\ (x_{1},y_{1},x_{2},y_{2})\)
10. Step 2.2: Calculate the elevation difference
11. \(e=\left|E[x_{1},y_{1}]-E[x_{2},y_{2}]\right|\)
12. Step 2.3: Calculate the floor discrepancy
13. \(f=\left|F[x_{1},y_{1}]-F[x_{2},y_{2}]\right|\)
14. Step 2.2: Multiply each metric by its weight factor:
15. \(d=t\times d\)
16. \(e=q\times e\)
17. \(f=r\times f\)
18. Step 2.3: Store the similarity between the data points in the similarity matrix \(S\):
19. \(S[x_{1},y_{1},x_{2},y_{2}]=-(d+e+f)\)
20. Step 3: Return the similarity matrix \(S\)
21. End Function
```  
**Algorithm 2**The procedure that calculates the weighted Similarity Matrix \(S\)
## 4 Conclusions  
In this work, INS, GNSS, LEO, and 5G signal sources were simulated, and integrated navigation simulation experiments were carried out using tightly coupled and federated filtering algorithms. By setting the azimuth angle and the satellite visible altitude angle, the positioning results in four occlusion scenes, namely, Open scene, Semi-occluded scene, Surrounding occluded scene, and Headspace occluded scene, are compared. The main conclusions that the paper can support are as follows: (1) the experimental results show that, after adding the LEO constellation, the geometric configuration of the LEO constellation can significantly improve the accuracy factor, which provides strong support for the positioning effect of the Headspace occlusion scene, and improve the positioning accuracy of the original INS/GNSS by an order of magnitude; (2) after the addition of the 5G ranging signal, due to the uninterrupted characteristics of the 5G ranging signal, the overall positioning continuity of federated filtering was greatly improved; (3) by allocating different scale factors, the experiments show that the federated filtering algorithm can combine sensors with different precision for navigation and positioning, to adapt to the integrated navigation modes in different scenes, and open up a new idea for new sensor integrated navigation.  
The future improvement of this paper lies in that this paper only tests the single point positioning mode with new LEO and 5G sensors based on INS/GNSS. In the future, we can study higher-precision positioning algorithms, including but not limited to RTK positioning and PPP positioning. The data used in LEO navigation and positioning in this paper is the satellite position and velocity obtained by simulating the satellite constellation, and there is no measured data source for verification. The simulation of 5G ranging signal only adopts pseudo-range measurement simulation with noise added based on theoretical value. There  
Figure 20: Attitude error of different mode federated filtering.  
is no contrast experiment with the way of positioning by specifying positioning protocol in communication standard. The difference of positioning accuracy between the two ways has not been specified yet.  
[PERSON] carried out the experiment and contributed to writing the manuscript. [PERSON] helped to simulate satellite constellations. [PERSON] helped to carry out the experiments. [PERSON] created the idea. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 2 Materials and Methods
### Morphologic Analysis of the biofilms by Scanning Electron Microscopy  
Multispecies biofilms exposed to CAP for 1, 3, 5 and 7 min were analyzed morphologically by scanning electron microscopy. After exposure to CAP, the biofilms were prepared by standard fixation protocol 2.5% glutaraldehyde solution in 0.1 M phosphate buffer (pH 7.4) for 24 h and serial dehydration in ethanol (Synth, 99.8%), for 10 min in each concentration [51]. After 24 h under room temperature, the biofilms were metallized in gold. The specimens were analyzed by scanning electron microscopy with Field-emission gun (SEM-FEG). The analysis was performed using a Tescan Vega 3 microscope with accelerating voltage of 10 kV and magnification of 10 k\(\times\). Two fields per membrane were analyzed. The first was located at the central region of the membrane and the other at the peripheral region (approximately 2.5 mm from the center).
###### Abstract  
The objective of this study was to obtain the deep subsurface structure of a saline-evaporitic formation affected by an anticlinal structure, defining the position and thickness for its future exploitation (potassium salts). To improve this knowledge, geophysical methods can be applied to establish detailed information on geological structures at depth. This work analyzes the results of a deep time domain electromagnetic (TDEM) survey acquired over the southern flank of the Barbastro-Balaguer Anticline present in the Ebro Basin in the vicinity of Graus (Huesca, Spain), that controls the geometry of the Barbastro saline-evaporitic formation, of interest from the mining point of view. A deep time domain electromagnetic system (TDEM) is used, providing a penetration capability down to 3.0 km depth. A parametric constrained inversion over a lithological known borehole (Monzon-1) is used to obtain a resistivity model and then applied to the rest of the survey points. The applied methodology contributes to improving the geological knowledge, revealing a new detailed geological structure of the Barbastro saline-evaporitic formation affected by the Barbastro-Balaguer Anticline. The survey shows that the Barbastro saline-evaporitic formation presents a structure with a ramp hanging wall and close to flat football, generally dipping to the SW, with a decreasing thickness from 1103 m to 601 m, in concordance with the previous accepted geological structural model. The new geophysical study provides essential data, allowing design and drilling optimization in future mining exploitations.  
deep time domain electromagnetics; geophysics; mineral exploration; saline deposits; Barbastro Formation
## 4 Results  
### Dataset  
#### 4.1.1 Data Processing  
We developed a benchmark eyeglass frames dataset specifically for this task. Professional designers created most models in the repository using commercial modeling software, and these models are used in real-world production. To train our continuous SDF model, we generated SDF samples \(X\) for each mesh, comprising 3D points and their corresponding SDF values \(SDF(X)\).  
For data preparation, we begin by normalizing each mesh to fit within a unit sphere, ensuring uniformity and scale consistency across different meshes. Subsequently, we sample 500,000 spatial points \(X\), with a higher density of points near the object's surface, to capture finer details of the SDF in that region.  
We begin processing the mesh files by extracting the vertex indices of the mesh faces. Using these indices, we calculate the area of each triangle formed by the vertices. This step is essential for creating a cumulative distribution function (CDF) based on the triangle areas, enabling weighted random sampling. To construct the CDF, we iterate through the mesh faces, calculating the area of each triangle and updating the CDF accordingly. This iterative process ensures that the CDF accurately reflects the distribution of triangle areas, with larger triangles contributing more significantly due to their greater surface area.  
After constructing the CDF, we use a random number generator to sample points from the mesh surface. The CDF guides this random sampling, ensuring that the likelihood of selecting a point from a particular triangle is proportional to the triangle's area. This weighted sampling method increases the probability of sampling from larger triangles, covering a larger area, and ensuring a more accurate representation. Consequently, the distribution of sampled points accurately reflects the mesh surface, capturing both prominent and finer details.
###### Abstract
Most of them first theoretically derive the analytical formula of the received optical signal or received optical power of the two terminals, and then they substitute the correlation function for calculation to obtain the correlation coefficient. Finally, numerical emulation is used to judge the influence of turbulence intensity and other factors on the correlation of instantaneous channel fading [17]. There are few reports on the correlation of instantaneous fading in bidirectional atmospheric turbulence channels under actual conditions [14; 18]. In this paper, a bidirectional transceiving coaxial atmospheric laser propagation device is built, and a hot air convection type atmospheric turbulence emulation device is used as the atmospheric propagation channel for experiments. The turbulence intensity depends on the temperature difference between the upper and lower plates of the turbulence emulation pool, which realizes the channel reciprocity exploration of the bidirectional propagation and reception of coaxial atmospheric laser propagation in the adjustable atmospheric turbulence intensity channel. When the atmospheric turbulence increases from no turbulence to weak atmospheric turbulence and then to strong atmospheric turbulence, the correlation coefficient of the atmospheric channel increases from 0.023 to the maximum of 0.923, and then gradually decreases. Then, the similarity of the instantaneous change trends for these two receiving terminal optical signals, and the consistency of probability density function, indicates that there is a good reciprocity between bidirectional atmospheric turbulence optical channels. In addition, the greater the scintillation factor of the received signal, where the probability density distribution obeys the logarithmic normal distribution, the smaller the scintillation factor of the received signal, and the probability density distribution obeys the normal distribution in the bidirectional atmospheric laser propagation link, which is consistent with the unidirectional laser propagation characteristics in the atmosphere.
## 2 System Model  
The system model is shown in Figure 1. We investigated the use of UAVs for providing remote delivery to areas without internet connectivity (thus also providing DTN connectivity in the process). From the perspective of goods delivery, two main scenarios can occur. In the first scenario, illustrated in Figure 1a, each remote rural area/village has a single offloading zone where the goods can be delivered and where a telecommunication base station (BS) or access point (AP) can be made available for data exchange (offloading data from the UAV to the AP/BS and carrying the stored data destined to the UAV from the AP/BS). In this scenario, it is assumed that the village has a local communications network, but that network is not connected to the backhaul. Users in the village communicate locally with the central BS, where the data are stored for the UAV to collect and carry to the core network in a DTN fashion. For example, readings from Internet of Things (IoT) sensors can be transmitted regularly to the BS, where possibly some edge processing/intelligence can take place. When a UAV visits the area, it collects the stored data at the BS and transmits to the BS any information coming from the core network. The BS would then take care of distributing it locally to the users in the remote village. An example of this scenario is shown in Figure 1a.  
The example in Figure 1 consists of having the UAVs provide DTN connectivity to remote areas while using the nearest railway stations as points for recharging. This solves some practical constraints since the train stations are connected to the power grid and could provide a safe place for the UAVs to be stored and recharged. This can also allow the use of trains to transport the UAVs between stations, e.g., to cover different sets of remote zones on the same day. When the start and end stations are the same, this was modeled in [22] as a Traveling Salesman Problem (TSP), where the UAV has to visit each remote area once during its trip while minimizing the trajectory time, which also minimizes the energy consumed during flight.  
In the second scenario, illustrated in Figure 1b, at least some of the remote rural areas/villages do not have a single offloading zone, e.g., due to having fewer houses that  
Figure 1: DTN connectivity provided by UAVs benefiting from railroad stations as recharge points: (**a**) village with central BS; (**b**) village without central BS.  
are sparsely scattered and relatively far from each other. In that case, the UAV must perform doorstep delivery of the items to their final destinations. Hence, the UAV must still visit each village, but with the absence of a central offload site, the UAV will have to go over each house.
# Numerical Simulation on Backfilling of Buried Pipes Using Controlled Low Strength Materials
## 2 Pipe and Backfill Material Parameters  
### Testing of Pipe Ring Stiffness  
In order to accurately understand the physical properties of the pipeline and provide corresponding parameters for numerical simulation, the ring stiffness of the pipeline was tested. The ring stiffness of PE pipe was measured according to the \"GB/T9647-2003  
Figure 1: Soil arching effect of rigid and flexible pipes in soil.  
Determination of Ring Stiffness of Thermoplastic Pipes\" [18], and the true elastic modulus of the pipe was reversed according to the formula. The test instrument included PE pipes with a length of 300 mm, an electronic ring stiffness machine, etc.  
The operator adjusted the pipe position before pressing the pipe to ensure that the long shaft of the PE pipe is parallel to the machine and placed in the center of the ring stiffness machine. A deformation tester was set up in each specimen to measure the pipe displacement. The specimen was compressed at a speed of 10 mm/min according to the specification until the pipe deformation reaches at least 0.03 d, recording the force value and deformation amount. Schematic diagram of PE pipe ring stiffness test is shown in Figure 2. Equation (1) is used to calculate the ring stiffness of the pipe:  
\[S=\frac{(0.0186+0.025Y/d)F}{LY} \tag{1}\]  
where:  
F--Pressure value (kN) when the pipe is deformed by 3.0%;  
L--specimen length in meters (m);  
Y--pipe deformation (m);  
S--ring stiffness of the specimen in kN/m\({}^{2}\).  
Three identical tubes were taken for testing, and the average of the three was used as the final value of the ring stiffness. The specific test results and summary are shown in Table 1.  
According to the \"Manual of Structural Design of Water Supply and Drainage Engineering\" [19], the relationship between the ring stiffness of PE pipes and the modulus of pipes is shown in Equation (2):  
\[S_{p}=\frac{E_{p}}{12}\left(\frac{t_{0}}{D_{0}}\right)^{3} \tag{2}\]  
The formula for calculating the elastic modulus of the pipeline is as follows (3):  
\[E_{p}=12S_{p}\bigg{(}\frac{D_{0}}{t_{0}}\bigg{)}^{3} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Number** & **1** & **2** & **3** \\ \hline Compressive force value/kN & 1.13 & 1.10 & 1.15 \\ Pipe ring stiffness/(kN/m\({}^{2}\)) & 7.41 & 7.22 & 7.54 \\ Mean ring stiffness/(kN/m\({}^{2}\)) & & 7.40 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ring stiffness of PE pipe.  
Figure 2: Schematic diagram of PE pipe ring stiffness test.  
where:  
\(E_{p}\)--elastic modulus/MPa of pipe;  
\(S_{p}\)--Tube ring stiffness/MPa;  
\(D_{0}\)--Pipe diameter/mm;  
\(t_{0}\)--Calculation of wall thickness/mm of pipe
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phenolic Compound**} & **DPPH\({}^{\bullet}\) Scavenging** & **NO\({}^{\bullet}\) Scavenging** & **Lipid Peroxidation** \\  & **Activity** & **Activity** & **Inhibition** \\ \hline Quercetin & 0.87 \(\pm\) 0.04 \({}^{\text{d,e}}\) & 20.50 \(\pm\) 0.60 \({}^{\text{d}}\) & 2.06 \(\pm\) 0.26 \({}^{\text{d}}\) \\ Myricetin & 0.82 \(\pm\) 0.07 \({}^{\text{d,e}}\) & 20.77 \(\pm\) 0.63 \({}^{\text{d}}\) & 1.68 \(\pm\) 0.10 \({}^{\text{d}}\) \\ Gallic acid & 0.33 \(\pm\) 0.01 \({}^{\text{e}}\) & 12.36 \(\pm\) 0.93 \({}^{\text{e}}\) & 11.27 \(\pm\) 0.90 \({}^{\text{ab}}\) \\ Caffeic acid & 0.62 \(\pm\) 0.04 \({}^{\text{d,e}}\) & 18.79 \(\pm\) 0.42 \({}^{\text{d}}\) & 141.76 \(\pm\) 15.60 \({}^{\text{*}}\) \\ Catechin & 1.34 \(\pm\) 0.01 \({}^{\text{cd}}\) & 31.75 \(\pm\) 0.92 \({}^{\text{c}}\) & 13.05 \(\pm\) 12.2 \({}^{\text{a}}\) \\ Epicatechin & 1.90 \(\pm\) 0.01 \({}^{\text{b,c}}\) & 21.26 \(\pm\) 0.26 \({}^{\text{d}}\) & 10.12 \(\pm\) 0.57 \({}^{\text{b}}\) \\ Resveratrol & 7.56 \(\pm\) 0.52 \({}^{\text{a}}\) & 35.08 \(\pm\) 1.32 \({}^{\text{b}}\) & 5.02 \(\pm\) 0.34 \({}^{\text{c}}\) \\ Trolox & 2.50 \(\pm\) 0.75 \({}^{\text{b}}\) & 60.87 \(\pm\) 1.51 \({}^{\text{a}}\) & - \\ \hline \hline \end{tabular} The data are expressed as mean values of three independent experiments \(\pm\) standard deviation. Mean values displaying different letters within each column are significantly different according to [PERSON]’s multiple comparisons test at a 95% confidence level. - not tested; *: excluded from statistical analysis due to the extremely high IC\({}_{50}\) value.  
\end{table} 
Table 6: Comparative overview of antioxidant effects (IC\({}_{50}\) values, \(\text{\mu g}\)/mL) of the main phenolic compounds from Croatian wine.
## 2 AC Equivalent First Principle Linear Circuit and Experiments
### Experiment of Steady-state DC static Voltage Responses on Battery Terminals  
The purpose of the experiment is to clarify that there are no steady-state DC static voltage responses on batteries' terminals under stable AC sinusoidal current excitation. Nowadays, board-level BMS is used to operate at frequencies as low as 0.5 Hz or 1.0 Hz. So, the electrochemical workstation (Solartron ModulabXM) sends high-precision pure trigonometric (sinusoidal) current-source excitations (5A rms) to the tested battery at two fixed frequencies, 0.5 Hz and 1.0 Hz. The tested battery, manufactured by Zhejiang Narada Power Source Co., Ltd., as shown in Figure 5, is a 500 Ah lead-acid cell with model name GFM-500R and number E-08#. The detection circuit, in Figure 6, is designed to sample battery terminal voltage and to amplify errors between terminal voltage and reference voltage (2.132 V) with DC gain (40.14). The overall configuration of the experimental platform is in Figure 7. AC current injections of the electrochemical workstation are intermittently closed and open, and the outputs of the detection circuit are measured by a digital multimeter (UNI-T UT33A+). When AC current injection is closed, the multimeter display is read at a fixed time delay; when AC current injection is open, the multimeter display is read at the same fixed time delay. After measuring two cycles by the same time delay, length of the next time delay is increased by 30 s.
## 3 Results
### Results for the IEEE 30-bus system  
Similar results are also observed for the IEEE 30 bus system. In Figure 8, the total real (\(Pa\)) and reactive (\(Pr\)) power losses desired (\(\mathbf{Y_{des}}\)) are presented, as well as those obtained (\(\mathbf{Y_{ob}}\)) via ANN, for the two phases: training (80% of samples), validation (20% of samples), and for all samples (100%). In Figure 8a, the training results (148 samples) are shown, while in Figure 8b, the validation results (36 samples) are shown. It is important to highlight that the validation samples were not used in training the network; however, even so, the ANN was able to estimate \(Pa\) and \(Pr\) values very close to those desired. Finally, in Figure 8c, the total real (\(Pa\)) and reactive (\(Pr\)) power losses desired (\(\mathbf{Y_{des}}\)) and obtained (\(\mathbf{Y_{ob}}\)) for all samples (184 samples) are shown. Figure 9 shows, on the left, the curves of real power loss (\(Pa\)) as a function of load \(\lambda\), including both the desired one via CPF and that obtained via ANN. It is observed that there was a difference in the values in the region of maximum real power loss. However, at the critical point (CP), the values obtained were quite close: (\(\lambda\), \(Pa\)) = (1.5335, 0.8036) for \(\mathbf{Y_{des}}\) and (\(\lambda\), \(Pa\)) = (1.5335, 0.8316) for \(\mathbf{Y_{ob}}\), resulting in an error of 0.0280. On the right of Figure 8, the reactive power loss curves (\(Pr\)) as a function of load \(\lambda\) (\(\mathbf{Y_{des}}\)-desired and \(\mathbf{Y_{ob}}\)-obtained) show similarities between both, resulting in very close CP values: (\(\lambda\), \(Pr\)) = (1.5335, 2.9657) for \(\mathbf{Y_{des}}\) and (\(\lambda\), \(Pr\)) = (1.5335, 3.0070) for \(\mathbf{Y_{ob}}\), resulting in an error of 0.0423. These values can be seen in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sistema**} & \multicolumn{3}{c}{**CPF-\(\mathbf{Y_{des}}\)**} & \multicolumn{3}{c}{**ANN-\(\mathbf{Y_{ob}}\)**} & **Error** & \multirow{2}{*}{**Error (\(Pr\))**} \\ \cline{2-2} \cline{4-7}  & \(\lambda\) & \(Pa\) & \(Pr\) & \(Pa\) & \(Pr\) & (\(Pa\)) & \\ \hline IEEE-14 & 1.7680 & 0.7855 & 3.1321 & 0.8008 & 3.1245 & 0.0153 & 0.0076 \\ IEEE-30 & 1.5335 & 0.8036 & 2.9657 & 0.8316 & 3.0070 & 0.0280 & 0.0413 \\ IEEE-57 & 1.7252 & 1.0600 & 3.4851 & 1.0540 & 3.4492 & 0.0060 & 0.0359 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values obtained at the critical point (CP) by the CPF and ANN.  
Figure 7: Obtained curves from the IEEE 14-bus system: (**a**) \(Pa\) (\(\mathbf{Y_{des}}\) and \(\mathbf{Y_{ob}}\)) vs. \(\lambda\), i.e., total real power loss curve (\(Pa\)) vs. loading factor (\(\lambda\)); (**b**) \(Pr\) (\(\mathbf{Y_{des}}\) and \(\mathbf{Y_{ob}}\)) vs \(\lambda\), i.e., curve total reactive power loss (\(Pr\)) vs. loading factor (\(\lambda\)).