esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electroporation  
The studies were performed in vitro on a human lung cancer cell line (H69 AR). The cells were maintained in RPMI (Sigma-Aldrich, Poznan, Poland) supplemented with 5% fetal bovine serum (FBS, Lonza BioWhittaker, Visp, Switzerland), 1% penicillin/streptomycin (Sigma-Aldrich, Poznan, Poland), 0.5% sodium pyruvate (Sigma-Aldrich), and 50 umol/L 2-mercaptoethanolhanol (Sigma-Aldrich, Poznan, Poland). Cell cultures were cultivated as a monolayer in 25 and 75 cm\({}^{2}\) plastic flasks (Nunc, Roskilde, Denmark), maintained in  
Figure 3: The photograph of the (**A**) electroporator prototype, where a—HV source, b—LV sources with galvanic decoupling, c—microcontroller, d—array of drivers DRV1–4, e—power capacitors, and f—the high-power circuit; (**B**) the final device.  
Figure 2: The block diagram of the driving circuit.  
a humidified atmosphere at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\), and detached for the experiments by trypsinization (trypsin 0.025% and EDTA 0.02% solution, Sigma-Aldrich, Poznan, Poland). Cells were passed every 2-3 days and a day before the experiment.  
For electroporation, cells were trypsinized and centrifuged (5 min, 1000 rpm, MPW-341 centrifuge with stable rotor, MPW Med. Instruments, Warsaw, Poland). Then, cells were counted and for each sample 5 \(\times\) 10\({}^{5}\) of cells were resuspended in HEPES buffer (10 mM, Sigma) containing 250 mM sucrose (POCH, Gliwice, Poland) and 1 mM MgCl\({}_{2}\) in Milli-Q water.  
Flow cytometry analysis using YO-PRO-1 was performed to evaluate electroporation. Before the pulsing, YO-PROTM-1 iodide (YP-1, \(\lambda\)exc491/\(\lambda\)em509, Thermo Scientific, Warsaw, Poland) was added to the cell suspension. Final concentration of YP-1 was 1 uM. Flow cytometric analysis was performed using a CyFlow CUBE-6 flow cytometer (Sysmex, Warsaw, Poland). The samples were excited using the 488 nm line of the blue laser and the fluorescence of YP-1 was measured with an FL-1 detector. Data were analyzed using CyView software (Sysmex, Warsaw, Poland).  
For electroporation itself, a single unipolar pulse of 20 kV/cm \(\times\) 200 ns-1 us was used. Alternatively, the same energy was delivered via bipolar pulse for comparison (e.g., the effects of a 200 ns unipolar pulse were compared to the effects of a 100 ns + 100 ns bipolar pulse). One-way analysis of variance (ANOVA; \(p\) < 0.05) was used to compare the treatments. A Tukey HSD multiple comparison test for evaluating the difference was used when ANOVA indicated a statistically significant result (\(p\) < 0.05 was considered statistically significant). All experiments were performed in at least three repetitions, and the treatment efficiency was expressed as mean \(\pm\) standard deviation.
## 2 Background  
This section presents an overview of the existing anti-collision protocols. To properly understand the information gathered in this work, some basic terms related to anti-collision protocols are explained. Tags' responses are encapsulated in periods of time called slots. During the identification process, and depending on the number of tag responses received by the reader, three types of slot can occur: collision, idle, and success. A collision occurs when more than one tag responds to the reader's command in the same slot. When no tag responds to the reader's command, an idle slot happens. Success occurs when only one tag is correctly read and, therefore, identified by the reader.  
Communication between a single reader and several tags is based on sending and receiving information. Every communication channel has a predefined capacity, which is specified by its maximum data rate and the range of its antenna. The available channel capacity must be divided between all tags in the interrogation zone such that all of the data can be transmitted from several tags to a single reader without mutual interference. In early radio technology, the problem of multi-access arose. Many participants attempted to access a single satellite or base station. Therefore, many procedures were developed with the aim of separating the individual participants' signals from one another.
# Efficient Enhancement of Second Harmonic Generation via Noninvasive Modulation
## 2 Methods and Experimental Setup  
### Methods  
When a coherent light transmits through the scattering mediums, a speckle pattern with random phase and amplitude can be generated. Generally, the scattering process of light passing through a scattering medium is linear, which can be represented as follows [28]:  
\[E_{b}=\sum\limits_{a=1}^{N}t_{ba}A_{a}e^{i\varphi_{a}}, \tag{1}\]  
where \(t_{ba}\) describes the transmission matrix element of the scattering medium, \(A_{a}\) and \(\varphi_{a}\) are amplitude and phase of the incident light field, and \(E_{b}\) is the transmitted light field, which can be regarded as the linear superposition of \(N\) input elements of the incident light field. Therefore, the scattering medium can introduce a spatially distributed light field with a specific phase and amplitude.  
Studies have shown in theory that the intensity of SHG has a dependence on the gradient of phase [13, 29]. According to the Ref. [13], we can get the Equations (2) and (3), which are given as follows:  
\[\partial_{z}A_{n}=\frac{-1}{\Sigma_{R}}\{2\big{[}(\partial_{x}A_{n})(\partial _{x}\varphi_{n})+\big{(}\partial_{y}A_{n}\big{)}\big{(}\partial_{y}\varphi_{n })\big{]}+A_{n}\
abla_{\perp}^{2}\varphi_{n}\} \tag{2}\]  
\[A_{n}\partial_{z}\varphi_{n}=\frac{-1}{\Sigma_{R}}\Big{\{}A_{n}\Big{[}( \partial_{x}\varphi_{n})^{2}+\big{(}\partial_{y}\varphi_{n}\big{)}^{2}\Big{]}- \
abla_{\perp}^{2}A_{n}\Big{\}} \tag{3}\]  
\[+F_{n}cos(\Delta kz+\Delta\varphi),\]where \(n=1\) and 2 mean the fundamental and doubling frequency light, respectively. \(\Delta k=k_{1}-2k_{2}\), \(\Delta\varphi=\varphi_{1}-2\varphi_{2}\), and \(F_{\rm n}\) is an interaction term related to the effective nonlinear coefficient of crystal and the amplitude of each wavelength. The above equations clearly explain the relationship between the complex field amplitude and transverse phase and amplitude. In other words, we only need to control the wavefront phase distribution (\(\varphi_{a}\)) in front of scattering medium to change the intensity of SHG.  
It is possible to achieve the enhancement of SHG by modulating the transmitted light of the scattering medium. To our acknowledge, a speckle field has a random phase and amplitude distribution. The wavefront shaping technique was widely used in the modulation of the wavefront phase of incident light through the scattering medium [30; 31; 32]. Applying the wavefront shaping technique and spatial light modulator can obtain a spatial distribution of speckle field with specific phase and amplitude. In order to improve the light intensity of SHG, we used the wavefront shaping technique to control the wavefront phase distribution of the fundamental frequency light through the scattering medium. The optimization algorithm plays a key role in finding an optimal phase mask of wavefront shaping technique. In this paper, we adopted the genetic algorithm (GA) [33] as the feedback iterative algorithm, which can converge quickly and obtain the optimal phase.
# A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm
## 3 Complex-Valued Encoding Grey Wolf Optimization Algorithm (CGWO)
### The Complex-Valued Encoding Method
#### The Updating Method of CGWO  
Update the Real Parts  
\[\overrightarrow{D}_{R\alpha}=\left|\overrightarrow{C}_{1}\cdot\overrightarrow{X }_{R\alpha}-\overrightarrow{X}_{R}\right|,\overrightarrow{D}_{R\beta}= \left|\overrightarrow{C}_{2}\cdot\overrightarrow{X}_{R\beta}- \overrightarrow{X}_{R}\right|,\overrightarrow{D}_{R\delta}=\left| \overrightarrow{C}_{3}\cdot\overrightarrow{X}_{R\delta}-\overrightarrow{X}_ {R}\right| \tag{9}\]  
\[\overrightarrow{X}_{R1}=\overrightarrow{X}_{R\alpha}-\overrightarrow{A}_{1} \cdot(\overrightarrow{D}_{R\alpha}),\overrightarrow{X}_{R2}=\overrightarrow {X}_{R\beta}-\overrightarrow{A}_{2}\cdot(\overrightarrow{D}_{R\beta}), \overrightarrow{X}_{R3}=\overrightarrow{X}_{R\delta}-\overrightarrow{A}_{3} \cdot(\overrightarrow{D}_{R\delta}) \tag{10}\]  
\[\overrightarrow{X}_{R}(t+1)=\frac{\overrightarrow{X}_{R1}+\overrightarrow{X}_ {R2}+\overrightarrow{X}_{R3}}{3} \tag{11}\]  
Update the Imaginary Parts  
\[\overrightarrow{D}_{I\alpha}=\left|\overrightarrow{C}_{1}\cdot\overrightarrow {X}_{I\alpha}-\overrightarrow{X}_{I}\right|,\overrightarrow{D}_{I\beta}= \left|\overrightarrow{C}_{2}\cdot\overrightarrow{X}_{I\beta}-\overrightarrow {X}_{I}\right|,\overrightarrow{D}_{I\delta}=\left|\overrightarrow{C}_{3} \cdot\overrightarrow{X}_{I\delta}-\overrightarrow{X}_{I}\right| \tag{12}\]  
\[\overrightarrow{X}_{I1}=\overrightarrow{X}_{I\alpha}-\overrightarrow{A}_{1} \cdot(\overrightarrow{D}_{I\alpha}),\overrightarrow{X}_{I2}=\overrightarrow {X}_{I\beta}-\overrightarrow{A}_{2}\cdot(\overrightarrow{D}_{I\beta}), \overrightarrow{X}_{I3}=\overrightarrow{X}_{I\delta}-\overrightarrow{A}_{3} \cdot(\overrightarrow{D}_{I\delta}) \tag{13}\]  
\[\overrightarrow{X}_{I}(t+1)=\frac{\overrightarrow{X}_{I1}+\overrightarrow{X}_ {I2}+\overrightarrow{X}_{I3}}{3} \tag{14}\]  
where \(\overrightarrow{D}_{R}\) and \(\overrightarrow{D}_{L}\) indicate the real and imaginary of the approximate distance between the current solution, alpha, beta, and delta respectively. \(\overrightarrow{X}_{R}\) and \(\overrightarrow{X}_{L}\) indicate the real and imaginary parts of the final position of the current solution. It should be noted here that \(\overrightarrow{A}\) and \(\overrightarrow{C}\) are coefficient vectors, so it is not necessary for them to be converted to real and imaginary.
## 5 Other Monitoring Analysis and Measurement Verification
### Analysis and Comparison of Other Monitoring Means
It can be seen from the statistical results in Table 2 that the position of the working resistance of the tail entry anchor bolts is about 50 m away from the coal wall of the working face under the influence of mining, and the peak stress is about 29 m. Meanwhile, the working resistance of anchor bolts in the belt transport down chute is affected by mining-induced stress at a distance of approximately 49 m from the coal wall of the working face, with the stress peak occurring around 23 m.  
(3) Comprehensive comparative analysis results  
The field measurement results of the mining influence range and the distribution characteristics of the abutment pressure of the solid working face are summarized and listed in Table 3. It can be seen from the table that the mining influence range obtained from microseismic monitoring is basically consistent with the corresponding results obtained from \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measure Point** & & & **Tail Entry** & & & **Belt Transport Down Chute** \\ \hline anchor bolts dynamometer & 4\(\sharp\) & 6\(\sharp\) & Average value & 7\(\sharp\) & 8\(\sharp\) & 10\(\sharp\) & Average value \\ \hline Distance from coal wall when affected by mining/m & 60.3 & 41.6 & 50.95 & 40.4 & 42.5 & 64.5 & 49.13 \\ \hline Distance from stress peak to coal wall/m & 32.3 & 26.1 & 29.2 & 26 & 24.9 & 19.7 & 23.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: 1300 working face periodic weighting statistics. Figure 13: The location distribution of measuring points in the real-time monitoring of and warning system for rockburst. dynamic stress monitoring. The distance from the peak position of abutment pressure to the coal wall and the influence range of abutment pressure obtained by microseismic monitoring are larger than those obtained by dynamic stress monitoring. Therefore, it can be inferred that there are two bearing pressures in the surrounding rock of the working face, namely, dynamic bearing pressure and static bearing pressure. In fact, the commonly mentioned abutment pressure is dynamic abutment pressure, which is formed in a short time with the advancement of the working face. For general working faces, the distance from the strike peak position to the coal wall is about two to three times the mining height, and the distance from the lateral peak position to the goaf is 15 to 20 m. In addition to the dynamic abutment pressure, there is also a static abutment pressure in the surrounding rock of the working face, which is formed by the weight transfer of the high roof. The distance from its peak position to the coal wall is about two to four times the distance from the peak position of the dynamic abutment pressure to the coal wall.
## 7 Evaluation Results
### Bundle Arrival Rate  
The bundle arrival rate for each bundle is a ratio of the number of receiving nodes that have received the bundle within the destination area to the number of receiving nodes within the destination area. It can be expressed as:  
\[\begin{split}&\text{Bundle arrival rate}\left(\%\right)\\ &=\frac{\text{Number of receiving nodes that have received the bundle within the destination area}}{\text{Number of receiving nodes within the destination area}}\end{split} \tag{1}\]  
The bundle arrival rates of different routing methods are shown in Figure 22. The vectorial covered area model and \"epidemic routing\" both attained a bundle arrival rate of almost 100%. They were followed by the circular covered area model and \"spray and wait routing\", which both recorded a bundle arrival rate of about 75%. The bundle arrival rates of \"location-based routing\" and \"motion vector routing\" were about 40% and about 10%, respectively. Since both the circular covered area model and vectorial covered area model of the proposed routing determine in advance based on the traveling route information whether the bundle can reach its destinations and relay the bundle only when it is sure that it can reach its destinations, their arrival rates were higher than those of the existing routing methods. The circular covered area model had a somewhat lower bundle arrival rate than the vectorial covered area model because circular covered areas can contain pockets of areas that cannot be reached. \"Location-based routing\" and \"motion vector routing\" attempt to relay a bundle to a node that is closer to the destination or that is moving towards the destination. However, they often selected nodes that did not go to the destination.
## 1 Introduction
These papers have preliminarily established the calculation model for a layered structure, but they only stay in the stage of theoretical analysis, and there are few practical application cases. The XPS extruded board is damaged due to the small thickness of the protective layer during the actual use of the ground, which affects the construction safety, as shown in Figure 2. The current building ground design codes in most countries provide a variety of ground bedding cushion construction codes, but do not fully consider how to calculate the minimum thickness of the protective layer on the non-adhesive extruded board [22]. Based on the above theoretical calculation model of layered elastic half space, the thickness of XPS is taken as 40 mm and 60 mm, respectively, and the minimum thickness of the leveling layer of seven groups of different materials is calculated, so as to provide theoretical support for the actual laying of the leveling layer. Figure 1: Schematic draft of non-adhesive flat extruded board system. 1. decorative layer; 2. steel wire mesh; 3. fine aggregate concrete protective layer; 4. thermal insulation and sound insulation layer (XPS); 5. floor structure layer (including leveling treatment). Firstly, this paper briefly reviews the theory of a shear-rigid plate on Winkler foundation, based on Westergaard theory, the theory of elastic half space and 3D finite element simulations (3D FE simulations), and compares their applicability [21]. Secondly, the calculation program of the corresponding relationship between the thickness of the leveling layer and the tensile strength of the functional layer material under load is established by using the elastic half-space theory. Then, the minimum thickness of cementitious self-leveling mortar and gypsum base course is calculated, when the thickness of the functional layer is 40 mm and 60 mm, respectively, under the action of 2 kN/m\({}^{2}\) floor load. Finally, the numerical solutions of influencing factors, such as elastic modulus and Poisson's ratio of base material, are obtained by using MATLAB language in the literature [21]. MATLAB integrates many powerful features, such as numerical analysis, matrix calculations, visualization of scientific data, modeling and simulation of nonlinear dynamic systems, etc., providing a comprehensive solution for scientific research, engineering design, and many scientific fields where efficient numerical calculations must be performed.
# Distributed Attitude Synchronization for Spacecraft Formation Flying via Event-Triggered Control
## 1 Introduction
Preliminary knowledge and the problem statement are presented in Section 2. Sections 3 and 4 present the event-based attitude consensus control strategy and numerical simulations, respectively. Finally, some conclusions are shown in Section 5.
## 4 Discussion
Errors in manufacturing, buckling problems that occur when setting the initial position, deriving stiffness through linearization, plastic deformation of flat springs, and friction can be considered as error factors. Among these, the buckling problem is a common problem of a hinged-slide joint, whereby it loses balance in the initial position [16]. In this study, efforts were made to minimize the buckling problem by supporting the segment using flat springs at the initial position. The prediction model introduced in this study expresses the overall position error as the accumulated sum of the errors of each joint. Despite efforts to increase sophistication in the manufacturing process, errors still occurred. More efforts are needed to reduce mechanical errors by designing an assembly method that can improve precision, for example, by manufacturing a jig for fixing the position. In addition, the issue of plastic deformation of the flat spring could be avoided by replacing stainless steel with a hyper-elastic material such as nitinol. A hyper-elastic material would contribute significantly towards improving the performance of this machine, owing to its excellent flexibility and resilience. However, nitinol is more expensive than stainless steel, and substantial expertise is required for shape forming. Therefore, for this prototype production, a relatively widely used material was used. Another error occurred in the process of obtaining the stiffnesses through linearization, since linearization inherently involves some errors. Finally, modeling techniques considering friction of segments, flat springs, and cables can also aid accurate analyses. Nevertheless, it was a reasonable method to bring the stiffnesses within the shape prediction, and several repeated experiments were implemented to increase the reliability of the linearization.
## 2 Methodology
Finally, if the value is greater than a chosen threshold, this match is selected. ```
ref_img: Image with one band no restriction about datatype)  img: Image with one band no restriction about datatype)  d: Distance of the ring in cell units  t: Transformation (General transformation from ref_img to img that has to be reversible)  s: Relation of scale between ref_img and img  minp: Minimum value of Pearson test to consider a keypoint  l: List of matched keypoints  l = list(l)  for each cell(i,j) in img1  ring1  ring1 = getRing(i,j,d,ref_img)  i2, j2 = transform(ij,t)  phase, rotationbase = 0, 0  ibase, jbase = i2, j2  for each cell(ir2,jr2) in range([i2 - d x s,i2 + d x s], [j2 - d x s]2 + d x s])  ring2 = getRing(ir2,jr2,img)  ring2 scale = scale(ring2, len(ring1))  p = pearson(ring1,ring2 scale)  r = 0  for each rotation in ring_perimeter  ring2 rotated = rotate(ring2 scale, rotation)  rotated = pearson(ring1, ring2 rotated) if p < protated then  p = protated  r = rotation if phase < p then  ibase, jbase = ir2,jr2  phase, rotationbase = p, r  if phase >= minp then  l.add((i,j),(ibase,jbase),(phase,rotationbase)) return1
```  
**Algorithm 1**Determine matched keypoints between two images. With regard to the theorical time, it is close to order O(n\({}^{4}\)). However, some improvements can be applied, for example, because the rotation of the ring, which is a linear element, is only a translation in the offset of the initial index of the element. On the other hand, the rings can be read in a cached way in the tested image side because the comparison of each ring is applied various times, one for each pixel of the reference image inside the possible matching zone. With these two changes, the theorical time can be reduced. Moreover, if we consider that correlation test has several sums that are not affected by rotation, only the crossed products between the ring from the tested image and the reference image are calculated in the order defined by rotation. The final approach of the proposed algorithm is to recalculate the initial transformation and determine the differences between transformed keypoints from reference image and their homologous points in tested image. These differences are considered local changes, while the transformation is considered the positional quality. Finally, it is important to note that there are some parameters that define the goodness of the keypoints' definition. First, the comparison ring distance, because of its low variance, can be defined by several rings. The other parameter is the minimum value of correlation test; in this case, a very high value greater than 0.9 is recommended, because of the low variance of DEM data. With regard to the other parameters, like scale between DEMs or the test zone maximum search distance, they are defined using the preprocessing phase, so they have fixed default values. However, maximum search distance can be modified if both DEMs are extremely different.
## 3 Minimum Nonlinearity PL Design Guidelines
### Adding New Design Criteria  
A general optimization problem is defined as a search in an \(n\)-dimensional decision space, mapped through a cost function \(f\) to a lower \(k\)-dimensionality objective space, in which a comparison of elements \(x\) in the original space is performed through metrics \(y\) defined in the destination space. In the case of RA design, diverse antenna parameters compose the comparison metrics, hence, a multiobjective optimization problem is implicit.  
\[f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{k} \tag{6}\] \[y=f(x).\]  
Common RA design approaches mix multiple parameters to obtain a single performance indicator to facilitate decision, however, if the cost function is defined in a way such that \(k=1\), there is a risk of over-simplifying the problem as there exists the possibility that \(f\) be a surjective projection of \(\mathbb{R}^{n}\rightarrow\mathbb{R}\) having several individuals with very similar cost values and quite different configuration and performance in the individual criteria; this prevents the discovery of the trade-offs involved in the design process, and may result in the optimization being biased towards the easier of the individual performance metrics, neglecting the harder ones.  
Adding dimensions to the objective space allows the creation of Pareto fronts, representing the best objective combination, enabling the visualization of trade-offs and exploring the interrelation between objectives. Furthermore, adding new decision axes to the objective space creates new separation criteria for configurations with very similar performance on the other axes.  
In this work, a new power-related metric is defined in addition to the port parameters and far-field related metrics allowing to account for antenna port match, isolation, beam pointing, gain, power efficiency and nonlinearity.