esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Drought Index  
The study conducted by [PERSON] (2002) found that there were many isoclines in the LST-NDVI feature space; they simplified these feature space into a triangle shape and proposed the TVDI [34]. The TVDI is calculated using Equation (1).  
\[TVDI=\frac{LST-LST_{min}}{LST_{max-LST_{min}}} \tag{1}\]  
where \(LST\) is the land surface temperature of the observed pixel, and \(LST_{max}\) is the maximum observation temperature corresponding to the NDVI value of the observed pixel, that is, the dry edge, as follows:  
\[LST_{max}=a+b\times NDVI \tag{2}\]  
\(LST_{min}\) is the minimum observation temperature corresponding to the NDVI value of a specific pixel, the wet edge, as follows:  
\[LST_{min}=c+d\times NDVI \tag{3}\]  
\(NDVI\) is the normalized difference vegetation index of the observed pixel; \(a\), \(b\), \(c\), and \(d\) are the coefficients of the linear dry- and wet-edge linear fitting equations, respectively. The TVDI values range from 0 to 1, where TVDI = 1 indicates that the soil has no evaporation or a limited moisture supply, and TVDI = 0 indicates that the soil has maximum evaporation or unlimited moisture supply. The drought was divided into five grades by using the method of dividing the drought level in arid and semiarid areas, as follows: humid (0 \(\leq\) TVDI \(<\) 0.2), normal (0.2 \(\leq\) TVDI \(<\) 0.4), slight drought (0.4 \(\leq\) TVDI \(<\) 0.6), drought (0.6 \(\leq\) TVDI \(<\) 0.8), and severe drought (0.8 \(\leq\) TVDI \(<\) 1) [49].
# Localized Necking Model for Punching Fracture Simulation in Unstiffened and Stiffened Panels
## 2 Plasticity and Fracture Models  
### Stress State Parameters  
The stress state for an isotropic solid material can be represented by the non-dimensional parameters, stress triaxiality (\(\eta\)) and Lode angle parameter (\(\overline{\theta}\)), which are expressed in terms of stress tensor invariants:  
\[I_{1}=\text{tr}[\sigma], \tag{1}\]  
\[J_{2}=\frac{1}{2}\mathbf{s}:\mathbf{s}, \tag{2}\]  
\[J_{3}=\text{det}[\mathbf{s}], \tag{3}\]  
where \(I_{1}\) is the first invariant of the Cauchy stress tensor (\(\sigma\)), and \(J_{2}\) and \(J_{3}\) are the second and third invariants of the deviatoric stress (\(\mathbf{s}\)), respectively. The stress triaxiality and Lode angle parameters are expressed as follows:  
\[\eta=\frac{I_{1}}{3\sqrt{3J_{2}}}, \tag{4}\]  
\[\overline{\theta}=1-\frac{2}{\pi}\text{arccos}\left[\frac{3\sqrt{3}}{2}\frac{ \left(J_{3}\right)}{\left(J_{2}\right)^{3/2}}\right]=1-\frac{2}{\pi}\text{ arccos}\left[\frac{27}{2}\frac{J_{3}}{\overline{\sigma}^{3}}\right]; \tag{5}\]  
\(\overline{\theta}\) varies between axisymmetric compression (\(\overline{\theta}=-1.0\)) and axisymmetric tension (\(\overline{\theta}=1.0\)). Based on the modified Haigh-Westergaard coordinates \(\{\overline{\sigma},\;\eta,\overline{\theta}\}\), the principal stress (\(\sigma_{1}\geq\sigma_{2}\geq\sigma_{3}\) ) can be reconstructed as follows:  
\[J_{3}=\text{det}[\mathbf{s}]. \tag{6}\]  
The stress triaxiality and Lode angle parameters are expressed as follows:  
\[\sigma_{1}=\overline{\sigma}\left(\eta+\frac{2}{3}\cos\left[\frac{\pi}{6} \left(1-\overline{\theta}\right)\right]\right), \tag{7}\]  
\[\sigma_{2}=\overline{\sigma}\left(\eta+\frac{2}{3}\cos\left[\frac{\pi}{6} \left(3+\overline{\theta}\right)\right]\right), \tag{8}\]\[\sigma_{3}=\overline{\sigma}\bigg{(}\eta-\frac{2}{3}\cos\Big{[}\frac{\pi}{6}\left(1+ \overline{\theta}\right)\Big{]}\bigg{)}. \tag{9}\]  
The relationship between stress state parameters \(\{\eta\), \(\overline{\theta}\}\) under plane stress is defined as  
\[\overline{\theta}=1-\frac{2}{\pi}\text{arccos}\bigg{[}\frac{27}{2}\eta\bigg{(} \eta^{2}-\frac{1}{3}\bigg{)}\bigg{]}. \tag{10}\]
## 4 Discussion
### Response of SM and VPD to Vegetation Growth Peak Under Different Hydrothermal Conditions  
Broadly speaking, elevated VPD and reduced SM typically hinder vegetation growth. However, an opposite trend is shown in some regions in Figure 6, where VPD is positively correlated and SM is negatively correlated. First, SM exhibited a negative correlation in areas with higher precipitation levels. This can be explained from two major perspectives. Firstly, a moderate increase in precipitation can directly enhance soil moisture, making it more favorable for plant growth and resulting in a positive correlation between the vegetation peak values and SM. In addition, excessive rainfall may lead to a reduction in solar radiation, thus impacting the photosynthesis in plants and consequently decreasing the vegetation peak values [74; 75; 76; 77]. Secondly, with the gradual warming of the global climate, the corresponding increase in VPD is notable. VPD not only hinders plant photosynthesis and stomatal conductance, but also elevates plant transpirational water loss, thereby negatively impacting the vegetation peak values [58]. However, Figure 6 shows that some regions exhibit opposite trends in VPD, while the regions with positive correlations in VPD are mostly concentrated in areas with higher temperatures and precipitation. The main reason may be that the increase in VPD promotes photosynthesis when the temperature is suitable and the water availability is sufficient.  
Due to VPD's regulation of plant stomata, plants will close their stomata to reduce water loss when VPD is high. However, this closure also limits the absorption of CO\({}_{2}\) by the plants, leading to a decrease in photosynthesis and affecting the peak growth of the vegetation [31]. On the one hand, when the temperature rises, it leads to an increase in VPD, which in turn reduces the photosynthesis of the vegetation [58]. Higher temperaturesalso accelerate water evaporation, thereby reducing SM. Additionally, low soil moisture and high VPD both limit the photosynthesis of the vegetation [36]. On the other hand, increased precipitation enhances SM, and the subsequent rise in evapotranspiration reduces VPD, thereby promoting the photosynthesis of the vegetation [78]. However, even with sufficient precipitation, rising temperatures can lead to increased evapotranspiration, thereby inhibiting plant growth. Conversely, plants will not experience water stress when the precipitation is insufficient as long as the temperature is suitable [79]. Therefore, the multifaceted impacts of SM and VPD on vegetation under varying temperature and precipitation conditions warrant further exploration. From the results of this study, it is found that SM and VPD follow established patterns in their effects on vegetation when temperature and precipitation are within normal ranges; however, they exhibit completely opposite effects under certain extreme conditions.
## 2 Generic Multi-Objective Optimization Problem in Wireless Sensor Networks  
The generic multi-objective optimization problem consists of four segments: (1) inputs; (2) required output; (3) objectives; and (4) constraints. Figure 1 shows different possibilities for each part of the problem. In the generic resource allocation problem, the input parameters/decision variables are set by the network operators or the regulatory authorities. For example, selection of transmit frequency is influenced by the surrounding radio frequency environment and the regulatory rules. The selection of frequency can affect the transmission range of the sensors and ultimately many important performance parameters namely, coverage, bit error rate and delay. Increasing or decreasing the transmit power can significantly impact many desirable objectives namely, maximizing energy efficiency, link quality, network life time, reliability, coverage, cost and packet error rate. In [38], the authors have proposed an optimization formulation to maintain sensing coverage by keeping a minimum number of active sensor nodes and a small amount of energy consumption in wireless sensor network. Energy consumption has been considered in [39] by simultaneously satisfying delay and reliability through a multiobjective optimization algorithm. Total energy and residual energy of the nodes can also affect many performance indicators for example, coverage, throughput, network life time and packet error rate. A multi-objective formulation has been used in [40] to achieve a tradeoff solution between energy consumption and packet error rate. Location and density of the sensors determine the overall cost and the network performance in terms of observability, coverage, transmission range, reliability and energy consumption. Practical optimization problems relating to wireless sensor networks are constrained by many factors namely, network connectivity, interference, quality of service, transmit energy, coverage, topology, density, cost, latency, reliability and delay. These constrained optimization problems are expected to precipitate in optimal location of sensors, optimal number of sensors, optimal scheduling, optimal transmit power, optimal coverage, optimal throughput, optimal delay, optimal cost, optimal packet error rate, fairness and reliability. Nature of multi-objective optimization problem will change in accordance with certain input parameters, required objective function to optimize and the constraints imposed by the specific area of sensor network deployment.
## 4 Discussion
### Future Work  
Future work will focus on improving the discriminator loss function, for example, Wasserstein loss [58] could be more effective in training the discriminator network since the feedback is more nuanced compared to the stricter class probabilities from the BCE loss. The training dataset for CGAN can be built based on the different biomes and seasons to capture the relationships between the two sensors for building a more spatially generalizable model. Similar to the harmonization task of L8 and S2, the CGAN model can also be used to harmonize L8, Landsat 7 ETM+ (L7), and Landsat 5 (L5). Generating L8-like data from L7 (launched in 1999) and L5 (launched in 1984) will allow us to take advantage of the entire Landsat archive, building upon the works of [PERSON] et al. [59] and [PERSON] et al. [60]. Reconstructed time series from combining these sensors could be used to analyze the leaf area index to further demonstrate the potential of the method. The CGAN model can be trained to translate S2 to predict higher spatial resolution PlanetScope-like spectral bands at 3 m, building on the work by [PERSON] et al. [61]. Predicting near-daily PlanetScope-like imagery from Sentinel-2 would generate higher-quality publicly available data.
## 2 Overview of Random Linear Network Coding
### RLNC Extensions to Erasure Networks  
In this paper, we restrict our attention to single-hop erasure channels, either in unicast or multicast/broadcast scenarios. Such models will be sufficient for our RLNC-based mobile video delivery considerations later in Sections IV and V. Before proceeding, we make two remarks.  
**RLNC and Rateless Codes:** For single-hop channels considered in this paper, RLNC schemes described above represent instances of rateless codes [30]. Thus one can replace RLNC with other popular classes of rateless codes such as LT codes [31] or Raptor codes [32]. Indeed, Raptor codes provide near-optimal performance under significantly lower decoding complexity, thus allowing for larger source block lengths. The main benefit of using RLNC is that their usage is easily extended to multi-hop erasure network models where coding is performed in intermediate nodes. On the other hand, the source blocks lengths \(K\) in video delivery scenarios are typically small, thus using RLNC usually does not incur significant decoding complexity penalty.  
**Extensions to Erasure Network Models:** RLNC emerged as a practical solution to the network coding problem, where RLNC is applied in intermediate nodes of erasure network models [17]. Since then, RLNC has been investigated in various erasure networks scenarios. Among these, we point out to line networks [33], and more general multicast and multiple-unicast models [34; 35], as the models of interest for RLNC applications in future dense mobile cellular networks.
## 2 Materials and Methods
### Parameterization  
We used in situ measurements of ET and GPP with EC systems at the 63 sites across the globe to optimize \(b_{2}\), \(b_{3}\), \(a_{1}\), \(g_{0}\), \(\varepsilon_{\mathrm{max}}\) and \(d\). We first optimized the parameters site by site, yielding 63 site-specific parameter sets. The parameters were optimized by Monte Carlo simulation, which was described in detail in our previous study [14]. Briefly, we performed 10,000 Monte Carlo simulations to select ten top-performance parameter sets, and the mean of the ten top-performance parameter sets was regarded as the best-fit parameter set. As expected, the site-specific parameters resulted in desirable model performance at most sites. For regional application of the model, we next calculated the bulk average values of the parameters for each biome based on the site-specific parameters; i.e., all parameter sets of the sites belonging to the same biome were averaged as the parameter set for that biome. Note that the flux data and meteorological data at some sites were gap-filled due to diverse technique problems causing the data missing, which might cause large uncertainties if the gaps were very big, and this in return may cause unreliable parameterization. To minimize this adverse effect we excluded the sites with poor calibration performance, i.e., if the \(R^{2}\) between EC measurements and model predictions was less than 0.6, the parameter estimated at this site was not used to estimate the biome-average parameters. Finally, the estimated parameter sets at three sites (i.e., ID-Pag, US-Ha1, and DHS in Table S1) were excluded.  
At the very beginning, we calibrated the model using the measurements from half of the sites, and the measurements from the remaining sites were used to test the model performance (Figure S1). Comparing with the results with all sites being used for calibration, we yielded very close parameter estimates for each biome, and the simulated ET was nearly identical (data not shown). Assuming thatusing data of more sites would make the estimated parameters more convincing, we therefore used the parameters with all sites for calibration in this study. All results of the model simulation in this paper were based on the biome-specific parameter sets.
## 3 Results and Discussion  
### ITC Inhibit S. dysenteriae Growth and SHIGA Toxin Production  
A desirable antibacterial compound must be safe for therapy, should inhibit bacterial growth and development and also preferably inhibit virulence factor secretion. Importantly, to avoid unnecessary burden for the patient's organism, the lowest possible effective concentration should be used. Thus, a first step to assess the antimicrobial potential of a given compound is determination of the minimum inhibitory concentration (MIC). Three different isolates of _S. dysenteriae_ were used for the assessment of the ITC effect. For this work, we chose SFN and phenethyl PEITC as these ITCs have been quite well studied for their effects in humans. Moreover, PEITC was shown to be a potent antibacterial agent against _E. coli_ and _S. aureus_ as reported in [29], and also against EHEC strains [19]. The antibacterial effect of SFN and other aliphatic ITC was also reported [22; 30] and furthermore, as part of the daily diet SFN is known for its chemopre and anticancer effect [31; 32]. The determination of MIC (Table 1) indicates that both ITCs exert their effect against all tested _S. dysenteriae_ isolates, as well as against _S. sonnei_ and _S. flexneri_.  
The minimum inhibitory concentrations are relatively low for S. _dysenteriae_ strains (in the range between 11-44 and 41-81 mg/mL, for SFN and PEITC, respectively), which enhances the possibility of employing these ITC in the therapy of _Shigella_ infection. Notably, in comparison to the MIC for EHEC strains [19; 20], the growth of _S. dysenteriae_ representatives was impaired at the same or lower range of compounds. Nevertheless, the other two _Shigella_ representatives, _S. sonnei_ and _S. flexneri_, were less sensitive to the ITCs treatment with their MIC values greater than 100 mg/mL, and therefore we decided to exclude them from further analysis. Next, we assessed growth of _S. dysenteriae_ in the presence of varying concentrations of the ITCs tested, and found that even at concentrations of \(\frac{1}{4}\) MIC, the ITCs were able to significantly inhibit growth (\(p<\)0.001) of bacterial cultures in a rich liquid medium (Figure 1). Interestingly, the PEITC antibacterial effect does not appear to be dose-dependent, as complete growth inhibition was observed for all concentrations tested and for all strains. We observed _S. dysenteriae_ Boon/76 was the most sensitive to SFN. Thus, we can conclude that both SFN and PEITC act as effective antibacterial agents against _Shigella_ strains, providing a promising course of further studies.
# Overview and Perspectives for Vehicle-Integrated Photovoltaics
## 2 Results and Discussions
#### 2.1.1 Overall View of the Market Offers and R&D Demonstrators
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Manufacturer** & **Model/Project** & **Stage** & **Year** & **Vehicle** & **PV Surface** & **PV** & **Cell Technol-** & **PV Use** & **References** \\ \hline Train & Byron Bay & & & Proof of concept & & & 6.5 & Si & Prop. & [51] \\ \hline Truck & TSSC & TSSC & Commercial & & & 6.4 & 1.2 & Si & Aux. & [12] \\ \hline Truck & Volvo & SuperTruck & Proof of Cab & & 2014 & & 0.27 & & Aux. & [24, 52] \\ \hline Truck & Renault & \begin{tabular}{c} Optifuel Lab \\ 2 \\ \end{tabular} & \begin{tabular}{c} Proof of concept \\ \end{tabular} & 2014 & & 31.1 & 4.6 & \begin{tabular}{c} Si mono \\ IBC/22.0\% \\ \end{tabular} & Aux. & [11] \\ \hline Truck & Renault & & & & & & & & \\ Volvo Truck & & Development & & & & & & \\ \hline Truck & Navistar & Catalyst & \begin{tabular}{c} Proof of concept \\ \end{tabular} & 2017 & & 22.4 & 3.64 & Silicon poly & Aux. & [54, 55] \\ \hline Truck & Deimler & SuperTruck & \
\begin{tabular}{c} Proof of concept \\ \end{tabular} & 2015 & & & & Aux. & [56] \\ \hline Truck & Mitsubishi and Nippon & i Cool Solar &
\begin{tabular}{c} Proof of concept \\ \end{tabular} & 2010 & & 0.9 & Organic/4.0\% & Aux. & [57] \\ \hline \hline \end{tabular} Note: Prop., propulsion; Aux., powering accessories; Si, silicon; IBC, interdigitated back contact; HIT, heterojunction.  
\end{table} 
Table 1: _Cont._  
Figure 2: Number of initiatives for several vehicle types and their development stage. Aspects such as module curvature and electrical module architecture (series or series-parallel cells, number of bypass diodes, etc. ), as well as vehicle system self-consumption, will lower the energy provided by PV during both parking and drive modes. Other aspects can impact the solar kilometers but not the energy provided as vehicle efficiency. The effects of added weight and changes in aerodynamic drag (if any) on the on-board photovoltaic should be considered when evaluating the energy and solar kilometers provided.
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 1 Introduction
We thoroughly compare parameters obtained using physics-informed neural networks (PINNs) and those derived from fully connected deep neural networks (DNNs). To assess the effectiveness of both methodologies, we focus on two key aspects: computational efficiency and accuracy. Computational efficiency is evaluated based on the time and resources required to achieve convergence, while accuracy is measured in terms of the fidelity of the parameters to known or established values. We employ a suite of established error metrics, including mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), to evaluate the performance of the proposed method rigorously. These metrics provide a comprehensive view of the method's accuracy and reliability in different scenarios. The evaluation process is designed to validate the efficacy of PINNs in modeling dynamical systems and offer insights into how they compare with traditional DNN approaches. By analyzing the results through these error metrics, we can draw informed conclusions about the practicality of employing PINNs for complex dynamical systems, particularly regarding their ability to provide precise and computationally efficient solutions. 4. We show that employing the PINN algorithm to this specific problem becomes computationally more efficient as the number of layers increases, outperforming DNNs in terms of computational time. This finding suggests that PINNs can provide faster processing rates for complex computational tasks, providing a substantial advantage for time-sensitive applications. The paper is organized as follows: Section 2 discusses the systems of ordinary differential equations used in the study. Section 3 explores the neural network used for analysis. Section 4 examines error metrics for model performance assessment. Section 5 analyzes computational simulations of dynamical systems. Finally, Section 6 concludes the paper, summarizing the study's main findings and key points.
## 3 System Modeling
### Energy Model
\tag{2}\]  
where \(E_{dec}\) is the energy spent by transmitter on running the radio electronics, \(\varepsilon_{fs}\) is the energy dissipated by power amplifier depending on the Euclidean distance \(d_{V_{i}V_{j}}\) between the transmitter and receiver when free space fading is considered for distance less than \(d_{o}\), and \(\varepsilon_{mp}\) is the energy dissipated by power amplifier depending on Euclidean distance \(d_{V_{i}V_{j}}\) between transmitter and receiver when multi-path fading is considered for distance greater than \(d_{o}\). The threshold distance is given as \(d_{o}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}}\). Similarly, the energy required to receive \(l\) bits of data over distance \(d\) can be computed in Equation (3) as:  
\[E_{R}=\ E_{dec}l \tag{3}\]  
Likewise, the energy spent by CM or CH at the beginning of each round for sensing \(l\) bits of data can be analyzed as \(E_{sense}=\ E_{dec}l\). Here, we have assumed that the distance between CM and CH is shorter than the distance between CH and BS. Thus, we use \(\varepsilon_{fs}\) (free space fading model) for communication between CM and CH and \(\varepsilon_{mp}\) (multi-path fading model) for communication between CH and BS. Accordingly, the energy consumed by each CM can be calculated in Equation (4) as  
\[E_{CM}=\ E_{sense}+\ E_{T}=\ E_{dec}l+\ E_{dec}l+\ \varepsilon_{fs}ld_{V_{i}V_{j}}^{2} \tag{4}\]  
Each CH aggregates the received sensory data and then forwards it towards BS. The energy consumed by each CH can be calculated in Equation (5) as  
\[ \begin{array}{cc}E_{CH}&=E_{sense}+\ \Big{(}\frac{N}{N_{C}}-1\Big{)}E_{R}+ \Big{(}\frac{N}{N_{C}}\Big{)}|E_{A}+\ \Big{(}\frac{N}{N}\Big{)}E_{T}\\ &=E_{dec}l+\ \Big{(}\frac{N}{N_{C}}-1\Big{)}E_{dec}l+\Big{(}\frac{N}{N_{C}} \Big{)}\frac{E_{dec}}{R_{CC}^{2}}+\Big{(}\frac{N}{r}\Big{)}E_{dec}l\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+ \Big{(}\frac{N}{r}\Big{)}\varepsilon_{mp}ld_{V_{i}V_{j}}^{4}\end{array}  \tag{5}\]  
where \(N_{C}\) represents the number of clusters in the network, \(\frac{N}{N_{C}}\) is the working sensor nodes per cluster in which we have 1 CH and \(\frac{N}{N_{C}}-1\) CMs. \(E_{A}\) represents the energy consumed during aggregating data packets at CH, \(r\) is the compression ratio and \(R_{CC}\) is the communication to computation ratio used only during aggregation in this case. It is pertinent to mention here that the total energy consumed in a cluster is the sum of energies consumed by all CMs plus the energy consumed by their corresponding CH. The total energy consumed by a cluster \(E_{cluster}\) is given in Equation (6) as  
\[E_{cluster}=\ E_{CH}+\ \sum_{i=1}^{(\frac{N_{C}^{i}}{N_{C}}-1)}E_{CM_{i}} \tag{6}\]