esa-sceva/satcom-chunk-collection huggingface dataset:
# Conservation Integrals in Nonhomogeneous Materials with Flexoelectricity
## 5 A Nonhomogeneous Bernoulli-Euler Beam Problem with Flexoelectricity
\begin{array}{l}G_{1}^{left}=\int_{\frac{1}{2}h}^{0}\Big{(}g^{E}-\sigma_{11}u _{1,1}+\tau_{113,3}u_{1,1}\Big{)}dz+\int_{\frac{1}{2}h}^{0}\frac{\alpha}{2}( \sigma_{11}u_{1}-\tau_{113,3}u_{1})dz\end{array}  \tag{78}\]Substituting Equations (69)-(71), (76)-(78) into Equation (75), the conservation integral can be finally obtained  
\[ \begin{array}{c}G_{1}^{J}=\Gamma_{1}\Big{(}e^{\alpha(L-l)}-e^{-\alpha l}\Big{)} +\Gamma_{2}\Big{(}e^{\alpha l}+e^{-\alpha(L-l)}\Big{)}+\Gamma_{3}\\ +\Gamma_{4}\Big{(}e^{\alpha(L-l)}+e^{-\alpha l}\Big{)}+\Gamma_{5}\Big{[}e^{ \alpha(L-l)}(L-l)-e^{-\alpha l}l\Big{]}\end{array}  \tag{79}\]  
where  
\[\Gamma_{1}=\frac{\big{(}4B_{3}^{2}\mu_{0}^{2}-16 Va_{0}B_{3}d_{0}+3B_{2}^{2}\mu _{0}^{2}\alpha^{4}\big{)}h^{2}-20 Va_{0}B_{3}\mu_{0}h+8V^{2}a_{0}^{2}}{16\mu a_ {0}} \tag{80}\]  
\[\Gamma_{2}=\frac{\alpha^{4}B_{2}^{2}h\big{(}4h^{2}C_{0}a_{0}+15h\mu_{0}d_{0}+ 18\mu_{0}^{2}\big{)}}{96a_{0}} \tag{81}\]  
\[ \begin{array}{c}\Gamma_{3}=\frac{\alpha^{2}B_{2}h^{3}\big{[}B_{2}\big{(}d_{0 }^{2}-C_{0}a_{0}\big{)}\alpha^{2}+(L-2L)B_{3}\big{(}d_{0}^{2}-C_{0}a_{0}\big{)} \alpha\big{]}}{24a_{0}}\\ +\frac{\alpha^{2}B_{2}h[12\mu_{0}^{2}g_{3}-d_{0}a_{0}V+(B_{3})\mu_{0}^{2}-Va_ {0}d_{0}L_{\alpha}]}{96a_{0}}\\ +\frac{\alpha^{2}B_{2}\big{(}10\mu^{2}d_{0}\mu_{0}B_{3}-8\mu_{0}a_{0}V-5 Va_{ 0}h_{0}L_{\alpha}\big{)}}{84a_{0}}+\frac{\alpha^{2}B_{2}B_{3}\big{(}d_{0}^{2} +3C_{0}a_{0}\big{)}h^{3}}{12a_{0}}\end{array}  \tag{82}\]  
\[\begin{array}{c}\Gamma_{4}=\frac{B_{3}h^{3}\big{[}B_{2}\big{(}d_{0}^{2}-C_{ 0}a_{0}\big{)}\alpha^{2}+2B_{3}\big{(}d_{0}^{2}+C_{0}a_{0}\big{)}+15b\mu_{0}B _{3}^{2}h^{2}\]  
\[ \begin{array}{c}\Gamma_{5}=\frac{2B_{3}^{2}h^{3}\big{(}d_{0}^{2}-C_{0}a_{0} \big{)}\alpha-3B_{3}hd_{0}a_{0}V\alpha}{24a_{0}}\end{array}  \tag{84}\]  
If the material is homogeneous, the gradient terms and the flexoelectric effect are omitted, i.e., \(\alpha=\mu_{0}=0\), the conservation integral reduces to  
\[G_{1}^{J}=\frac{h^{3}C_{0}}{24}\bigg{(}\frac{M_{0}}{A_{1}I}\bigg{)}^{2}+\frac{ h^{3}d_{0}^{2}}{24a_{0}}\bigg{(}\frac{M_{0}}{A_{1}I}\bigg{)}^{2}-\frac{-7d_{0}Vh}{9 6}\frac{M_{0}}{A_{1}I}+\frac{V^{2}a_{0}}{4h} \tag{85}\]  
Further, if the piezoelectric effect is also omitted, the conservation integral will reduce to the classical elasticity:  
\[G_{1}^{J}=\frac{M_{0}^{2}h^{3}}{24C_{0}I^{2}}+\frac{V^{2}a_{0}}{4h} \tag{86}\]  
This result is identical with the J-integral of classical elasticity in dimension in [34].
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 4 Discussion
### Limitations and Uncertainties of Reflectance Simulation  
Despite the encouraging results of the proposed method in this study, there are still some limitations that should be addressed. Firstly, forest phenology could affect the reflectance simulation and shadow fraction estimation. In this study, we assumed that the phenology of tropical rainforest would not experience much change. Figure 8 shows the time series of Normalized Difference Vegetation Index (NDVI) in the target area using Sentinel-2 images between January 2019 and December 2020. The NDVI values were calculated from the same 40 pixels described in Section 3.2. As shown in Figure 8, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Canopy Shape**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{2-3}  & **Reflectance Simulation** & **Shadow Simulation** \\ \hline C & 0.542 & 0.125 \\ E & 0.456 & 0.035 \\ HE & 0.385 & 0.032 \\ IHE & 0.537 & 0.129 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMSE of Sentinel-2 Band 4 reflectance simulation and shadow simulation in each canopy shape (C: cylinder, E: ellipsoid, HE: half ellipsoid and IHE: inverted half ellipsoid).  
average of the NDVI shows a minimum in April at 0.7 and a maximum in October at 0.85, indicating that it fluctuates by about 0.15 throughout the year. Although NDVI is affected by many other environmental factors, such as shadows [33] and the water content of leaves [34], there is still a slight change in the phenology. Consequently, the use of relative reflectance could be affected by phenology, making it difficult to apply this method to forests with noticeable phenological variation. Secondly, there is uncertainty in the creation of virtual forests. Allometric equations were used to create the virtual forest, and growth of the canopy was assumed to be orthotropic. However, canopy shyness is an important factor for canopy closure [35; 36; 37]. Therefore, there may be differences between the actual forested landscape and the virtual forest.
# Role of Kir4.1 Channel in Auditory Function: Impact on Endocochlear Potential and Hearing Loss
## 4 Discussion
### Kir Channel Family  
The inward rectifying potassium channels (Kir) are so called because they display a change in conductance based on the cell's polarization state. The entry of current into the cell is facilitated by their higher conductance during hyperpolarization than during depolarization. Kir channel conductance is dependent on the extracellular concentration of K\({}^{+}\) since changes in extracellular K\({}^{+}\) levels cause a displacement of the current outward [22]. The typical structure of Kir channels is composed of multiple subunits assembled together, with minor structural distinctions between various subtypes and species. Kir channels have been reported to be present in a wide range of tissues, including cardiomyocytes, endothelial cells, erythrocytes, neurons, glial cells, and epithelial cells [11; 14]. In the channels there are hydrophobic transmembrane segments, M1 and M2, which cross the cell membrane and are crucial for the formation of the ionic pore inside which the selection loop is the region that enables the selective passage of ions through the channel. Hydrophilic amino-(N\(-\)) and carboxylic (C\(-\)) terminals, respectively, located on the intracellular and extracellular sides of the membrane, are involved in the regulation of channel activity and its interaction with other proteins and regulatory factors. The gating region plays a role in regulating channel opening and closing in response to specific cellular stimuli, like changes in membrane potential or chemical modulator binding [23]. The Kir family consists of seven subfamilies that are classified by their molecular structure and electrophysiological properties and are grouped into four groups based on their biophysical characteristics as follows: (a) classic Kir channels (including the subfamily Kir2.x); (b) Kir channels with protein G (Kir3.x); (c) K\({}^{+}\) channels sensitive to ATP (Kir6.x); (d) K\({}^{+}\) transport channels (Kir1.x, Kir4.x, Kir5.x, and Kir7.x) [13].
## 1 Introduction  
### Preliminaries  
Non-orthogonal multiple access (NOMA) is a promising access scheme for 5G networks. It multiplexes multiple users on the same radio resources frequency, code, or time with different power levels in the power domain. In contrast with orthogonal multiple access (OMA), the NOMA technique utilises the power domain to multiplex multiple users simultaneously. Thus, NOMA can support 5G requirements, such as massive connectivity, enhanced spectral efficiency, and sum-rate, which can support a balanced data rate in a system. In NOMA, the base station (BS) superimposes the message on the same subchannel for the multiplexed users via superposition coding (SC); then, successive interference cancellation (SIC) is applied to the signal detection receiver [1, 2]. The NOMA system can provide balanced throughput for users in the network in accordance with the channel conditions.  
One fairness criterion that can be implemented involves using the strong channel condition as a relay to improve the data rate under poor channel conditions; this technique may improve reception reliability for users under poor channel conditions. However, this improvement can come at the cost of additional channel resources, such as dedicated time slots or power balance [3]. Hence, the power allocation scheme and user pairing can be applied to support fairness with less system complexity [4].
## 3 Results
### Influence of Natural Factors on GPP\({}_{\text{RF}}\)  
The SEM utilised in this study aimed to comprehensively analyse the direct and indirect influencing mechanisms of various natural factors, including PRE, TEM, FAPAR, PAR, and LAI, on vegetation GPP changes in the arid zone of Xinjiang. The model's goodness-of-fit assessment results demonstrated exceptionally high fit quality, as evidenced by the chi-square, CFI, SRMR, and RMSEA values of 0.817, 0.99, 0.002 (\(<\)0.05), and \(<\)0.05, respectively. These metrics collectively verified the reasonableness and validity of the model construction (Figure 8).  
Figure 7: (**a–e**) Spatial distribution of GPP in response to SPEI at different timescales in 2009; (**f**) correlation coefficients between vegetation GPP and SPEI09 in 2009.  
Among the natural factors, TEM emerged as the key factor significantly impacting vegetation GPP\({}_{\text{RF}}\) in Xinjiang. Specifically, TEM exhibited a highly significant positive driving effect, with a direct influence coefficient of 0.199 (\(p<0.001\)). Furthermore, TEM indirectly affected vegetation GPP by influencing LAI and FAPAR, contributing an indirect influence coefficient of 0.302 and an overall influence coefficient of 0.501; this demonstrates that TEM plays a complex, multilevel regulatory role in GPP. In contrast, PRE had a direct negative driving effect on vegetation GPP with an impact coefficient of \(-0.171\) (\(p<0.001\)). However, PRE also indirectly influenced GPP by affecting LAI, subsequently influencing FAPAR and, ultimately, GPP. The indirect impact coefficient is 0.209, contributing to an overall impact coefficient of 0.038.  
In summary, despite the direct negative effect, the overall impact of PRE on GPP exhibited a positive tendency when both direct and indirect effects were considered. PAR did not directly affect GPP but indirectly influenced it through its effects on LAI and FAPAR. The overall indirect impact coefficient was \(-0.298\), indicating a negative driving effect.  
In conclusion, this study not only quantified the direct and indirect effects of environmental factors on vegetation GPP in the arid zone of Xinjiang using SEM but also unveiled the potentially complex mechanisms underlying these effects. These findings provide an important scientific basis for a deeper understanding of vegetation growth in arid zones and its responses to climate change.
## 2 Emerging Cloud Technologies
### Cloud-Related Architectures  
As already mentioned, in order to support the requirements of different emerging applications, many new cloud-related architectures have been proposed in recent years. These architectures can be generally categorized based on their DC characteristics, such as DC location, number and size.  
A cloud-related architecture mainly includes one or more DCs and a number of end users, all connected together through telecommunication networks. In this case, as Figure 2 shows, we can divide cloud-related architectures into four main groups based on DC characteristics: fully centralized (FC), partly distributed (PD), fully distributed with centralized controller (FD-CC), and fully distributed (FD) [12].  
Figure 2: The cloud architecture taxonomy; ranging from fully centralized to fully distributed (inspired by [12]).  
In the FC architecture, there is a large-size DC and all end users connect to the central DC and receive services from it. PD architecture includes several geographically distributed DCs, and a telecommunication network connects them together. It also connects end users to DCs. DCs in the PD architecture are usually located closer to end users in comparison to the FC architecture. However, DCs are not still located in end users' premises. The FD-CC architecture includes several DCs that are located on end users premises (i.e., one DC for one or a few end users). However, controller(s) remain in one or more DCs in the core of the network. The DCs that are located in the end users premises are usually in the size of a physical machine (PM). The remaining DCs, in the core of the network, are only used for management aspects and they are not used for computing and storage services. Finally, in the FD architecture, unlike the FD-CC architecture, the management system is also distributed on end users' locations. It means that there is no DC in the core of the network in charge of controlling the resources [12].
## 2 Related Work
### Needs for Vision-Based FEQS Method  
In current FEQS methods, problems have been exposed, such as high labor and economic costs, limited application environment, continuous maintenance requirement, and transportation interruption. Thus, it is of practical significance to develop a better FEQS system that is free of human labor, low cost, and well-adapted to the operating environment. Currently, there is no suitable solution for non-contact truck weighing; although devices that are more advanced than scales have been developed, contact with vehicles and laborious installation are still required [18; 19]. Thus, high cost and limited application is unavoidable in weighing FEQS. However, for counting FEQS, as long as problems that need human judgment can be solved by unmanned and low-cost approaches, the goalof developing a better means of FEQS is possible. Hence, in this paper, the research focus is placed on counting FEQS.  
Counting FEQS is applicable for civil engineering projects, like hydropower, airport, large-scale landscape transformation, etc., as they have huge earthmoving demands and do not require precise truck load weighing. For these projects, their sites are generally located in open fields and can be equipped with surveillance camera systems for construction management and safety. Also, for these projects, earthmoving trucks normally are uniform and have just two states of full or empty when in working order, because project investment is adequate, contracting relationships are simple, and the truck loading processes are well-organized to guarantee the earthmoving quality of each time (a partially loaded truck is considered as a fault when working). Moreover, unlike transportation on city roads that strictly forbids dust spreading, in the open field, buckets of trucks do not need to be covered as no residents will be disturbed by dust due to earthmoving. Under this earthmoving operation scenario, through the surveillance camera systems, the truck loading conditions of full or empty can be directly viewed without occlusions, as uncovered trucks have an obvious characteristic difference between full/empty-load conditions, as Figure 1 shows. Thus, by judging the truck loading condition, i.e., the binary classification problem of full/empty-load trucks, the core work of the FEQS framework can be achieved. Also, as machine vision [20] can replace human eyes for truck loading condition judgment with little cost from the collection of video information and is free of human labor, a fully-automated, non-contact full/empty-load classification of earthmoving trucks can be implemented. Hence, a novel vision-based FEQS of truck counting can be proposed, and proper vison-based truck image classification approaches are needed.
## 4 Experimental Verification
### Pressure Relief Hole Image Generation Test  
#### 4.2.1 Training Environment and Process  
The hardware environment was as follows: Windows Professional Edition, Inter (R) Core (TM) i7-7500 CPU @ 3.5 GHz, NVIDIA GeForce GTX 1080, Xuzhou, China. The software environment was as follows: Python 3.6, Python 1.1.0, CUDA 10.0, Xuzhou, China, etc. The structures of the generator and the discriminator in the SinGAN model were basically the same; both contained convolution layers and filters. The number of convolution layers was 3, the total number of filters was 192, and the size was 3 \(\times\) 3. The number of iterations in each stage of the training process was calculated by Equation (2). The initial learning rate was set to 5/10,000, and the number of iterations was set to 1600 as the boundary. After 1600 times, the learning rate was reduced to 1/10 of the original. If less than 1600 times, the learning rate remained unchanged. This model used the ADAM optimizer to optimize the network. The ReLU activation function parameter was set to 5%. In order to improve the training efficiency of the SinGAN model and to capture the detail feature of the pressure relief hole, we consulted the relevant literature and carried out some comparative analyses. Then, the number of training stages was set to 4 and the learning rate adjustment scale was set to 0.1. In addition, 3, 5, 7,... can be generally set for the number of convolution layer layers, and 32, 64, 128,... can be set for the number of filters in each convolution layer. The number of filters and the number of convolution layers were  
Figure 8: Drilling experiment platform.  
Figure 9: Pressure relief hole images under different conditions: (**a**) different brightnesses; (**b**) different shooting angles; (**c**) different blurring degrees; (**d**) different shooting distances.  
larger, so the image effect generated was generally better. However, the training efficiency and the memory of the computer's graphics card need to be considered to prevent training failure of SinGAN. Thus, the number of filters in each convolution layer was set to 64, the number of convolution layers was set to 3, and the filter side length was set to 3. Finally, the maximum side length of the generated image was able to reach 533.
# Quantifying Climate Change and Ecological Responses within the Yangtze River Basin, China
## 1 Introduction
At the same time, in the arid and semi-arid regions of northern and north-west China, the interannual variation of NPP is mainly affected by the change of precipitation [23]. There are obvious spatial differences in the effects of climate change and human activities on vegetation dynamics [17]. To better understand climate change and its effects on ecological indicators, it is necessary to carry out research in different climatic zones. The Yangtze River Basin (YRB) is an important climatic and economic zone in China, and mainly belongs to the subtropical monsoon region. Much research has been conducted on climate change across the YRB and its impact on ecological indicators. For example, in the semi-arid Tibetan Plateau region, increased NDVI is positively correlated with air temperature; however, an increase in temperature will ultimately inhibit vegetation growth due to reduced precipitation [24]. [PERSON] et al. [25] found that NDVI was mostly related to air temperature and not to precipitation in the upper reaches of the YRB. [PERSON] et al. [26] found that during the period of 2000-2009, the climate in the YRB exhibited a warming and drying trend, while GPP and NPP increased significantly, especially in the middle and lower reaches. Several researchers have also studied the impact of human activities on changes in ecological factors in the YRB [24; 27]. However, given the ecological and economic importance of the region, arguably further research is needed on climate change across the YRB and its impact on multiple ecological factors. Consequently, the main aims of the present study were: (1) to represent the spatiotemporal trends of climate change in the YRB from 1961-2013 using observed temperature and precipitation data and meteorological data derived from multiple model simulations (CMIP5); (2) to explore the corresponding variations in ecological indicators (NDVI, EVI, LAI, GPP, NPP); and (3) to analyze the relationships between climate change and ecological indicators. The results potentially provide an improved understanding of climate change and its impact on ecological indicators which may facilitate the protection of the ecological environment of the YRB, and aid in the formulation of policies for regional sustainable development in China and elsewhere.
## 3 Computational Modeling
### Simulation Results
#### 3.3.1 Blast Wave and Projectile Flight Characteristics  
After the detonation of the explosive charge, only part of the blast wave enters the tube. Multiple reflections occur till the formation of a planar blast wave. The planarity evolution is evaluated through a section of tracers at 400 mm, 800 mm, and 1200 mm from the point of detonation. To enable comparison with the experimental results measured by the pressure transducer, additional tracers are placed at 1180 mm. The results reveal that, for example, for the case of 30 g of C4 (simulation ID 12 in Table 5), a planar blast wave front begins to form at time 0.529 ms as shown in Figure 16. A graph of the projectile's velocity from the moment of detonation until its arrival to the tube's exit is presented in Figure 17. It is observed from Figure 17 that the projectile's flight path can be divided into four phases. The first phase starts at the moment of detonation initiation until the entire impulse transfer from the explosion to the projectile has completed. During this phase, the projectile accelerates to finally achieve a maximum velocity. In a second phase, a pressure decrease in the expansion gases surrounding the projectile causes the latter to cease accelerating. As of this moment, the projectile travels at constant speed. In a third phase, the reflected blast waves from the EDST walls interact with the projectile leading to a drop in the projectile's velocity. At 0.2 ms, the fourth phase is initiated and the projectile continues its flight accompanied with consecutive drops in velocity caused by the multiple interactions with the reflected blast waves. At time 2.01 ms, the projectile exits the tube at quasi constant velocity as shown in phase 4 of Figure 17. The same phases are discerned in all other simulations with differences in the projectiles velocities at the tube's exit.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Simulation** & \multicolumn{3}{c}{**Simulation Inputs**} & \multicolumn{3}{c}{**At 1180 nm from the Center of Detonation**} & \multicolumn{3}{c}{**At 1200 nm from the Center of Detonation**} \\
**ID** & \multicolumn{3}{c}{**Simulation Inputs**} & \multicolumn{3}{c}{**At 1180 nm from the Center of Detonation**} & \multicolumn{3}{c}{**At 1200 nm from the Center of Detonation**} \\ \cline{2-13}  & **Change Many (a)** & **Change** & **Dynamics** & **E** & **relative** (ms)** & **Facial Pressure (MF)** & **Facial Pressure (MF)** & **Facial Pressure (MF)** &  
Figure 16: Propagation of a blast wave in the EDST (30 g C4 and 7 mm diameter projectile) (NB: the planarity of the blast wave front is checked via placing a section of tracers at 400 mm, 800 mm, 1180 mm, and 1200 mm from the detonation point).