esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Setup and PAM Production  
The DBD CAP generation system is made up of a custom-made high voltage AC power supply with a sinusoidal waveform (maximum voltage 15 kV), a high voltage electrode (HVE), a dielectric layer and a grounding electrode (GE). The power supply is composed of a Variac transformer (0 to 240 V) and a 20 kV peak-to-peak neon transformer with a maximum average current of 30 mA. The HVE consists of four stainless steel cylindrical subunits, each with a 14 mm diameter interconnected by a copper plate. The GE electrode is represented by a copper plate with a thickness of 1.5 mm, covered with a 0.1 mm thick dielectric layer [62; 63]. In order to expose the medium to CAP discharges and to create the PAM, 24 well flat-bottomed cell culture plates (cat. 92024, TPP Techno Plastic Products AG, Trasadingen, Switzerland) containing 150 \(\upmu\)L exposure medium/well were used as a support/discharge vessel. The HVE was introduced into the cell culture plate without allowing the electrode subunits to come into contact with the internal walls of the wells. The height of the exposure medium column was 0.8 mm, the gap between the electrode subunits and the base of each well, as well as the distance between the electrode and the exposure medium surface was consistently maintained at 3 mm and 2.2 mm, respectively. Completing the exposure assembly was conducted by attaching the dielectric layer and the GE under the cell culture plate (Figure 1A). The number of cylindrical subunits in the HVE composition is equal to the number of wells in the column, thus exposing the medium from all four wells from a column to the CAP discharge at the same time. In order to avoid the appearance of possible side effects of the discharge that may have as a consequence of the modification of the exposure parameters, columns 1 and 6 from the exposure plate were not used (Figure 1B). In all experiments, the medium was exposed to CAP for different periods of time (30 s, 60 s and 90 s), known as exposure time (T\({}_{\text{exp}}\)), this being the period in which the source was turned on, generating DBD plasma in the air trapped between the lower end of each cylindrical subunit of the HVE and the medium in the well. In this study we will refer to the solutions exposed to plasma for the previously presented time periods as PAM-medium* 30 s, PAM-medium* 60 s and PAM-medium* 90 s (* where medium represent the exposed solution, i.e., PBS or RPMI 1640). Following the exposure, the PAM from each well was collected and mixed with the PAM from the other wells within the same exposed column to create a uniform treatment.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
###### Abstract  
Smartphones with integrated sensors play an important role in people's lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information--Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors--characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BILSTM) network combined with contextual information, and a meta-heuristic optimization algorithm.  
multi-source sensor; scene classification; GNSS; data mining; CNN +
Footnote †: journal: Sensors
## 5 Results
Figure 7: Figure shows LOS displacement in the period between (**a**) 18 December 2020 and 30 December 2020 from a Sentinel-1A ascending orbit T146, (**b**) 24 December 2020 and 4 January 2021 from a Sentinel-1B descending orbit T124, (**c**) 30 December 2020 and 23 January 2021 from a Sentinel-1A T146, and (**d**) 04 January 2021 and 28 January 2021 from a Sentinel-1B T124 orbit. The vertical displacements range between \(-12\) cm and \(22\) cm stretching over the earthquake stricken area (see Figure 9). The spatial split between the land subsidence and the land uplift follows the directions of Pokupsko and Petrinja strike-slip faults mapped by Croatian Geological Institute [74]. Vertical displacement is mostly expressed in the western quadrant; on the other hand, the displacement across the faults themselves is minimal. However, we find the increased values of displacement on the western and southern quadrant relatively expected due to topography indented in that area. On other hand, in the northern and eastern quadrant, the displacement is lower than the topography regarding the faults of Pokupsko and Petrinja, but there is still a displacement range of \(\pm 5\) cm suggesting a real ground movement related to the earthquake and independent of topography also in a wider affected area. The results of the horizontal west-east displacement coincide with the recently published GNSS field measurements [75]. Comparing the results from [75] with the results of the SAR analysis, the ground displacements around the epicenter are the resultant vector of two identified strike-slip faults: Pokupsko and Petrinja, as seen in Figure 9.  
The results of analyzed GNSS data show that there has been a significant shift of SISA station of 5 cm towards the East and 2 cm to the South (see Figure 10). The eastern shift coincides with the results we obtained from the SAR analysis, while the north-south direction is not straightforward in SAR processing since SAR technique is more sensitive to horizontal east-west shift neglecting the north-south shift since the orbit path stretches along the north-south direction. As for the height, the SISA station shows subsidence up to 3 cm, as seen in Figure 11, which is the complementary result obtained by the SAR analysis: The identical displacement is obtained at the location of SISA station when vertical displacement is derived from SAR analysis, shown in Figure 9. Considering the geological field data acquired after the earthquake, a hundred sinkholes [76] opened as one of the extreme post-seismic near-surface effects [54] in a small place Mecencani, located on a south-east line of Pokupsko strike-slip faults, as shown in Figure 9. Comparing the vertical displacements results obtained by SAR analysis and the geological occurrence of sinkholes, it is visible from Figure 9 that the impact on the vertical dimension was the strongest on the west side of Pokupsko strike-slip fault, reaching \(-6\) cm subsidence over the Mecencani area, probably activating the occurrence of sinkholes. Table 2.0 Figure shows vertical displacement over affected area.
## 3 Results
### Transmitter with Pulsed QC Laser  
The final step of the transmitter tests was to analyze emitted PRBS signals in RZ format coding. To this end, a 10-bits frame (100001000\({}_{\text{BIN}}\)) was generated. Figure 12 presents registered eye diagrams for three modulation speeds (5 MHz, 6 MHz, and 7 MHz).  
An increase in the pulse frequency decreases the pulse amplitude and the eye width. These parameters correspond with the previously defined capabilities of the laser's structure switching. Regarding the configured OWC system, the maximum modulation speed of 7 MHz was obtained.  
Figure 11: (**a**) Frame with 16 light pulses and (**b**) two registered pulses for different time periods.  
Figure 10: Deviation ranges of the optical pulse amplitude for different repetition rates.
## 5 Conclusions  
Our results suggest that MHM within advanced image analysis is capable of correctly distinguishing between changes in cell motility, cell dry mass, cell density, and cell death induced by blue light. As quantitative phase dynamics of a cell population is typical for specific processes in cells, it may be useful for viability tests and medical drugs evaluation. We have also shown that specific doses of blue light significantly influence cell motility, cell dry mass, and cell density. These changes seem to be specific for the particular cell line and the malignant status of tested cells. Based on the dynamics of QPI parameters, we assume that light dose 208 mJ/cm\({}^{2}\)\(\times\) 1000 ms induced different types of cell death in A2780 and G361 cell lines.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/7/2597/s1](http://www.mdpi.com/2076-3417/10/7/2597/s1)]([http://www.mdpi.com/2076-3417/10/7/2597/s1](http://www.mdpi.com/2076-3417/10/7/2597/s1)). Supplementary Videos S1-S4. Quantitative phase image dataset used in the manuscript is available at Zenodo repository (www.zenodo.org), [[https://doi.org/10.5281/zenodo.3670966](https://doi.org/10.5281/zenodo.3670966)]([https://doi.org/10.5281/zenodo.3670966](https://doi.org/10.5281/zenodo.3670966)) (PC-3 and PNT1A cells), [[https://doi.org/10.5281/zenodo.3673425](https://doi.org/10.5281/zenodo.3673425)]([https://doi.org/10.5281/zenodo.3673425](https://doi.org/10.5281/zenodo.3673425)) (A2780 and G361 cells).  
Conceptualization, J.B.; Methodology, M.F. and J.B.; Software, M.F. and J.B.; Data curation, T.V. and J.G.; Formal analysis, M.R. and J.B.; Writing-Original Draft Preparation, M.F.; Writing-Review and Editing, M.R., A.G.W. and J.B.; Visualization, M.F., J.G., T.V. and J.B.; Supervision, J.B.; Project Administration, J.B. and M.M.; Funding Acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by funds from the Faculty of Medicine, Masaryk University to Junior researcher ([PERSON]), by Grant Agency of the Czech Republic (18-24089 S), European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 721297, Biofilms Research Center for Biointerfaces, and Malmo University and by the \"Center for Tumor Ecology--Research of the Cancer Microenvironment Supporting Cancer Growth and Spread\" (reg. no. CZ.02.1.01/0.0/0.0/0.0/16_019/0000785) supported by the Operational Program Research, Development and Education.  
We acknowledge Telight, a.s. for support to Telight Q-PHASE technology.  
The authors declare no conflict of interest.
# Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process
## 3 Validation
### Liquid Droplet: Validation of Surface Tension  
Surface tension plays a crucial role in LPBF processes. Therefore, it is necessary to investigate the method's accuracy in approximating surface tension forces before applying it to a more complex problem. For this purpose, a surface-tension driven deformation of a liquid droplet in free space (i.e., no gravity) is examined. The initial geometry of the droplet is a square of size \(l=1\) cm. Properties of the liquid material are given in Table 1.  
With the surface tension forces acting as a traction boundary condition, the initial square droplet reaches an equilibrium state by transforming into a circular shape. To capture this process within the SPH framework, the first term on the right-hand side of Equation (20) is exerted to one layer of free-surface particles in the normal direction. Figure 5 shows this evolution at 5 time steps using a total of 1600 uniform particles. For better visualization, the arrows are scaled to the magnitude of curvature at surface particles.  
Figure 4: Graphical illustration of the proposed cell optimization for multi-resolution SPH domains.  
At the last time step when \(t=0.1\) s, the SPH particles are at rest and turn into a nearly perfect circle, as expected.  
The analytical solution is considered here by calculating the radius of the circular droplet at rest. Given the incompressible condition, this computation leads to a circle with \(R=0.56\) cm, which has the same area as the initial square geometry. To show the convergence of the method, the shape of this analytical circle is compared to the convex hull of the SPH particles at the last time step in 3 different resolutions. Figure 6 demonstrates this comparison, where \(\Delta\) is the initial particle spacing for the lowest resolution case with a total of 100 SPH particles (i.e., \(\Delta=0.11\) cm). The numerical result using \(dx=\Delta/4\) and the exact solution in this figure are nearly identical and on top of each other, indicating the accuracy and convergence of the present method.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 5 Conclusions  
In this paper, we proposed a novel sea wave interference suppression method based on improved CEEMDAN and WT. Under the condition of sea wave interference, this novel method and the traditional filtering method were discussed and compared. The novel sea wave interference suppression method has the characteristics of strong environmental adaptability and a low bit error rate, and it is suitable for cross-media communication in complex sea environments. This method can automatically eliminate the wave interference and reduce the bit error rate, which is of great significance for the practical application of cross-medium communication.  
Conceptualization, J.L.; methodology, J.L.; formal analysis, J.L.; investigation, X.B.; resources, X.L.; data curation, L.Z. and X.B.; validation, J.L., X.B. and Q.G.; writing--original draft preparation, J.L.; writing--review and editing, J.L., X.B., Q.G. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors thank all colleagues who participated in the cross-media system's design and the acquisition of the measured data. The authors would like to express their gratitude to the anonymous reviewers and the editor for their constructive comments on the paper.  
The authors declare no conflict.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CEEMDAN & Complete Ensemble Empirical Mode Decomposition with Adaptive Noise \\ AEIC & Acoustic and Electromagnetic Integrated Communication Technology \\ WSAW & Water Surface Micro-Amplitude Wave \\ WT & Wavelet Transform \\ IMF & Intrinsic Mode Function \\ BER & Bit Error Rate \\ LDV & Laser Doppler Vibrometer \\ FMCW & Frequency-Modulated Continuous Wave \\ RF & Radio Frequency \\ EMD & Empirical Mode Decomposition \\ TARF & Translational Acoustic-RF Communication \\ \end{tabular}
## 4 Outline and Methodology
### Harmonization of Land Cover Classification
In addition, the K-Means unsupervised classification method of the multi-temporal MODIS NDVI datasets express the key classification information and characterize the quantitative traits of each category based on SLCR. The SRTM DEM data improve the difference between categories by the elevation data. Moreover, the phenological variation characteristics of five MCD12Q1 data layers and GlobeLand30 form the feature vectors through the database attribute table. These feature vectors represent the clustering centers of each category. Subsequently, the category with the minimum Euclidean distance belongs to the corresponding land cover type, through computing the Euclidean distance between the feature vectors of each category and the clustering centers of each land cover type. Thus the mapping matrix is made through the mutual mapping of different classification systems and the KR is then established as shown in Table 4. A logical look-up table is constructed to harmonize the land cover classes. The \"barren land\" dose not present in Wanbei and Wannan and dominates a very small portion of the total area, so it is ignored when evaluating the area consistency and treated as \"others\". Consequently, a total of five categories are harmonized between the five MCD12Q1 data layers and GlobeLand30: \"Woodland\" is defined as the woody plant community; \"grassland\" is defined as the annual or perennial herbaceous vegetation dominated by plant communities; \"cropland\" is defined as the artificial cultivated vegetation cover for the purpose of the harvest; \"wetland\" is defined as the surface with the saturated water for a long time in the vegetation area and the non-vegetation area; and \"artificial surfaces\" refers to the lands modified by human activities. Figure 3: The flow chart of harmonizing land-cover classification based on KR.
## 5 Simulation Result Analysis  
The ULRRE model is simulated with no disturbance, constant disturbance, and parameter variation to determine the effect of external disturbances and uncertainties on the controllers. The ESO and FTSTD parameters have been tuned and selected heuristically to obtain the optimal response of controllers for trajectory tracking. The method for selection of observer bandwidth for ESO was given by Dr. [PERSON] [54] and is followed in this paper, which suggests choosing the values of observer bandwidth as a trade-off between noise sensitivity and LESO performance. Control effort tends to increase with a high value of bandwidth, which is one limitation. In this paper, the authors have chosen 400 rad/s. The proportionality and derivative constant are selected as \(k_{p}=w_{c}^{2}\) and \(K_{d}=2w_{c}\) based on [54].  
In MATLAB (2017b) [55], the controllers are evaluated in simulation under two disturbance conditions. The ode4 (Runge-Kutta) solver is employed with a sample time of 0.001 s. The shoulder and elbow joints have input trajectories of amplitude 0.7855 rad/s. The model is subjected to an external control disturbance of amplitude 5 N.m at 1.2 s. The response is recorded by applying \(\pm\)20% parametric uncertainty and the same disturbance of 5 N.m. The performance indices such as IAE, ITE, ITAE, and ISTE played a role in the comparison of different controllers and are called generic tools for evaluation of control algorithms [56]. The lower value of performance indices indicates better performance of controller [57] and all the parameters have been selected to minimize indices values.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 2 Implicit Assembly Algorithm for Overlapping Grid
### Classification of Grid Nodes  
Let \(\Gamma\) denote the \(M\) components in the entire solution region and each component \(\gamma^{i}\in\Gamma\), \(i\in\{0,1,\cdots,M-1\}\), correspond to a unique grid \(G^{i}\), whose nodes, faces and cells are presented with \(N^{i}\), \(F^{i}\) and \(C^{i}\), respectively. The nodes on the solid-wall boundaries of grid \(G^{i}\) are indicated with \(N^{i}_{w}\). For any node \(p\in N^{i}\), the distance between \(p\) and the solid wall of the \(j\)th component is defined as  
\[d(p,N^{i}_{w}):=\min\{d(p,q)\mid q\in N^{j}_{w}\} \tag{1}\]where \(d(p,q)\) means the Euclidean distance between grid points \(p\) and \(q\). If there exists a grid \(G^{j}(j\
eq i)\) that meets the following conditions:  
\[\begin{cases}d(p,N_{w}^{j})<d(p,N_{w}^{i})\\ (\exists e\in C^{j},p\in\hat{e})\vee(p\odot\gamma_{w}^{j})\end{cases} \tag{2}\]  
then \(p\) is called an _inactive_ node; otherwise, \(p\) is treated as an _active_ node. Here, \(p\in\hat{e}\) represents that \(p\) is an inner point of \(e\), and \(p\odot\gamma_{w}^{j}\) means \(p\) is located in the space bounded by the wall surface \(\gamma_{w}^{j}\) of the \(j\)th component. Consequently, the nodes \(N^{i}\) of grid \(G^{i}\) can be partitioned into _active_ nodes and _inactive_ nodes.  
Equation (2) indicates that the inactive property of grid nodes is jointly constrained by two conditions, i.e., the distance relation and the coverage relation. For an inactive node, the distance to its own solid wall exceeds that of some other component. The coverage relationship specifies that an inactive node is either an internal point of a volume cell belonging to one other component or surrounded by the solid wall boundaries of the very component. It can be seen that the distance relation does not hold for the grids nodes \(N_{w}^{i}\) on the solid wall, i.e., \(N_{w}^{i}\) should be active nodes. The distance relation and the coverage relation consider the influence of the solid wall boundaries and the external boundaries on the automatic hole-cutting process, respectively. Note that the method determining the relation between \(p\) and \(\hat{e}\) will be discussed in Section 3.2.
## 3 Results
### Cross-Validation Techniques
decision that had the highest score, which is a measure of the probability of an observation belonging to a particular class. It can be used to decide the class of an observation based on a threshold. (Figure 4C). Next, we tried to optimize cross-validation (CV). CV is a key step in machine learning. It is a method to evaluate ML models by correcting for overfitting, in order to improve the model's ability to generalize. It also estimates the goodness of a model. The most used is k-fold CV, which we used for all models in Figures 3 and 4. However k-fold CV may not be the best for small samples, since some classes may be absent from one or more folds. Therefore, we tested bootstrapping, leave-one-out (LOOCV) and stratified CV. Bootstrapping is a method used to simulate new samples from a single data set, by performing resampling with replacement. LOOCV uses a single observation as a validation set and all the remaining data as a training set. Therefore, LOOCV creates as many models as the sample size (193 in our case) and tests them the same number of times. Stratified CV ensures that all the outcome classes have an even proportion of outcome classes, so that that CV's results are a closer approximation of the real prediction error. We obtained the best predictions with LOOCV (Figure 5B). At the beginning of the study (Figure 3), we showed that the tree ensemble performs better for our data than the other models. However, it is possible that, with different cross-validations, the performance of other models improves too. Therefore, we tested bootstrapping, LOOCV and stratified CV also on the classification tree (Figure 6). To address class imbalance and reduce prediction bias, we implemented a cost-sensitive error function that applies a higher penalty for errors in the over-represented classes. Specifically, the cost error was calculated as the reciprocal of the class frequency. Figure 4: Confusion charts for (**A**) prediction of a tree ensemble on five categories using RUSBoost. (**B**) Prediction of the same tree ensemble for three consolidated categories. (**C**) Prediction of the binary decision model.