esa-sceva/satcom-chunk-collection huggingface dataset:
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials
### Synthetic Biomaterials
#### 4.1.6 Poly(glycerol sebacate) (PGS)  
Poly(glycerol sebacate) (PGS) is a biocompatible and elastomeric polymer derived from the condensation reaction of glycerol and sebacic acid, resulting in a flexible and cross-linked polymer. It possesses interesting physical features such as high elasticity, tunable mechanical properties, biodegradability, and biocompatibility [214]. Cells can easily adhere, migrate, and proliferate on PGS surfaces, promoting tissue restoring. The flexible nature of PGS allows it to conform to surrounding tissues, providing mechanical support, and maintaining cell viability [215].  
PGS elastic properties make it particularly suitable for engineering heart tissues that require mechanical functionality [216].  
Researchers have explored PGS's potential in developing cardiac patches, bioartificial myocardium, and cardiovascular implants. PGS-based constructs have shown the ability to promote cell growth, enhance contractile function, and improve cardiac tissue regeneration [217].  
A brief summary of synthetic biomaterials described is showed in Table 2.
## 5 Experiments
### Comparative Experiments on Different Time Scales
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\text{L}_{seq}\)} & \multicolumn{3}{c}{**L\({}_{pred}\)**} \\ \cline{2-13}  & \multicolumn{3}{c}{**4**} & \multicolumn{3}{c}{**12**} & \multicolumn{3}{c}{**24**} & \multicolumn{3}{c}{**48**} & \multicolumn{3}{c}{**96**} & \multicolumn{3}{c}{**192**} & \multicolumn{3}{c}{**384**} \\ \cline{2-13}  & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline
4 & 0.0608 & 0.1460 & 0.3113 & 0.4109 & 0.6429 & 0.5987 & 0.8736 & 0.7330 & 1.2311 & 0.9496 & / & / & / & / \\
12 & 0.0573 & 0.1414 & 0.1788 & 0.2570 & **0.2883** & 0.3770 & 0.4664 & 0.4626 & 0.8106 & 0.7038 & 1.1504 & 0.8942 & / & / \\
24 & **0.548** & **0.1382** & **0.1752** & **0.2466** & 0.2976 & 0.3824 & 0.4189 & 0.4236 & 0.6769 & 0.6175 & 1.0912 & 0.8466 & / & / \\
48 & 0.0599 & 0.1473 & 0.1788 & 0.2576 & 0.3070 & **0.3465** & **0.3811** & **0.0412** & 0.5456 & 0.5272 & 0.1107 & 0.7934 & / & / \\
96 & 0.0638 & 0.1535 & 0.1940 & 0.2756 & 0.3108 & 0.3556 & 0.4742 & 0.4574 & **0.4861** & **0.4850** & 0.8165 & 0.6669 & 1.3481 & 0.9780 \\
192 & 0.0614 & 0.1518 & 0.2035 & 0.2829 & 0.3175 & 0.3625 & 0.4382 & 0.4440 & 0.5443 & 0.5071 & **0.5487** & 0.5459 & 1.0697 & 0.8089 \\
384 & 0.0733 & 0.1772 & 0.2071 & 0.2898 & 0.3308 & 0.3953 & 0.4799 & 0.4670 & 0.4746 & 0.4906 & 0.5912 & 0.5689 & **0.6718** & **0.6055** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance table of gated transformer at different time scales. The smaller the value, the better the performance. The best results are highlighted in bold and underlined. \(\text{L}_{seq}\) is the length of the input sequence. \(\text{L}_{pred}\) is the length of the pred sequence. \({}^{\prime}/^{\prime\prime}\) indicates the accuracy is too low and, therefore, meaningless in the specific situation. Figure 8: Comparison of predicted waveforms of different models. All data are standardized by Z-Score. The sampling period is 15 min. Figure 9: Comparison of predicted waveforms of different models. All data are standardized by Z-Score. The sampling period is 15 min. Each group of experimental results consists of an attention map of the input sequence and a comparison map of actual power and predicted power. (**A-D**) is the randomly selected four sets of experimental data.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 3 Design of Cooperative Search Strategy
### Q-Value Update Process  
When the \(i\)th UAV is in state \(\mathbf{s}_{i}(k)=[x_{i}(k),y_{i}(k),\phi_{i}(k)]^{\mathrm{T}}\), the policy \(u_{i}(k)\) is selected according to the largest Q-value in the \(\mathbf{s}_{i}(k)\) row in the Q-table. After executing it and arriving at state \(\mathbf{s}_{i}(k+1)\), then the immediate reward or penalty value is used to update \(Q(\mathbf{s}_{i}(k),u_{i}(k))\) in the Q-table. If the UAV gets a reward, \(Q(\mathbf{s}_{i}(k),u_{i}(k))\) increases; that is, when the \(i\)th UAV is next in the state \(\mathbf{s}_{i}(k)=[x_{i}(k),y_{i}(k),\phi_{i}(k)]^{\mathrm{T}}\), the maximum Q-value corresponding to the decision is selected. On the contrary, when the penalty value is obtained, the Q-value corresponding to the decision \(u_{i}(k)\) becomes smaller until it is not the maximum Q-value, then the values of other decisions are selected. When the Q-table finally converges, the optimal decision \(u_{i}(k)\) is obtained, from which the updating rule of the Q-value function can be obtained.  
\[Q(\mathbf{s}_{i}(k),u_{i}(k))=(1-\alpha)Q(\mathbf{s}_{i}(k),u_{i}(k))+\alpha[ r(k)+\gamma\underset{u\in A}{\max}Q(\mathbf{s}_{i}(k+1),u)], \tag{19}\]  
where \(\mathbf{s}_{i}(k)\) is the current state of the \(i\)th UAV, \(u_{i}(k)\) is the decision of the current choice, which is the variation of yaw angle, \(r(k)\) is the immediate reward value or penalty value, \(\underset{u\in A}{\max}Q(s_{i}(k+1),u)\) represents the maximum Q-value obtained by the policy \(u\) in the state \(\mathbf{s}_{i}(k+1)\), and \(\alpha\in[0,1]\) is the learning rate. When \(\alpha=1\), the original Q-value has no effect on the new Q-value, and all knowledge learned is new, but it is easy to cause Q-value instability; when \(\alpha=0\), the Q-value remains unchanged, and the learning stops. Therefore, \(\alpha\) determines the learning ability of the algorithm. \(\gamma\) is the discount factor, and \(\gamma=1\) means that there is no discount on the delay return; that is, it attaches great importance to the influence of the current decision on the future. \(\gamma=0\) means that the Q-value does not calculate the delay return (it only calculates the immediate return); thus, \(\gamma\) determines the importance that the learning algorithm attaches to the delay return. Below, we describe the specific design of \(r(k)\) in Equation (19) and the strategy selection in the learning process.
# Feasibility Investigation of SIW Cavity-Backed Patch Antenna Array for Ku Band Applications
## 5 Feasibility and Discussion on Material Choice  
The SIW cavity-backed 16 \(\times\) 16 antenna array optimized for the Ku band operated well, providing a good gain and a wide band. The cost reduction was another important factor. The PCB technology can be employed for large scale and low cost production. A sample without screws to assembly the layers was engineered via the prepreg press compression molding procedure starting from four different layers. This confirmed the same characteristics of gain and bandwidth and is illustrated in Figure 25.  
However, this kind of antenna is actually interesting for niche applications, when a low profile/small thickness/flat shape is strictly required and not for typical domestic applications. In fact, the cost of the employed Rogers Duroid 5880 layers makes the antenna array not competitive with respect to the conventional parabolic antenna. A lot of work has been devoted to understand whether a similar antenna could substitute the conventional parabolic one by considering different materials. We designed, fabricated, and characterized another antenna by employing Taconic RF35 A2 as the substrate material, which has a lower cost. The geometry was accurately optimized by considering the intermediate steps of 2 \(\times\) 2, 4 \(\times\) 4, 8 \(\times\) 8, and 16 \(\times\) 16. First, the prototype with the layers assembled with screws and then the engineered one were constructed, as shown in Figures 26 and 27.  
Figure 24: Cross polarization measurement of the 16 \(\times\) 16 antenna array gain at f = 11.8 GHz.  
Figure 25: Engineered prototype, Rogers Duroid 5880, 16 \(\times\) 16, dual layer, SIW cavity backed antenna array: (**a**) top view; (**b**) bottom view.  
They exhibited similar performances that were unfortunately quite low, with a gain less than 23 dBi. Therefore, these prototypes are less interesting for actual applications.
_Int. J. Mol. Sci._**2015**, _16_, 321-349; doi:10.3390/ijms16010321  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Review_  
**tRNAs as Antibiotic Targets**  
**[PERSON] and [PERSON] ***  
Department of Cell Biology and Physiology, the University of North Carolina at Chapel Hill,  
536 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC 27599-7545, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-919-843-1556.  
Academic Editor: [PERSON]  
_Received: 25 November 2014 / Accepted: 19 December 2014 / Published: 25 December 2014_  
**Abstract:** Transfer RNAs (tRNAs) are central players in the protein translation machinery and as such are prominent targets for a large number of natural and synthetic antibiotics. This review focuses on the role of tRNAs in bacterial antibiosis. We will discuss examples of antibiotics that target multiple stages in tRNA biology from tRNA biogenesis and modification, mature tRNAs, aminoacylation of tRNA as well as prevention of proper tRNA function by small molecules binding to the ribosome. Finally, the role of deacylated tRNAs in the bacterial \"stringent response\" mechanism that can lead to bacteria displaying antibiotic persistence phenotypes will be discussed.  
**Keywords:** tRNA; protein translation; antibiotics; stringent response; biogenesis
## 4 Dynamic Weighted Clustering Algorithm with Dual Cluster Heads
### Weight Calculation  
According to the weighting factors given by Equations (1)-(5), the weighting function of node \(i\) when selecting pCH and bCH can be denoted by  
\[W_{i}^{p}=w_{1}^{p}\cdot\triangle_{i}+w_{2}^{p}\cdot D_{i}+w_{3}^{p}\cdot PL_{ i}+w_{4}^{p}\cdot M_{i}+w_{5}^{p}\cdot E_{i} \tag{6}\]  
\[W_{i}^{b}=w_{1}^{b}\cdot\triangle_{i}+w_{2}^{b}\cdot D_{i}+w_{3}^{b}\cdot PL_{ i}+w_{4}^{b}\cdot M_{i}+w_{5}^{b}\cdot E_{i} \tag{7}\]  
where \(w_{1}^{k},w_{2}^{k},w_{3}^{k},w_{4}^{k},w_{5}^{k}\) are the weighting coefficients, and \(\sum\limits_{i=1}^{5}w_{i}^{k}=1\). Additionally, \(k\in\{p,b\}\), where \(p\) and \(b\) denote the selection of pCH and bCH, respectively. Due to the different functions of pCH and bCH, the corresponding weight coefficients are different during the selection of cluster heads. It should be noted that the selection criterion of the pCH in this paper is higher service efficiency, therefore the value of weight coefficients \(w_{1}^{p}\), \(w_{2}^{p}\), and \(w_{3}^{p}\) which represent the serving quality are relatively large, while the criterion of selecting the bCH is higher stability, thus the value of \(w_{4}^{b}\), \(w_{5}^{b}\) is larger. In other scenarios, the weight coefficients can be adjusted based on actual network requirements.
# Geometric Spherical Surfaces Emulating the Geostationary Orbit at Any Latitude with Zenith Links
## 3 Geometrical and Radio Electrical Parameters
\[\theta=2\times arctan\Big{\{}\frac{R}{h}\times sin\Big{(}\frac{\varphi}{2} \Big{)}\Big{\}} \tag{4}\]2 Antenna diameter as a function of the service area diameter, at 40 GHz for the GeoSurf constellation. **A** refers to h = 35,876 km; **B** to h = 20,000 km; **C** to h = 10,000 km, **D** to h = 1500 km. \[\theta=2\times arctan\Big{\{}\frac{R}{h}\times sin\Big{(}\frac{\varphi}{2} \Big{)}\Big{\}} \tag{4}\]3 Antenna diameter as a function of the service area diameter, at 30 GHz for the GeoSurf constellation. **A** refers to h = 35,876 km; **B** to h = 20,000 km; **C** to h = 10,000 km, **D** to h = 1500 km. \[\theta=2\times arctan\Big{\{}\frac{R}{h}\times sin\Big{(}\frac{\varphi}{2} \Big{)}\Big{\}} \tag{4}\]4 Antenna diameter as a function of the service area diameter, at 50 GHz for the GeoSurf constellation. **A** refers to h = 35,876 km; **B** to h = 20,000 km; **C** to h = 10,000 km, **D** to h = 1500 km. To guarantee the minimum signal-to-noise ratio required in the design, the radio frequency power onboard the satellites will largely depend on the free space distance. In other words, keeping constant the minimum signal-to-noise ratio in any possible propagation conditions, the frequency band (therefore, working always with the same tropospheric fading), the ultimate variable that determines the necessary power is the (practically zenith) distance between the satellite and the ground station, and therefore it sets the satellite constellation features.
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Interactive Engagement (Based on Constructivist Learning Theory)
#### 4.1.2. Learner-Learner Interaction in VR  
The domain of virtual reality (VR) not only magnifies the scholar's communion with content but also recalibrates the intricacies of inter-peer dialogues. Conventional linguistic acquisition frequently encompasses dyadic or collective engagements, wherein students' discourse, dramatize, or amalgamate efforts on assignments. VR elevates these dialogues to an unparalleled plateau of veracity and engrossment.  
Within a VR ambit, acolytes can assume personas, facilitating interactions in ambiances reflecting tangible life tableau. For illustration, scholars assimilating Spanish might be transported to a virtual bazaar in Barcelona, haggling with contemporaries, brokering terms, and effectuating acquisitions--such dialogues obligate acolytes to wield the dialect in pragmatic, instantaneous circumstances, bolstering eloquence and assurance.  
VR proffers a cosmopolitan dais. An understudy in Tokyo can effortlessly commune with a counterpart in Buenos Aires, surmounting spatial and ethnocultural chasms. This planetary discourse acquaints scholars with many vernaculars, tonalities, and idiomatic expressions, augmenting their linguistic arsenal and ethnocultural discernment.  
The alacity of reciprocation in VR stands as another boon. Articulatory aberrations or syntactical lapses can be promptly rectified by contemporaries, engendering a synergistic learning milieu. Beyond this, the VR interface can assimilate instantaneous trans-literation utilities and articulation manuals, aiding scholars amidst exchanges.  
VR metamorphoses scholarly interchanges from mere dialogic drills into profoundly engrossing, genuine, and international exchanges, rendering linguistic assimilation a communal and culturally opulent pursuit.
## 4 Fire Consequences of Pool Fires on Inclined Substrates
### Computational Model Validation  
The HRR of the pool fire and mesh resolution of the control volume, among other parameters, need to be defined adequately to ensure a certain degree of accuracy of the model. The HRR for the computational model can be calculated using the proposed formula for HRR prediction on inclined surfaces (Equation (4)). The material properties of JP-5 shown in Table 2 were used for the calculation.  
The mesh resolution of the computational model was determined using the plume resolution index \(D^{*}/\delta x\), where \(D^{*}\) denotes the characteristic fire diameter and \(\delta x\) represents the nominal mesh size. The characteristic fire diameter is defined in Equation (5). The recommended plume resolution for liquid pool fires ranges from 4 to 16 [33].  
\[D^{*}=\left(\frac{\dot{q}}{\rho_{\infty}c_{p}T_{\infty}\sqrt{\dot{g}}}\right)^{ 2/5} \tag{5}\]  
where \(\dot{q}\), \(\rho_{\infty}\), \(c_{p}\), \(T_{\infty}\), and \(g\) represent the HRR, air density, air specific heat, ambient temperature, and gravitational acceleration, respectively. The severity and duration of the fire were controlled using the RAMP function.  
To confirm the validity of the computational model, heat flux and heat dose predictions based on this model were compared with experimental measurements. An indoor pool fire experiment was conducted in a 12 m \(\times\) 10 m \(\times\) 12 m room, where JP-5 was ignited in a square tray, with the surface area of 0.5 m\({}^{2}\), located in the centre of the room. The heat flux was measured using heat flux transducers (Medtherm 64 series), which were positioned 2.2 m, 3.2 m, 4.2 m, and 5.2 m away from the centre of the pool in the x-direction. Figure 9 illustrates the setup of the pool fire experiment. The control volume of the computational model is indicated with a red dashed line.  
Figure 8: Comparison of the experimental data and pool dimensions predicted by empirical formulae: (a) length, (b) breadth, (c) area, [PERSON] et al. [18].  
Figure 10a shows the experimentally measured heat flux (located closest to the fire in Figure 9) and heat flux predicted by the fire simulation. The fire duration of the experiment was 1062 s, whereas it was 1064 s in the simulation. The overall trend of the experimental measurement was accurately simulated, with an error of 15.8% between the two measurements in the steady state (70-550 s). In Figure 10b, the heat dose, which is defined as the time integral of the heat flux, as shown in Equation (6), is shown. The heat dose at the end of the fire duration was 3890.7 kJ/m\({}^{2}\) for the experiment and 3953.4 kJ/m\({}^{2}\) for the simulation, which corresponds to an error of 1.5%. The comparison shows that the computational model can reasonably accurately simulate JP-5 pool fires.  
\[\mathcal{Q}=\int q(t)dt=\int-k\frac{dT(x)}{dx}(t)dt \tag{6}\]  
where \(Q\), \(q\), \(k\), and \(T\) represent the heat dose, heat flux, thermal conductivity, and temperature, respectively.  
Figure 9: Pool fire experiment setup: (**a**) schematic layout, (**b**) actual layout.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 3 Bus Depot Simulator  
Bus Depot Simulator is a quasi-real-time cosimulation program based on software Python and DigSilent PowerFactory. It simulates the operation of a large-scale bus depot taking into consideration buses arriving and leaving, ambient temperature, delays due to traffic, dis/charging processes, etc. Figure 2 shows the structure of the program with the belonging classes.  
The Controller manages the simulation by communicating with other classes such as Schedule, Depot and Environment, which are implemented in Python. The Controller also communicates with PowerFactory. The simulation runs in discrete time steps of one minute. For each minute, Python  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bus ID** & **Route ID** & \
\begin{tabular}{c} **Departure** \\ **Time** \\ \end{tabular} & **Arrival Time** &
\begin{tabular}{c} **SoC Before** \\ **Trip** \\ \end{tabular} & **SoC After Trip** \\ \hline
1 & 66,001 & 05:43 & 09:51 & 100\% & 80\% \\
2 & 66,002 & 06:04 & 14:50 & 100\% & 50\% \\
3 & 66,003 & 07:07 & 13:15 & 100\% & 60\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of an assembled driving schedule for buses at the bus depot Alsterdorf.  
Figure 2: Cosimulation process with Python and Power Factory.  
sends active power set points to PowerFactory based on the information, which buses are currently charging at which charging station. PowerFactory executes a Load Flow and sends the results back to Python. In that way, the user can follow the load profile of the depot in a quasi-real-time. During the initialization process, the Simulator creates all objects, establishes a connection with PowerFactory and loads the Schedule class. If there is no charging schedule in the Schedule class, the Simulator will charge the buses on demand as soon as they arrive to the depot. When a charging schedule is provided, the buses will charge only in the time interval defined in the schedule. In this way, the Simulator is independent from the Schedule class and can be used to validate any type of scheduling algorithms.  
Figure 3 shows one part of the Bus Depot Simulator and the tab \"Inside Depot\". This tab gives an overview of the buses currently at the depot together with their SoC. The figure also shows the flowchart with the co-simulation procedure.  
Besides the tab \"Inside Depot\", the Simulator also provides a deeper overview of different components of the system during the simulation, showing the following:  
* The decisions the Controller is making.
* An overview of the buses currently driving outside of the depot (the route they are driving and the expected arrival back at the depot).
* Load profile at different terminals within the depot (active and reactive power at the connection point as well as on all export terminals).
## 3 Results and Discussion
### Analysis of Study Variables in VSM  
The previous VSM of the company indicated that, during an 8 h period, production starts with an inventory of 5000 g of raw material, allowing for the processing of 62 units of 200 capsules in a working day. Therefore, it is necessary to work 10 h, achieving 6.2 units per hour, and each finished unit is produced in 9.67 min.  
To obtain the current situation of the company, it was necessary to complement the VSM with the following three important concepts: effective capacity, cycle time, and TAKT TIME (time required to complete one unit to meet the demand) [111]. It can be observed that the company's effective capacity is 63%, meaning that it is capable of satisfactorily fulfilling 63 of 100 units. The process efficiency is 43%, which is relatively low. Therefore, it is common to require extra time on days of increased production and the hiring of temporary personnel to meet market demands. The current cycle time of the company to produce two units is 25,820 s. The TAKT time is 221.76 s, indicating that this is the time within which a customer can acquire a product or service from the company. In other words, the production rate should be adjusted as close as possible to the TAKT time. Overall, the VSM identified areas for improvement in raw material reception, raw material selection, sterilization, and the grinding-pulverizing process.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
#### 3.2.3 Fuzzy Stochastic Approach  
A fuzzy stochastic approach offers a method to evaluate the risks associated with BTEX contamination of groundwater under various uncertainties. Li et al. [464] utilized a stochastic fuzzy approach, employing a modified fuzzy vertex method alongside a Monte Carlo simulation to forecast petroleum contamination in the subsurface. Their study, conducted using a 3D model of a petroleum-contaminated aquifer, underscored the significance of integrating uncertainties in critical hydrogeological parameters like permeability and porosity into the calculations to accurately depict the contamination extent. Maqsood [465] introduced this approach to quantify the relationships among uncertain hydrogeological parameters. Similarly, Zhang and Huang [466] adapted an integrated3D multiphase and multicomponent model, UTCHEM, in conjunction with an interval fuzzy modelling system for the subsurface (IIFMS) to project benzene concentrations over a 20-year simulation period, incorporating uncertainties in variables such as porosity, longitudinal dispersivity, and permeability.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 3 Materials and Methods
### Data Processing and Method
#### 3.2.3 LiDAR Waveform Processing
The LiDAR bathymetry data were calculated using the same reference surface as the optical image for the satellite-derived bathymetry. MLLW data (referring to the depth data in the Figure 6) are commonly used as the reference for bathymetric mapping. Therefore, MLLW data were selected as the starting surface. The conversion formula is as follows:  
\[H=h_{2}-h_{t}-L. \tag{14}\]