esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The safety and reliability of bridges gradually decrease over time under the influence of disadvantage environmental factors, primarily due to reinforcement corrosion caused by chloride ingress. The traditional lateral load distribution (LLD) theory does not consider the influence of corrosion, which degrades the accuracy of bridge performance and reliability calculation. A time-dependent reliability assessment method for simply supported T-beam bridges is proposed in this paper, which considers the influence of reinforcement corrosion on LLD. Firstly, the steel corrosion process and degree are predicted based on the chloride ingress model, into which the water/cement ratio and concrete strength are innovatively introduced in order to improve the prediction accuracy. Secondly, the effective stiffness calculation method for corroded reinforcement bridges is established with the moment of inertia and section crack condition employed. Thirdly, the modified eccentric compression method is improved by the effective stiffness and iterative algorithm, which is suitable for the LLD calculation of corroded reinforcement bridges. The time-dependent vehicle load effect can be computed combined with the probability distribution of live load. Finally, the time-dependent reliability of the flexural bearing capacity is obtained by the Monte Carlo method and Bayesian theory without prior information. A simply supported bridge with five T-beams is taken as an example for analysis. It is indicated that the results calculated by the traditional reliability method are conservative, which cannot make a true and accurate evaluation. The method proposed in this paper can effectively reduce the assessment error caused by model uncertainty while considering the interaction between reinforcement corrosion and vehicle live load effect.  
reliability; simply supported T-beam bridge; chloride ingress; reinforcement corrosion; load lateral distribution; effective stiffness +
Footnote †: journal: Applied sciences
###### Abstract
It is still difficult for the drivers to sense the immediate danger because human beings tend to believe what they can see. In [12; 13], they proposed a collision avoidance scheme based on an occupancy grid which is determined by combining light detection and ranging (LiDAR) data. In [14], the authors also fused the features extracted from sparse point clouds. Expensive sensors were used to make up the missing parts. In [15], vehicle trajectory at intersections were estimated based on each vehicle's velocity through V2V communication. A system for cooperative collision avoidance for overtaking scenarios was proposed in [16]. The authors of [9] designed a real-time multisource data fusion scheme through cooperative V2V communications. Multiple confidences were fused based on the Dempster-Shafer theory of evidence (DS). There exist many studies on collision prediction or avoidance, but few works have been conducted on visual augmentation in a cooperative way. Work [17] proposes a transparent vehicle method based on V2V video streams in order to deal with passing maneuvers. Their method needs accurate distance information gathered from radar sensors to realize the object projection between two images. The work [18] uses linear constraints to enable rear vehicle drivers to see through the front vehicle. However, this method only make sense when the vehicles are both in the same lane. The authors of [19] introduced a method which can \"see through\" the forward vehicle by adopting affine transformation to fuse images from adjacent vehicles, no matter if they are in same lane or not. However, deviation in the occluded object's location and size always exists. The deviation might cause incorrect judgement by drivers or an autonomous system. Following this line, we propose the cooperative visual augmentation algorithm based on V2V technology. Expensive sensors, such as LiDAR, are not needed here. An ordinary camera, for example a driving recorder, can meet our requirement and there is no limitation on the location of the leading and host vehicles. The main contributions of this paper are as follows:  
1. A new collaborative visual augmentation method to eliminate blind spots is proposed. Our method can extend the visual perception ability of the driver or autonomous driving system to the obstacle area by fusing images from forward vehicles. 2. We also propose a deep-affine transformation to realize the visual fusing. Depth information and geometric constrains are introduced to optimize the affine matrix parameters. 3. We improve the results of the visual augmented method by projecting occluded objects onto host vehicle images. KITTI data are used as the evaluation dataset.
## 4 Conclusions  
This work first showed that the _Spirulina_ extract from the UE enhanced anti-inflammation activities in mouse microglial cells associated with its antioxidant effects, compared with that from the conventional 70% ethanol extract at high temperature. The non-thermal ultrasonic extract contained more than two-fold higher amounts of extremely heat-sensitive chlorophylls than the conventional extract and exhibited a two-fold higher extraction yield. Specifically, the extract effectively inhibited the production of inflammation mediators, such as NO and PGE2, while the conventional extract also showed a relatively high ability to inhibit their production. Strong anti-inflammatory effects of the extract from the UE were also demonstrated by the significant down-regulation of mRNA expression of the pro-inflammatory cytokines, TNF-\(\alpha\), IL-6 and IL-1\(\beta\). Moreover, we quantitatively demonstrated that inhibition of the gene expression was closely related to the suppression of pro-inflammatory cytokine secretion even though ELISA analysis supports PCR results in general; in particular, TNF-\(\alpha\) had a very strong relationship while IL-6 demonstrated the least correlation between the down-regulation of gene expression and the suppression of cytokine secretion. Notably, the effect on TNF-\(\alpha\) expression was greater than that on IL-1\(\beta\) and IL-6, which changed the least. This result suggests that the extract first controls TNF-\(\alpha\) secretion and later inhibits IL-6, but this hypothesis should be further validated with a more detailed mechanism of the anti-inflammation cascade within nerve cells. However, we clearly showed that the extract from the ultrasonic process was more effective at suppressing the secretion of both inflammatory mediators and pro-inflammatory cytokines, as reflected by its neuroprotective activities. Conclusively, the high anti-inflammatory effects of the extracts were closely correlated with the high amounts of chlorophylls, which have strong antioxidant activities, obtained only through a non-thermal ultrasonic process. The high anti-inflammatory effects of the _Spirulina_ extract with mouse microglia cell line could be employed to develop a possible mechanism for in vivo anti-inflammation effects along with the results from primary nerve cells. However, the expression of anti-inflammatory cytokines, such as IL-10 and TGF-\(\beta\), should also be studied to further elucidate more exact anti-inflammation effects of this extract. This study also provides useful information for developing functional foods from heat-labile natural resources.  
[PERSON], [PERSON], [PERSON] and [PERSON] carried out all of the experiments and [PERSON] designed the whole experiments and drafted the manuscript. All authors read and approved the final manuscript.  
This work has been supported by a research grant from the Marine Biotechnology Program funded by the Ministry of Oceans and Fisheries (PM61400), and all of the authors deeply appreciate their financial support.  
The authors declare no conflict of interest.
## 4 Conclusions  
In this research, the effect of the immersion ratio on operation parameters of a pouch-type battery is experimentally investigated using air and distilled water as working fluids. The surface temperature variations of the battery were determined for different discharge  
Figure 8: Maximum temperature differences at the end of different discharge rates for (**a**) T\({}_{\text{inlet}}\) = 25 \({}^{\circ}\)C and (**b**) T\({}_{\text{inlet}}\) = 15 \({}^{\circ}\)C.  
rates, immersion ratios and coolant fluid inlet temperature for distilled water. The results are compared with air natural convection results to reveal the impact of immersion cooling. The main findings of this research can be stated as follows:  
* The battery surface temperatures increase with an increment of discharge rates for both air and distilled water cooling conditions because of the exothermic chemical reaction occurring in the battery.
* The highest average battery surface temperature is observed for air cooling experiments due to the low specific heat capacity of air. This situation is valid for all discharge rates, and the highest increment average battery surface temperature is obtained at 10.9 \({}^{\circ}\)C for air at the 4C discharge rate compared to 100% water immersion.
* 100% immersion cooling has a significant impact on battery surface temperature since it can reduce average battery temperatures by up to 28% for tested working conditions.
* The lowest battery average surface temperature is recorded with the application of 100% distilled water immersion because of direct contact with fluid, and it increases by approximately 5 \({}^{\circ}\)C for different discharge rates in the range between 1-4C.
* The 50% immersion ratio has no significant effect on minimum battery temperature compared to 100%one for all discharge rates.
* The coolant inlet temperature has no significant impact on battery surface temperature except 100% immersion.
* The maximum temperature differences can be significantly reduced with the application of immersion cooling.
* The experimental results revealed that immersion cooling could be a good solution for battery thermal management systems, and their performance can be improved by using dielectric fluid having higher specific heat capacity and thermal conductivity.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 1 Introduction
(3) Experimental results demonstrate that the proposed two-stage optimization (DSO) algorithm outperforms traditional approaches, including the single-stage optimization algorithm (SSO), equal allocation algorithm (EAA), and dynamic resource pricing algorithm (DRP). Specifically, the DSO algorithm achieves the following advantages:  
Improved resource utilization: the DSO algorithm increases computing resource utilization by approximately 7-10% compared to SSO and EAA, ensuring more efficient use of available resources. Reduced task processing latency: by optimizing resource allocation and leveraging UAV mobility, the DSO algorithm reduces task processing delays by 3-5% compared to DRP and SSO, meeting the low-latency requirements of time-sensitive applications. Lower energy consumption: the DSO algorithm minimizes energy consumption by 4-8% compared to traditional methods, extending UAV operational endurance and reducing operational costs. The rest of this paper is organized as follows. Section 2 first discusses related studies, followed by the system model and blockchain framework in Section 3. Section 4 is the simulation design and experimental analysis, and the Conclusion follows.
# An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving
## 4 Experimental Analysis  
### Experimental Settings  
During the training phase, each batch of data comprises one randomly selected image from the source domain and one from the target domain, which are used as inputs for the model. The source domain data include corresponding labels, whereas the target domain data do not. The baseline model FasterRCNN adopts ResNet-50 as the feature extraction layer and adds FPN as the neck layer before RPN. The proposed model is implemented based on Pytorch and MMDetection and trained and tested on an Ubuntu system with a single RTX 3090 GPU. The initial learning rate is set to 0.001, and the weight adjustment parameter is \(\alpha_{1}=0.5,\alpha_{2}=0.5,\alpha_{3}=1,\eta_{1}=1,\eta_{2}=0.1,\eta_{3}=0.1\), updated using the stochastic gradient descent method with a momentum of 0.9 and a weight decay of 0.0001.  
To evaluate the effectiveness of the proposed method, validation was performed on the Cityscapes foggy, Cityscapes rainy, KITTI rainy, and real-world test datasets. The Cityscapes dataset consists of 5000 traffic scene images in urban environments, including additional foggy scenes. The proposed model was trained with normal weather conditions and tested on foggy scenes to verify the effectiveness of domain adaptation. As the Cityscapes dataset lacks rainy weather scenes, a rainy noise generation algorithm was employed to generate the Cityscapes rainy dataset. The KITTI dataset contains 7481 traffic scene images, with 3712 images used for model training and 3769 images for model validation. KITTI is a commonly used dataset in autonomous driving research, but it only provides data under normal weather conditions. Therefore, similar to the Cityscapes rainy dataset, the KITTI rainy dataset was generated using a rainy noise generation algorithm. The established dataset of Cityscapes rainy and KITTI rainy are shown in the Figure 5.  
Figure 5: The established datasets of Cityscapes rainy and KITTI rainy: (**a**) KITTI rainy. (**b**) Cityscapes rainy.
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
## 2 Sparse Coding Algorithm Based on Optimized LWD and FSS
### Solving Sparse Coefficient Based on FSS  
According to Equation (2), the objective function may not be a convex optimization problem about the atomic dictionary \(D\) and the coefficient S at the same time, so it cannot be solved at the same time. When dictionary \(D\) is fixed, the question of Equation (2) is equivalent to solving the l\({}_{1}\) norm regularized least squares problem. This paper uses the FSS algorithm to solve this problem. If we know the sign (positive, negative, or zero) of each sparse coefficient \(s_{i}\) when obtaining the optimal solution, then the l\({}_{1}\) norm in \(\left\|s\right\|_{1}\) can be ignored. If we only retain the non-zero coefficients, the above process can be predigested to a standard unconstrained secondary optimization problem to achieve an efficient solution. The coefficients should then be updated according to the signs of the sparse coefficients following the solution and assumption. The basic flow is as follows:  
1. Initialize the collection. Input signal \(y\), redundant dictionary \(D\), initialization sparse coefficient \(s=\overset{\rightarrow}{0}\) and its corresponding sign \(\theta=\overset{\rightarrow}{0}\), \(\theta_{i}\in\{-1,0,1\}\).
2. Search for \(i=\text{argmax}_{i}\big{|}\partial\|y-Ds\|^{2}/\partial s_{i}\big{|}\) in the coefficient \(s_{i}\) whose value is zero, and incorporate satisfying the following conditions into the active set: 1. If \(\partial\|y-Ds\|^{2}/\partial s_{i}>\beta\), then \(\theta_{i}=-1\); 2. If \(\partial\|y-Ds\|^{2}/\partial s_{i}<-\beta\), then \(\theta_{i}=1\); 3. Feature sign steps Select the column vectors of the active set in \(D\) to form a vector \(\hat{D}\), and select the sub vectors of the active set in \(s\) and \(\theta\) to form vectors \(\hat{s}\) and \(\hat{\theta}\). And calculate the unconstrained optimization equation \(\min\big{\|}y-\hat{D}\hat{s}\big{\|}^{2}+\beta\hat{\theta}^{T}\hat{s}\) to get its analytical solution \(\hat{s}_{new}\): \[\hat{s}_{new}=\left(\hat{D}^{T}\hat{D}\right)^{-1}(\hat{D}^{T}y-\frac{\beta \hat{\theta}}{2})\] (3)  
A discrete linear search is performed in the \(\hat{s}\) to \(\hat{s}_{new}\) interval to find all coefficient vectors whose signs change. At the same time, the objective function value is compared with the objective function value of \(\hat{s}_{new}\) and \(\hat{s}\) is replaced by the coefficient vector which makes the objective function take the minimum value. In the active set, the coefficients of zero elements in \(\hat{s}\) are deleted and the sign set \(\theta=sign(s)\) is updated.  
4. View the best conditions1. If \(\forall s_{i}\
eq 0\), all \(\partial\|y-Ds\|^{2}/\partial s_{i}+\beta sign(s_{i})=0\) holds, then check condition b, otherwise, return to step 3;
2. If \(\forall s_{i}=0\), all \(\left|\partial\|y-Ds\|^{2}/\partial s_{i}\right|\leq\beta\) hold, then output coefficient s, otherwise return to step 2.
## 3 Results and Discussion
### Energy Consumption in Accordance with RTQ-R
For the 2020 scenario, the reduction provided by the strategy, when compared to the annual cooling energy demand of the building without its implementation, is significant, varying from 32% to 35% depending on the orientation. The same occurs for the 2050 scenario, with a variation between 33% and 36%. The highest reduction in thermal load was observed when the thermal mass strategy was positioned facing west, with a 36% average reduction. Notice that a partially-permed earth-sheltered wall has the potential increased building resilience to face the global warming impacts, since cooling energy consumption in the 2020s is on average 12.3% lower than the base scenario, while in 2050, it is surpassed only by 10.7%. For the last time-slice, resilience is almost achieved, providing a considerable reduction in energy consumption (on average 37 kWh/m\({}^{2}\)/year, based on energy consumption for LIHe in the 2050s) and promoting sustainability in tropical climate regions. Previously isolated measures tested in similar low-income houses located in the southeastern and northeast regions of Brazil were also effective in reducing the building energy consumption of HVAC [12]. The three best performing solutions were found when the wall absorptance in buildings was reduced to 0.3 (base scenario reduction: 21.86% 12050 Scenario reduction: 20.98%) when the brick wall was substituted by insulated concrete (40.82% 121.74%), and when the clay roof was replaced by a metal roof with 0.07 m of insulation and solar absorptance 0.3 (34.07% 123.64%). Note that the thermal mass strategy provided by the Bermed earth-sheltered walls, when compared to the previous strategies, is more effective at counterbalancing the climate change effects, in turn, reducing the energy consumption of the building. Thus, as an isolated passive adaptation measure, as well as the other strategies tested in the previous study, it is not capable of completely counterbalancing the impact of climate change on cooling energy consumption. However, when combined with other adaptation measures, as proposed in this research, it may be an alternative to improve the building envelope and counterattack the effect of global warming, as observed in this study and previous ones [9; 12; 13]. For cities under hot weather conditions, the idealization of climate-responsive design on residential buildingsin the design phase is an alternative to reducing energy consumption demand due to the increase of air conditioning systems used for cooling.
## 2 Calculation Model and Method
### Calculation Method  
#### 2.2.1 [PERSON]'s Theory  
According to [PERSON]'s theory [18], the constitutive equation of homogeneous saturated media can be expressed as follows:  
\[\sigma_{ij}=\lambda e\delta_{ij}+2\mu\epsilon_{ij}-\delta_{ij}\alpha P\ \ \ (i,j=x,y) \tag{1}\]  
\[P=-\alpha Mu_{i,i}-Mw_{i,i} \tag{2}\]  
\[\mu u_{i,ij}+(\lambda+\alpha^{2}M+\mu)u_{i,j,i+}\alpha Mw_{j,i}=\rho\ddot{u}_{ i}+\rho_{f}\ddot{u}_{i} \tag{3}\]  
\[\alpha Mu_{j,i}+Mw_{j,i}=\rho_{f}\ddot{u}_{i}+m\ddot{w}_{i}+b\dot{w}_{i} \tag{4}\]  
where \(\sigma_{ij}\) is the total stress tensor of soil, \(P\) is pore water pressure. \(\epsilon_{ij}\) is the average strain tensor of soil, \(e\) is the volumetric strain of the soil skeleton. \(\mu\), \(\lambda\) is the lame constant of the soil skeleton, \(\delta_{ij}\) is the Kronecker function. \(u_{i}\) and \(w_{i}\) represent the displacement vector of the soil skeleton and the displacement of water with respect to the soil skeleton, respectively. \(\alpha\) and \(M\) are parameters to characterize the compressibility of soil particles and pore fluids. \(\rho\) is the total density of the soil, \(\rho_{f}\) is the mass density of the fluid, m is a parameter, which is the same as the physical meaning of the mass.  
\(b=\rho/g/k\) is the dissipation coefficient, which reflects the soil viscous coupling degree. \(g\) is the gravity acceleration, \(k\) is the permeability coefficient. The unit of the permeability coefficient is the same as the unit of flow velocity. When the permeability coefficient \(\mathrm{k}=\infty\), that is \(b=0\), the fluid can flow freely in the pores. The internal friction is not taken into account. When the permeability coefficient \(\mathrm{k}=0\), that is \(\mathrm{b}\rightarrow\infty\), the viscous coupling effect is significant, and the soil is almost impermeable.
## 2 Models and Problem Formulation
### Models  
#### 2.1.1 Antenna Model  
The radiation pattern of a direction antenna is often depicted as the gain values in each direction in space. We can project the radiation pattern of an antenna to an azimuthal or elevation plane. The projection of the pattern typically has a main _lobe_ (beam) of the peak gain and side-lobes and back-lobes of smaller gains.  
Since modeling a real antenna with precise values for main and side-/back-lobes is difficult, we use an approximate antenna pattern in [15]. In an azimuthal plane, the main lobe of antenna can be depicted as a sector with angle \(\theta_{m}\), which is denoted as the beamwidth of the antenna. The side-lobes and back-lobes are aggregated to a circle, as shown in Figure 1. The narrower the main beamwidth of the antenna is, the smaller the side-lobes and back-lobes are. Let us take the above antenna model as an example: the gain of the main beam is more than 100 times of the gain of side-lobes when the main beamwidth is less than \(40\,^{\circ}\)[15]. Thus, we temporarily ignore the effects of the side-lobe and back-lobes of an antenna in the follow sections. Specifically, we will extend our analysis with the side-lobes and back-lobes in Section 6.  
Our simplified model assumes that the directional antenna gain is within the main beam. The gain outside the main beam is assumed to be zero. At any time, the antenna beam can only be pointed to a certain direction, as shown in Figure 1, in which the antenna is pointing to the right.