esa-sceva/satcom-chunk-collection huggingface dataset:
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs
### Types of Atmospheric Scattering  
Atmospheric scattering in the atmosphere occurs in two primary modes [77]:  
* Elastic scattering --The wavelength of the scattered light remains unchanged. Elastic LiDAR does not detect specific chemicals. Instead, it measures how different gases, particles, and aerosols scatter light. This helps identify areas where the atmosphere changes, such as differences in density, humidity, dust, and pollution [70; 74; 78].
* Inelastic scattering--The wavelength shifts due to energy exchanges with molecules (e.g., \"Raman scattering\") Raman scattering measures a spectral response and can monitor multiple gas species simultaneously [79; 80; 81]. There are two main types of elastic scatterings in the atmosphere of the Earth:
* Mie scattering occurs when particle sizes are similar to or larger than the wavelength of light (e.g., dust, water droplets, aerosols, molecules \(NO_{2}\) and \(O_{2}\)) and scale with the \(d^{2}\) where \(d\) is the diameter of the particle. It is dominant in the lower atmosphere, where larger particles are present [72; 82; 83; 84].
* Rayleigh scattering is the scattering of light by particles much smaller than the wavelength of the light (e.g., dust, pollen, smoke, and water vapor). The intensity of Rayleigh scattering is inversely proportional to the fourth power of the wavelength with factor \(\lambda^{-4}\). This means that shorter wavelengths (such as 355 nm) are scattered much more strongly than longer wavelengths (such as 1064 nm) [35] and are more predominant in the upper parts of the atmosphere [72; 82; 83; 84]. Another essential phenomenon related to the atmospheric LiDAR system is depolarization, which occurs when the polarization state of light changes as it interacts with matter, such as aerosols or clouds in the atmosphere. In spaceborne LiDARs, depolarization is an important parameter that can provide information about the properties of the target being studied. For example, the linear depolarization ratio of cirrus clouds mainly reflects the single-scattering properties of non-spherical ice particles. Similarly, depolarization data can help distinguish between different aerosols or clouds in the atmosphere. Notably, the depolarization ratio can be used to identify non-spherical dust particles that unambiguously quantify dust in the atmosphere and improve the accuracy of LiDAR measurements by correcting for depolarization effects [85; 86; 87].
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 3 Results and Discussion
### Inhibition of Exopolysaccharides (EPS) by ZnO-NPs  
Exopolysaccharides (EPS) are one of the major components of exopolymeric substances that constitute biofilms [41]. Moreover, such polymeric substances are polymers of natural origin that provide structural integrity and framework to the biofilms [42]. Being a fundamental component, extracellular polymeric substance also plays a crucial role in the determination of physicochemical properties of the biofilms. EPS serves as barrier for the entry of chemotherapeutic agents, including antibiotics, by blocking their entry in the biofilms [41]. The levels of EPS in the absence and presence of ZnO-NPs are shown in Figure 7A. The EPS secretion in _S. aureus_ was reduced by 14.44%, 26.58%, 42.86%, and 61.24% by the treatment of 1/16 \(\times\) MIC, 1/8 \(\times\) MIC, 1/4 \(\times\) MIC, and 1/2 \(\times\) MIC of ZnO-NPs, respectively. Similarly, the presence of 1/16 \(\times\) MIC, 1/8 \(\times\) MIC, 1/4 \(\times\) MIC, and 1/2 \(\times\) MIC of ZnO-NPs inhibited the EPS production of _P. aeruginosa_ by 19.83%, 33.77%, 55.40%, and 67.96%, respectively. The EPS of _E. coli_ was maximally inhibited in which more than 73% inhibition was recorded. The secretion of EPS not only provides integrity to the biofilm architecture but also confers resistance against the antibiotics [43]. Due to the positive correlation between the EPS secretion biofilm formation, the inhibition of EPS is also considered as an alternative target to mitigate the biofilms of pathogenic bacteria [44]. Our results corroborate with earlier findings in which zinc oxide nanoparticles synthesized using leaf extract of _O. bacatus_ inhibited the EPS production of _P. aeruginosa_, _K. pneumoniae_, _E. coli_, _L. monocytogenes_, _S. marcescens_, and _C. violaceum_ at sub-MICs [28]. The inhibition of EPS production may result in exposure of bacterial biofilms cells that may assist in the eradication of biofilms. Our findings confirm that green synthesized ZnO-NPs successfully reduced the EPS production both in Gram-positive and Gram-negative bacteria.
# Efficient Gene Transfection by Electroporation--In Vitro and In Silico Study of Pulse Parameters
## 4 Discussion
### Gene Electrotransfer Using Different Plasmid Sizes  
Since pDNA size is reported to have an influence on GET efficiency, we compared GET, cell survival, and overall GET with a 4.7 kb and a 3.5 kb pDNA having the same CMV promoter and both encoding GFP. We did not observe any difference in GET and cell survival between different pDNA sizes. Interestingly, smaller pDNA led to higher overall GET only when using the 8 \(\times\) 100 \(\upmu\)s pulse protocol in both cell lines. For other pulse protocols, we did not observe a significant increase in the overall GET with smaller pDNA, although the copy number of smaller pDNA was 1.34 times higher compared to the larger pDNA, as we used the same concentration of pDNA (500 \(\upmu\)g/mL) (Figure 6). Similarly, it was previously shown that GFP knockdown with pDNA sizes from 1.9 to 4.3 kb was equally efficient when the same moles of pDNA were used applying 2 \(\times\) 30 ms pulses [83]. Our results show that smaller pDNA did not have a strong effect on overall GET, which is contrary to observations by [41], where they report higher sub-microsecond high-frequency GET with smaller pDNA. Reducing pDNA size also improved GET efficiency in other studies [84, 85]. The difference in the size of the pDNA used in our study was only 25%, which might be the reason why we did not observe a significant increase in overall GET efficiency with smaller pDNA.  
Interestingly, when comparing MFI of GFP-positive cells transfected with pDNA of different sizes in C2C12 myoblasts, a significantly higher MFI with smaller pDNA was observed after GET with the 200 ns, 500 ns, and HF-BP pulses. On the contrary, in 1306 fibroblasts, a higher MFI with smaller pDNA was observed after GET with 8 \(\times\) 100 \(\upmu\)s and 8 \(\times\) 5 ms (Figure 7) pulses. This means that in C2C12 myoblasts, shorter pulses enabled more copies of smaller pDNA to reach the nucleus. With longer pulses, the MFIs after GET with both pDNA sizes were comparable. The situation seems to be reversed in 1306 fibroblasts, where longer pulses enabled more copies of smaller pDNA to reach the nucleus. This suggests that not only cell line and pDNA size but also pulse parameters influence the degree of transgene expression.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 7 Simulation Results  
For the justification of the proposed algorithm, the 3 GPP specified LTE-based downlink NB-IoT system is considered according to release 13. Link level simulation using MATLAB-2019a has been exploited to compare and validate the operation of the proposed technique with the conventional LS, LMMSE, and other derived versions of LMMSE. We have evaluated the performance of the downlink NB-IoT systems discussed in Section 3 using NRS (pilot aided) channel estimation technique by mean square error (MSE) and bit error rate (BER) of the channel along with throughput related to operating SNR as performance indicator. According to the NB-IoT communication scenario, TU 1 Hz channel model as in [21; 45], and EPA 1 Hz channel model as in [28] are exploited for the performance investigations. In both channel scenarios, the Doppler drift is treated as 1 Hz to ensure the low mobility of NB-IoT terminals. For all the investigations, the performance of the proposed overlap banded SVD LMMSE channel estimation method is provided with the conventional and proposed interpolation method and compared with the state of art methods in the simulations for better understanding. The perfect channel estimation scenario of LMMSE (i.e., without noise and estimation error) named as theoretical LMMSE is also considered for the better realization compared to the other channel estimators. In this work, for all the simulations, the theoretical LMMSE is used as a lower bound for the performance of the MSE, BER, and instantaneous throughput.  
For the link-level simulation, we have executed the system in single input and single output (SISO) mode with 180 kHz system bandwidth according to the 3 GPP specification [36; 38]. A 900 MHz carrier frequency with 7.68 MHz sampling rate is considered, while the CP length was set to 16 samples along with 512 FFT size. One LTE PRB (12 subcarriers) is used as NB-IoT UE subcarriers where the subcarrier spacing is 15 kHz (15 \(\times\) 12 = 180 kHz). We have also used QPSK as the based band modulation with the modulation and coding scheme level (MCS = 0), transport block size (TBS = 16 bits), and 64 signal repetition for 256 ms transmission time. Hence, 64 repetitions of the similar signal are combined while decoding the signal at the receiver. NPDSCH timing and frequency synchronization error and hopping are not considered in our simulation because of keeping focus only on channel estimation. The simulation results under TU 1 Hz and EPA 1 Hz channels are explained and compared below.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **LMMSE [44]** & **SVD-Based LMMSE** & **Banded SVD LMMSE** & **Overlap Banded SVD LMMSE** \\ \hline Multiplications & \(L^{3}+2L^{2}\) & \((L+2)N^{2}+LN\) & \(R[(S+2)(N/R)^{2}+SN/R]\) & \((2R-1)[(S+2)(N/R)^{2}+SN/R]\) \\ Memory size & \(L\) & \(<L\) & \(S\) & \(S\) \\ Complexity & Very high & Moderate & Low & Low \\ \hline \end{tabular}
\end{table}
Table 3: Complexity comparison.
## 4 Experimental Studies
### Results Analysis and Discussion
#### 4.3.2 Convergence Process Analysis  
The convergence speed is also an important indicator of the performance of evolutionary algorithms. In this part, we took the experimental results of the 30-D problems as an example to analyze the differences in the convergence process of the 10 algorithms. The results of the convergence process were classified according to the type of benchmark functions in the test set. Due to space limitations, the convergence of some functions was reduced, as shown in Figures 6-9. The shaded area in the figures has been enlarged to more clearly illustrate the convergence process of the  
Figure 8: Convergence progress on the hybrid functions (f11–f20).  
Figure 7: Convergence progress on the simple multimodal functions (f4–f10).  
For the unimodal functions, the convergence process of the algorithms is shown in Figure 6. TSLPSO and XPSO achieved faster convergence speeds and superior performances on f1 and f3, respectively. However, MLDMS-PSO was limited by the population dynamic reorganization scheme, which weakened its exploitation ability and made it show more stable results on two single-mode functions.  
For the multimodal functions, the convergence process of the algorithms is shown in Figure 7. The classic PSO algorithm had a sharp convergence on f5, f7, and f8 but its performance was poor. MLDMS-PSO, HCLDMS-PSO, and PSO-DLS had similar features in f5, f6, f7, f8, and f9, that is, they could accelerate the convergence when approaching the late evolutionary stage, which shows that the dynamic multiswarm strategy has a stronger ability to jump out of the local optimum.  
For the hybrid functions, the convergence process of the algorithms is shown in Figure 8. MLDMS-PSO achieved the best performance in f14, f16, and f17. In most hybrid functions, the convergence process was slow. The reason for this is that the effective management of multiple learning strategies can continuously obtain more favorable information, making the population continue to evolve. MLDMS-PSO also demonstrated good exploratory ability in f16 and f20 during the later stages of evolution.  
For the composition functions, the convergence process of the algorithms is shown in Figure 9. MLDMS-PSO obtained competitive results in f22, f23, f27, f29, and f30, but its advantages were not obvious. By further improving the algorithm, we could make it more accurate in solving the composite function and improving the practicability of the algorithm, which is a direction that needs to be constantly explored.  
In summary, MLDMS-PSO adopts a multiswarm architecture, allowing various sub-swarms to fully explore the solution space and demonstrate strong exploration ability in multimodal, hybrid, and composition functions. However, the cost of powerful global search capability is the loss of some local development ability, which slows down its local development speed. Compared to the improvement in solution accuracy, the slight loss in convergence speed can be accepted.  
Figure 9: Convergence progress on the hybrid functions (f21–f30).
# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 5 Performance Evaluation and Analysis
### Tasks Extensibility Analysis  
In order to further confirm the application scenario of OETS and analyze the task extensibility, the relative performance experiments, including relative energy consumption (REC) and relative time (RT), are conducted. Since the FIFO does not consider the energy consumption factor and the performance of this algorithm is obviously worse than EMRSA and OETS, we do not compare the relative performance with FIFO in this subsection. The relative energy consumption is defined as the proportion of the energy consumption difference between EMRSA and OETS to the energy consumption of EMRSA. The relative time is defined as the proportion of the required calculation time difference between EMRSA and OETS to the required calculation time of EMRSA. Figure 7 shows the results of relative energy consumption and relative time. In Figure 7, the result of relative performance improvement is normalized and represented by the vertical axis. The horizontal axis represents the number of tasks.  
As shown in Figure 7, the REC sharply drops among the number of tasks 200-400, while the RT gradually increase in the same interval. The intersection of the two curves lies among the number of tasks 200-400. The rising amplitude of REC exceeds the declining amplitude of RT when the number of tasks lies at the left of the intersection point. This means that the network performances of the OETS algorithm, i.e., the energy consumptions and the required calculation time, are superior to the EMRSA algorithm. When the number of tasks is greater than 400, the situation is converse, namely the rising amplitude of REC is smaller than the declining amplitude of RT. One reason is that the energy consumption rises along with the increasing of the number of tasks. The other reason is that the time required to search for the optimal solution rises as the number of tasks enlarges. Hence, the superiority of the OETS algorithm is not as obvious as before. To sum up the above results, the OETS algorithm is the optimal task scheduling strategy when the number of tasks is no greater than 400.
# FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
## 1 Introduction
[30; 31] proposed a general PN decomposition method and coordination algorithm in order to optimize the conflict-free routing for an Automated Guided Vehicle (AGV). [PERSON] et al. [32] introduced the concepts of near independence and near decomposition where the authors developed an approach for the solution of stochastic Petri net models. The developed approach is applied in the case of a flexible manufacturing system. The second step of this work, a genetic algorithm, is introduced to solve the mathematical model in the case of larger-scale problems. The last part deals with the implementation of the above proposed solution in order to synthetize a supervisor, coupled with a decomposed TPN model. The paper is organized as follows: Section 2 presents an overview of the timed Petri net and genetic algorithms. Section 3 illustrates the notations and terminology of the different variables used. Section 4 presents the basic assumptions where our FMS modeling is based. Section 5 derives a method to decompose the initial TPN into a set of Timed Marked Graphs (TMGs). Section 6 details the proposed approaches to solve the scheduling framework under availability constraints. The supervisor synthesis method is illustrated in Section 7. Some illustrative examples and comparative studies are shown in Section 8. Finally, Section 9 is dedicated to the perspectives of this work.
## 2 Mobilized Friction Angle Under RB Active Earth Pressure Mode
### Mobilized Friction Angle of the Fill Under RB Active Earth Pressure Mode  
The soil in the shear zone is ideally generalized as parallel rigid slices with infinitesimal thickness, which move following the associated flow rule as the wall rotates. In this context, the associated flow rule dictates that each soil slice tilts away from the underlying soil at an angle equal to the soil's friction angle during velocity increments. The failure mechanism of the backfill soil under RB mode under this configuration is illustrated in Figure 5.  
According to the geometric relationship in Figure 5, we can get  
\[S_{\tau}(z)=\frac{\cos\varphi}{\cos(\alpha-\varphi)}S(z)=\frac{\cos\varphi}{ \cos(\alpha-\varphi)}(1-\frac{z}{H})S_{\text{a}} \tag{3}\]  
The corresponding length of the shear plane is  
\[L(z)=\frac{z}{\sin\alpha} \tag{4}\]  
Therefore, the shear strain on the corresponding shear plane can be obtained as  
\[\gamma(z)=\frac{S_{\tau}(z)}{L(z)}=\frac{\sin\alpha\cos\varphi}{\cos(\alpha- \varphi)}(1-\frac{z}{H})\frac{S_{\text{a}}}{z} \tag{5}\]  
From Equation (5), when \(z=H\), \(\gamma(z)=0\); when \(z=0\), \(\gamma(z)=\infty\).  
Assuming that the shear stress and shear strain of the soil behind the wall satisfy the hyperbolic relationship given in Equation (6), the non-limit internal friction angle is derived as Equation (7) [24]:  
\[\tau=\frac{\gamma}{a+b\gamma} \tag{6}\]  
\[\sin\varphi_{\text{m}}=\frac{(1-K_{0})(1+\sin\varphi)(1-R_{\text{f}}+\eta R_{ \text{f}})+\eta[(1+K_{0})\sin\varphi+K_{0}-1]}{(1+K_{0})(1+\sin\varphi)(1-R_{ \text{f}}+\eta R_{\text{f}})-\eta[(1+K_{0})\sin\varphi+K_{0}-1]} \tag{7}\]  
In Equation (7), \(\varphi\) is the internal friction angle of the backfill soil mobilized at the strength limit; \(\varphi_{\text{m}}\) is the internal friction angle of the backfill soil in the non-limit state;  
Figure 4: Distribution of mobilized wall–soil friction angle values under different displacements in RB mode.  
Figure 5: Schematic diagram of soil failure mechanism in RB mode.  
\(\eta\) is the strain ratio between the non-limit active state and the limit state (\(\gamma_{\text{m}}/\gamma_{\text{f}}\), when \(\eta=1\), \(\varphi_{\text{m}}=\varphi\)); \(R_{\text{f}}\) is the failure ratio in the Duncan-Chang model, which is typically \(R_{\text{f}}=0.75\)-\(0.95\); and \(K_{0}\) is the coefficient of earth pressure at rest. For typical fill materials (e.g., sandy gravel), calculations can be performed using the formula provided in Table 1.  
Using the corresponding shear strain \(\gamma_{\text{m}}\) from Figure 6a, the strain ratio \(\eta\) between the non-limit active state and the limit state can be calculated. Substituting \(K_{0}\) (the initial stress ratio, i.e., the coefficient of earth pressure at rest) into Equation (7), the distribution of the mobilized internal friction angle of the soil is obtained, as shown in Figure 6b.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 4 Proposed VM Placement Strategy
### Grouping VMs  
After analyzing the dependency of the VMs, the next step is clustering the VMs according to their traffic dependencies. As a result of the PCA analysis, the VMs are illustrated in a low-dimensional space in which VMs that are placed closely are considered highly dependent on each other. We propose using a gravity-based clustering method [9] so that highly dependent VMs are clustered in the same group. The gravity-based clustering can make a specific number of VM clusters with different sizes. Therefore, it is useful to adjust the VM cluster size according to the resource constraints.  
As mentioned in Section 3.3, there are \(m\) PMs; therefore, the VMs are first clustered into \(m\) groups. To group the VMs, the total area of the field that covers all nodes in the reduced-dimensional space is calculated, as illustrated by the shadowed region in Figure 3. Let \(S\) be the area of the region that covers all nodes in the reduced-dimensional space. Then, the region is divided into \(m\) subsections. Initially, each subsection is a circle with an area of S divided by \(m\); the radius, \(R\), of each circle can thus be calculated as follows:  
\[R=\sqrt{\frac{\text{Area of }S}{m\cdot\tau}}. \tag{3}\]  
Once the group radius \(R\) is calculated using Equation (3), \(m\) center nodes are iteratively selected. For each node, the number of nodes that exist within \(R\) from the node is counted. Then, the node with the largest number of nodes within the circle is selected as the first center node. The nodes that fall within the circle become the neighbor nodes of the center node. This process is repeated excluding the selected center node and its neighbors, and the next center node is selected from the remaining nodes. In this way, \(m\) center nodes and their neighbor nodes are identified; each selected group of a center node and its neighbor nodes is referred to as a dependency group. Any node that is located outside of all circles and not allocated to any group by this process is thereafter allocated to the group based on which circle is the closest in terms of the Euclidean distance.  
Once the dependency groups are determined, they are matched into physical machines (this matching is discussed in the following subsection).