esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of BWSPHs on RAW264.7 Cell Viability  
The effect of 24 h incubation with six BWSPHs (BW-SPH-A to -F) and six SGID BWSPHs (BW-SPH-A-GI to -F-GI) with final concentrations ranging from 0-1.0% (\(w/v\) dw), on the viability of RAW264.7 cells was investigated. BW-SPH-A (0.05% (\(w/v\) dw)) was the only BWSPH to induce a significant proliferative effect on RAW264.7 cells (\(p<0.05\)) (Supplementary Table S1). In contrast, BW-SPH-D (0.9% (\(w/v\) dw)) and BW-SPH-E (1.0% (\(w/v\) dw)) reduced the viability of RAW264.7 cells significantly compared with media alone (100%) (\(p<0.05\)). SGID samples (up to 1.0% (\(w/v\) dw)) did not significantly alter the viability of RAW264.7 cells compared to the control (\(p>0.05\)) (Supplementary Table S2). A BWSPHs concentration of 0.5% (\(w/v\) dw) was chosen for future cell-based experiments which induced greater than 80% cell viability. The same concentration was chosen for SGID BWSPHs to enable direct comparison between BWSPHs and simulated GI digests.
## 3 Construction of Immersive Simulation Platform
### Kinematics Modeling
#### 3.2.3 Model of Carrier Aircraft Landing  
When the carrier aircraft lands, the carrier aircraft lands towards the ideal landing area. The glide line equation of carrier aircraft landing is as follows:  
\[\frac{x-M}{\cos a\cdot\cos b}=\frac{y-N}{\sin a\cdot\cos b}=\frac{z-J}{-\sin b} \tag{15}\]  
where \(M\) is the coordinates of the landing point of the carrier aircraft in the \(X\) direction, \(N\) is the coordinates of the landing point of the carrier aircraft in the \(Y\) direction, \(J\) is the coordinates of the landing point of the carrier aircraft in the \(Z\) direction, \(a\) is the declination angle of the deck, and \(b\) is the landing angle of the carrier aircraft.  
The shipborne mechanism thinks that the ship point is a circular area, and the carrier-based aircraft lands randomly in this area when landing on the ship, then the calculation formula of \(M\) and \(N\) is:  
\[\left\{\begin{aligned} M=& m+r-2r\cdot\frac{rand( \cdot)}{RAND_{MAX}}\\ N=& n-\sqrt{r^{2}-\left(M-m\right)^{2}}+2\cdot \sqrt{r^{2}-\left(M-m\right)^{2}}\cdot\frac{rand(\cdot)}{RAND_{MAX}}\end{aligned}\right. \tag{16}\]  
where \(m\) is the \(X\)-direction coordinate of the center of the ideal landing area, \(n\) is the Y-direction coordinate of the center of the ideal landing area, and \(r\) is the radius of the ideal landing area, \(rand(\cdot)/RAND_{MAX}\) is a random value within 0 to 1.  
According to the above formula, the three-dimensional space coordinates of the carrier aircraft landing point can be obtained:  
\[\left\{\begin{aligned} \frac{x-M}{\cos a\cdot\cos b}=\frac{y-N}{ \sin a\cdot\cos b}=\frac{z-J}{-\sin b}\\ k_{1}(x-k_{1})+k_{2}(y-k_{2})+k_{3}(z-k_{3}+H_{s})=0\end{aligned}\right. \tag{17}\]  
where \((k_{1},k_{2},k_{3})\) is the normal vector of the deck plane and \(H_{s}\) represents the heave distance of the carrier deck. The stress analysis of the barrier cable is as follows:  
\[M\frac{d^{2}S}{dt^{2}}=F_{\epsilon}-2T_{\epsilon}\sin\theta-F_{f}-D \tag{18}\]  
where \(F_{\epsilon}\) is the aircraft thrust; \(T_{\epsilon}\) is the arresting cable tension; \(\theta\) is the angle between the connecting position of the arresting cable and the two reversing pulleys; \(S\) is the aircraft taxiing distance; \(F_{f}\) is the deck friction; and \(D\) is the air resistance.  
The deck friction drag \(F_{f}\) suffered by the aircraft is very small and negligible, that is, \(F_{f}=0\). After the aircraft successfully hooks the arresting cable, the engine shuts down to reduce the length of the glide, that is, the aircraft thrust \(F_{\epsilon}=0\). Thus, Equation (18) can be simplified as follows:  
\[M\frac{d^{2}S}{dt^{2}}=-2T_{\epsilon}\sin\theta \tag{19}\]
###### Abstract  
A high-power laser diode driving controller (HPLDDC), which incorporates the power converter with the feedback controller, was developed and implemented in this paper. The synchronous buck-boost converter (SBBC) was the topology of the power converter; the SBBC could be operated in step-up or step-down mode in accordance with variable on-board battery voltage inputs into the HPLDDC. Moreover, the feedback controllers were equipped with a current-loop controller (CLC) and a voltage-loop controller (VLC); the VLC was employed to regulate the SBBC output voltage to drive and start-up the high-power laser diode (HPLD). The CLC was used to regulate the SBBC output current to supply a constant-current driving the HPLDs. During the start-up transient phase, when the SBBC output mode is changed from the constant-voltage to the constant-current, a start-up current spike occurs that can destroy the semiconductor material of the laser diode. However, few studies have discussed methods of coping with this problem. Therefore, this study proposed a proportional-integral associating proportional (PIAP) control technology, which can be applied to the CLC for the start-up current spike mitigation. Complete designs and analyses are presented in this paper. Simulations and experiments validate that the PIAP control method is effectual to solve the start-up current spike.  
high-power laser diode; synchronous buck-boost converter; proportional-integral associating proportional 2018 8 1532; doi:10.3390/app8091532
## 3 Results and Discussion
### The Analysis of Electromagnetic Field in Metamaterial  
Metamaterials, in general, are engineered structures that are occasionally arranged to exhibit intriguing electromagnetic properties not seen in conventional materials. The interaction of the metamaterial with electromagnetic waves can be described by [PERSON]'s equations [32].  
The structure of artificial materials can be modified to obtain appropriate values of permittivity and permeability for applications. Figure 7 shows the electromagnetic properties of the artificial material. Electromagnetic waves are associated with a frequency and a wavelength, and they travel at the speed of light, c. The relationship between the wave characteristics can be explained by the equation \(c=\Omega\). The higher the frequency, the shorter the wavelength. As a result, a higher frequency carries more energy. That being the case, electromagnetic waves with a frequency of 8.81 GHz interact more strongly than electromagnetic waves at the frequency of 5.52 GHz. The red zone at the metamaterial structure of both resonance frequencies in Figure 7a indicates that there was a strong electric field reaction at the gap of the square shaped SRR and at the gap of the center of the oval-shaped metal. It means that the capacitor stores and releases charge across the SRR gaps. Meanwhile, the magnetic field result shown in Figure 7b indicated that the metal strip acted as an inductor, storing electrical energy in the form of magnetic energy. Because the magnetic field was created by an electric current, looking at Figure 7b,c, it it clear that the magnetic field was dependent on the current density. Because there was low current flow at a resonance frequency of 5.52 GHz, the magnetic field at the metal gap was minimal. Due to the larger current density, a strong magnetic field was sensed at the metal strip that joined the square and oval shapes. Furthermore, the current density was lower at the metal gap and the center of the oval-shaped SRR at the resonance frequency of 8.81 GHz, affecting the weaker magnetic field interaction. Due to the high concentration of current density, the magnetic field was stronger at the metal strip of the square and outer oval shapes of the SRR. Figure 7c demonstrates the proposed SRR's current distribution. As can be observed, the current was concentrated throughout the oval-square shape SRR. At the resonance frequency of 8.81 GHz, large amounts of current were concentrated practically all across the oval-square-shaped SRR, as opposed to the resonance frequency of 5.52 GHz, where the current was primarily concentrated at the connection metal between the oval- and square-shaped SRRs. A certain part of the metal cannot be coupled with the incident waves because the electric field polarization direction of the incident wave was in the \(y\)-axis direction; meanwhile, the magnetic field polarization direction was in the \(x\)-axis direction. Furthermore, the oval-square-shaped SRR's symmetrical design allows the current to flow in the opposite direction.
# Correction of Young's Modulus Calculation in the Flexural Mode of Resonant Column Test
## 2 Analysis Method of Flexural Test
### Correction for the Rotational Effect of EDD  
The proposed formula for the flexural mode (Equation (8)) does not consider the influence of the rotational inertia of the EDD and the top cap on the resonant frequency. Owing to its considerable radius and mass, the rotational influence of the EDD must be considered to improve the accuracy of [PERSON]'s modulus estimation.  
According to the displacement equation of the specimen (Equation (2)), the rotation angle at the top of the specimen (\(\Delta\theta\)) is calculated as:  
\[\Delta\theta=3\alpha L^{2} \tag{10}\]  
The average angular velocity (\(\overline{\omega}\)) of the top cap and the EDD can be obtained using the rotation angle and the vibration frequency (\(f\)) of the specimen at resonance. The conversion between average angular velocity \(\overline{\omega}\) and resonant frequency \(\omega_{f}\) is shown in Equation (11):  
\[\overline{\omega}=4\Delta\theta\times f=\frac{4\Delta\theta}{2\pi}\times \omega_{f}=\frac{6\alpha L^{2}}{\pi}\times\omega_{f} \tag{11}\]  
When the specimen vibrates at the resonant frequency, rotational kinetic energy \(J_{T3}\) of the top cap and the EDD can be computed as:  
\[J_{T3}=\frac{1}{2}\times I_{T}\times(\frac{6\alpha L^{2}}{\pi}\times\omega_{f })^{2} \tag{12}\]  
where \(I_{T}\) is the rotational inertia of the top plate, top cap, or EDD. According to the conservation of the energy state, the kinetic energy of the system (\(J_{T1}+J_{T2}+J_{T3}\)) is equal to the strain energy of the rod (\(J_{U}\)). Therefore, Equation (8) is modified as:  
\[{\omega_{f}}^{2}=\frac{3 EI_{b}}{\left\{\frac{33}{140}m_{T}+\sum_{i=0}^{N}m_{i} \cdot h(h0_{i},h1_{i})+\sum_{i=0}^{N}\left(\frac{3}{\pi L}\right)^{2}I_{T1} \right\}\cdot L^{3}} \tag{13}\]  
The mass of each component is substituted into Equation (13) to obtain:  
\[{\omega_{1}}^{2}=\frac{3 EI_{b}}{\left\{\frac{33}{140}m_{T}+\overline{m}_{a}+ \overline{m}_{c}+\overline{m}_{d}+\overline{I}_{T1}\right\}\cdot L^{3}} \tag{14}\]where \(\omega_{1}\) is the flexural resonant frequency of the system with the calibration rod only; \(m_{T}\) is the mass of the rod; \(\overline{m}_{a},\ \overline{m}_{c},\ \text{and}\ \overline{m}_{d}\) are the products of the mass and the centroid height of the top plate, top cap, and EDD, respectively; and \(\overline{I}_{T1}\) is the sum of the product of the rotational inertia of the top plate, top cap, and EDD and its coefficient \(\left(\frac{3}{\pi L}\right)^{2}\).
## 2 Super-Resolution Methods
### Infrared Polarization Super-Resolution Reconstruction Model
As a result, its probability density function can be expressed as follows:  
\[p(\alpha)=\frac{1}{2b}e^{-\frac{\left\lVert\alpha\right\rVert_{1}}{b}} \tag{10}\]  
The variable \(I_{LR}\) in Formula (9) is distributed according to a Gaussian distribution with the parameter \(\left(D_{L}a,\delta^{2}\right)\). Thus, its probability density function can be expressed as follows:  
\[p(I_{LR}\ \mid\ D_{LR},\alpha)=\frac{1}{\sqrt{2\pi\delta}}e^{\frac{1}{-2\alpha^{2 }}\left\lVert D_{LR}a-I_{LR}\right\rVert_{2}^{2}} \tag{11}\]  
By incorporating Formulas (10) and (11) into Formula (9), we derive the resulting Formula (12):  
\[ \begin{split}&\alpha^{*}=\underset{\alpha}{\text{arg}\ \underset{\alpha}{\text{max}}\log p(I_{LR}\ \mid\ D_{LR},\alpha)+\log p(\alpha)}\\ &=\underset{\alpha}{\text{arg}\ \underset{\alpha}{\text{max}}- \frac{1}{2\delta^{2}}\left\lVert D_{LR}\alpha-I_{LR}\right\rVert_{2}^{2}+\frac {1}{-2b}\left\lvert\alpha\right\rvert_{1}}\\ &=\underset{\alpha}{\text{arg}\ \underset{\alpha}{\text{min}}}\left\lVert D_{LR}\alpha-I_{LR}\right\rVert_{2 }^{2}+\frac{\delta^{2}}{b}\left\lvert\alpha\right\rvert_{1}\end{split}  \tag{12}\]  
Equation \(\lambda=\delta^{2}/b\) can be observed from the aforementioned formula. Given the sparsity of \(\alpha\), it is assumed that the scale parameter \(b\) remains relatively constant, while \(\lambda\) should be increased in the presence of higher input image noise. For the experiment, a value of 0.2 was assigned to \(\lambda\).
# Study and Investigation on 5G Technology: A Systematic Review
## 5 Description of Novel 5G Features over 4G
### Beamforming  
Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [84].  
Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [21], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [85]. Figure 8 shows the pictorial representation of communication with and without using beamforming.  
Figure 7: Pictorial representation of communication with and without small cells.
## 3 Results and Discussion
### Genome Sequencing and Annotation
#### 3.3.2 Prediction of Gene Clusters for Secondary Metabolite Synthesis in Strain Q3-6  
By using the antiSMASH software 6.0, 13 clusters of secondary metabolite synthesis genes were predicted (Figure S2), including RiPP-like, three NRPS, NI-siderophore, NRPS-metallophore, NRPS-like, terpene, LAP, T1 PKS, lanthopeptide-class-ii, betalactone, and two RRE-containing genes (Table S1). The enzymes and proteins encoded by these gene clusters are responsible for the synthesis of a wide range of structurally complex and functionally diverse secondary metabolites. The diversity of these products enables the strains to respond to environmental changes. One of them with 100% similarity is cluster3, which is petrobactin synthesized from NI-siderophore. And seven others could not be  
\begin{table}
\begin{tabular}{c c c} \hline
**Features** & **Numerical Value** \\ \hline Genome size (bp) & 5,776,368 \\ G + C content (mol\%) & 34.92 \\ Number of CDS & 5804 \\ CDS as a proportion of the genome (\%) & 98.31 \\ \hline \end{tabular}
\end{table}
Table 1: Genomic characterization of strain Q3-6.  
Figure 1: The FastTree is based on 120 ubiquitous single-copy proteins. Bootstrap values above 50% (1000 replicates) are shown at branch nodes. Filled circles indicate that the same topology was also obtained using the IQ-TREE algorithm. Bar: 0.0050 substitutions per nucleotide position.  
matched to known gene clusters, suggesting that they are unknown gene clusters with a high probability of having the potential to produce novel compounds.
Predicting parking space availability is a vital area of research that can significantly improve the efficiency of parking space utilization and reduce traffic congestion. As technology advances, more innovative solutions are likely to emerge that will transform the way we manage parking allocation and enhance the overall driving experience. The studies reviewed above demonstrate that deep learning techniques, particularly the transformer, are commonly employed. However, these techniques require substantial computational power, and therefore cloud computing may be necessary. Nonetheless, the high computational cost and the increasing number of prediction requests may result in latency problems, even with the use of cloud computing. In this work, we propose a fog computing-based system that predicts parking time for each vehicle and parking lot availability based on parking history records. The system incorporates a machine learning algorithm integrated into the cloud module, which reduces response time and provides data analytics on parking statistics. By utilizing this system, cities can optimize their parking spaces, reduce traffic congestion, and enhance the efficiency of daily life. This paper presents several contributions, including the development of a fog computing platform to minimize response delays in the system. Additionally, infrastructure based on the AdaBoost algorithm, which is of low computational cost, is proposed for integration to a fog node. This model is considered as a generalization of the autoregressive model. Another contribution is the creation of a mobile application that acts as the user interface for the system. Finally, various machine learning techniques are compared to determine the most suitable method for parking space prediction. Through these contributions, the paper provides a comprehensive framework that enhances the efficiency and accuracy of parking space prediction. The remainder of this paper is structured in the following manner. Section 1 provides an introduction to the theory of fog computing and the AdaBoost algorithm. Section 2 presents the proposed fog computing system, which includes the fog node and the user interface, implemented as a mobile application. Section 3 describes the methodology used for experimentation, while Section 4 presents the results obtained from the prediction and latency calculations. Finally, Section 5 summarizes the conclusions drawn from the research.
Therefore, we investigated and compared the binding affinity to di- and triphosphate nucleosides, dinucleotides and metabolites containing different bases (adenine, hypoxanthine, and guanine) for a set of ligands previously reported as hNudt16 substrates: m\({}^{7}\)GppPG [13; 30], NADH [30], NAD [13; 30] and dpCoA [13], GDP [16], IDP [15; 17; 16], free ADPr [19; 20; 21; 22; 23], GppPG [13], and CDP [16], novel unpublished compounds: GppG, m\({}^{7}\)GppG, m\({}^{7}\)GDP, and AppA, as well as control monophosphate compounds IMP (inhibitor with known binding parameters [17]) and GMP. We focused on free compounds because in our previous work we did not observe significant difference in hydrolysis when compounds (m\({}^{7}\)GppG or GppG) were bound to the RNA chain [12]. To study the binding affinity, we used the E76Q hNudt16 mutant, which shows impaired hydrolytic activity. To gain insights into the position of the different ligands with respect to the hNudt16 structure, we performed molecular docking of selected ligands to the hNudt16 crystal structure.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 1 Introduction
### Main Contribution  
In this paper, we focus on the design of novel mathematical models for covering-based problems for the use-case of the infrastructure optimization in modern wireless networks. The developed modelsare the key enablers for the automatic base station deployment while building new networks as well as for the optimized location of currently deployed base stations. Our main contribution lies in the development of two mathematical models that deal with the task of direct capacity assignment and while considering the existing network infrastructure. These models are implemented and verified through prepared 5G networks datasets defining the common gNodeB parameter as well as the expected average throughput required by each user. Additionally, we prepared a dataset collected from the case study scenario covering the deployment of cellular infrastructure in the selected city district in Central Europe.  
The remaining part of this paper is structured as follows. In Section 2, we introduce the newly created mathematical model for covering-based problems including direct capacity assignment. In Section 2.3, we discuss an extension for the capacity-based location problems with the consideration of the existing network infrastructure (i.e., already deployed gNodeB), which co-exists with the new one. The implementation perspective for the developed mathematical models, such as data representation and the whole computation concept, is described in more detail in Section 3. Finally, Section 4 introduces the results obtained through the numerical simulations and Section 5 is summarizing our main conclusions.