esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 2 Preliminary Definitions
### Stable Extension  
Dung-style semantics define the sets of arguments that can jointly be accepted (extensions). A \(\sigma\)-extension refers to an extension under semantics \(\sigma\)[24].  
**Definition 2** (A conflict-free set).: _Let an abstract argumentation framework be \(AF=(A,R)\). Set \(E\subseteq A\) is a conflict-free set if there do not exist \(x,y\in E\), such that \(x\) attacks \(y\) or \(y\) attacks \(x\). The set of all conflict-free sets of \(AF\) is denoted by cf(\(AF\))._  
**Definition 3** (Acceptability of arguments).: _Let an abstract argumentation framework be \(AF=(A,R)\). An argument \(a\in A\) is acceptable with respect to the set of arguments \(E\subseteq A\) if \(\forall b\in A\) (\(b\
otin E\)), and if \(b\) attacks \(a\) then \(E\) attacks \(b\)._  
**Definition 4** (An admissible set).: _Let an abstract argumentation framework be \(AF=(A,R)\). Set \(E\subseteq A\), \(E\) is an admissible set if \(E\in\) cf(\(AF\)), and every argument within \(E\) is acceptable with respect to \(E\). The admissible sets are denoted by adm(\(AF\))._  
Figure 2: Example of abstract argumentation frameworks.  
**Definition 5** (A complete extension).: _Let an abstract argumentation framework be \(AF=(A,R)\). Set \(E\subseteq A\) is a complete extension if \(E\in adm(AF)\) and every acceptable argument with respect to \(E\) belongs to \(E\). The complete extensions are denoted by \(\text{com}(AF)\)._  
**Definition 6** (A stable extension).: _Let an abstract argumentation framework be \(AF=(A,R)\). Set \(E\subseteq A\) is a stable extension if \(E\in\text{cf}(AF)\) and \(\forall a\in A\), and if \(a\
otin E\), then \(E\) attacks \(a\). The stable extensions are denoted by \(\text{stb}(AF)\)._  
**Definition 7** (A grounded extension).: _Let an abstract argumentation framework be \(AF=(A,R)\). Set \(E\subseteq A\) is a grounded extension if \(E\in\text{com}(AF)\) and, for each \(T\in\text{com}(AF)\), \(T\
ot\subset E\). The grounded extension is denoted by \(\text{grd}(AF)\)._  
Figure 3 shows several instances of grounded extensions. In Figure 3a, the set \(S=\{a\}\) is first obtained by performing a characteristic function call on the empty set, which contains the initial argument \(a\). Subsequently, the characteristic function call is performed again on set \(S\), resulting in the updated set \(S=\{a,c\}\). When the characteristic function call continues on updated set \(S\), the set remains unchanged as \(S=\{a,c\}\). This indicates that a grounded extension \(\{a,c\}\), has been obtained.  
Following the same procedure, the grounded extension \(\{a\}\) is obtained in Figure 3b. In Figure 3c, the grounded extension results in the empty set. For details of the theoretical basis and proof of this procedure, as well as details of the characteristic function, please refer to the literature [4].  
There are various semantic extensions, such as the preferred extension and semi-stable extension. For further information, refer to the literature [4]. This paper specifically addresses the stable extension.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 3 Selection of the Control Strategy
### Lower Actuators  
#### 3.2.1 Judgment Logic for the Braking Target Wheel Cylinder  
When a vehicle is in danger of overturning, a transverse swing moment opposite to the direction of overturning should be loaded onto it. As shown in Figure 12, when braking, only the angular velocity of the transverse swing generated by the front outer and inner wheels of the car is opposite to the direction of the turn, and the stabilizing transverse swing moment generated by the front outer wheel is the most effective.  
In this research, the left and right front wheels were selected as the main control targets; the braking torque distribution strategy is shown in Table 5. When the vehicle is in danger of rolling over due to a left turn, only the right front wheel is braked; when it is in danger of rolling over due to a right turn, only the left front wheel is braked.  
Figure 11: Unit step response curve of the braking system.  
Figure 12: Relationship curve between wheel braking force and vehicle yaw moment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Steering** & **LTR** & **Brake Wheel** \\ \hline Turn left & \textless{}0 & Right front wheel \\ Turn right & \textgreater{}0 & Left front wheel \\ \hline \hline \end{tabular}
\end{table}
Table 5: Braking torque distribution strategy.
## 4 Discussion  
The plant _Azima tetracantha_ is widely used in traditional medicinal systems including Ayurvedic and Chinese medicines.In addition, the plant decoctions are used as dietary supplements by various tribes in India. However, few studies indicate the scientific evidence of the biological and pharmacological properties of the plant. The present study evaluates the potential antiproliferative role of _A. tetracantha_ extract against different gastric cancer cells and its possible mechanism of action.  
Among the different solvents, the methanol extract showed the highest levels of polyphenols and flavonoids. In addition, the LCMS profiling indicated the presence of different polyphenols comprising simple phenolic acids and complex dietary polyphenols. Previous results by [PERSON] et al. [24] indicate the presence of flavonoid compounds and glucosinolates in _A. tetracantha_ tissues. Notably, the extract showed significant anticancer potential against the two gastric cancer cell lines, AGS and Kato-III. To provide the mechanistic basis of action, the expression of apoptotic genes including CASP3, CASP7, APAF1 and BAX was evaluated. These genes are known to be associated with apoptotic cell death in various cell types, and the changes in their expression levels modulate the mitochondrial-mediated apoptosis in cells [25; 26]. Furthermore, the proteins Apar-1 and cytochrome c, together with Caspase 9, are involved in the formation of the apoptosome complex via the intrinsic pathway [27]. Interestingly, our data show that the _A. tetracanth_-extract-mediated increase in BAX expression levels in both Kato-III and AGS cancer cells was higher than the increase obtained for BCL2 mRNA levels, leading to an increase in the BAX/BCL2 mRNA ratio, which has already been used to explain the anticancer effects of flavanols on breast and prostate cancer cells [28]. The cytochrome c release from damaged mitochondrial membrane was also found to be increased in those cancer cells exposed to different doses of _A. tetracantha_ methanol extract; in addition, the cellular reactive oxygen species levels were also found to be upregulated in these cells. It is thus possible that the ROS-mediated mitochondrial damage performed by _A. tetracantha_ extract could explain the anti-proliferative activity of the plant.  
_A. tetracantha_ is known to possess antioxidant potentials [16] and to inhibit inflammatory signaling [29] and cancer cell proliferation [30]; hence, together with these antioxidant and anti-inflammatory properties, the anticancer property may enhance the functional value of the plant.
## 4 Discussion
### Influencing Factors of Effective Arsenic Content Change in Soil Systems  
The variation in effective arsenic content in soil is related to many factors, and it is necessary to control not only the proportion of passivating agent added during the experiment, but also the effects of soil pH, dissolved organic matter (DOM) content and crop root exudates [29]. Studies have shown that the mobility and solubility of As in soil are closely related to pH value. When the soil pH value increases, it is more conducive to the release of As, which in turn increases the content of effective As in the system, which negatively affects the growth and development of crops [30]. Compared to the acidic soils of southern China, most farmland in northern China is alkaline. Therefore, it is recommended to use a pH-neutral passivator when improving the soil of alkaline farmland [7; 8].  
In soil, DOM easily forms complexes with As to change the morphology of As, improving its mobility and bioavailability in soil [31]. [PERSON]'s [32] research results show that the application of manganese-modified biochar to soil can significantly reduce the DOM content of soil [10], which is more conducive to the fixation of As and helps modified biochar to passivate the effective arsenic in soil. In this study, with the increase in the addition ratio of nano-MnO\({}_{2}\)-modified biochar, it is obvious that the passivation effect of SY5 series experiments is better than that of SY1 series experiments. It can be seen that the application of modified biochar may cause changes in soil pH and DOM, which directly affects the mobility of soil As and thus changes the effective arsenic content in the system.  
In addition, the fixing effect of modified biochar on soil As is also related to the characteristics of the soil itself. If there are inorganic ions such as PO4\({}^{3-}\) and SO4\({}^{2-}\) or organic ions such as degradants derived from plant residues in the soil, they will compete with As for adsorption points to varying degrees, thereby inhibiting the fixed adsorption of arsenic by the passivator [33]. In this study, the passivation effect of SY1-2, SY1-3 and SY5-2 in the experimental group was reduced, which may be due to the presence of degradants of some plant residues in the relevant experimental pots, and the upper adsorption point on the surface of the biochar was reduced, which reduced the arsenic fixing effect of the nano-MnO\({}_{2}\)-modified biochar.  
In this study, the successful loading of manganese oxide to the surface of biochar played a key role in the passivation restoration experiment, which effectively improved the fixation efficiency of nano-MnO\({}_{2}\)-modified biochar on soil effective state As, and provided useful information for the application of nano-MnO\({}_{2}\)-modified biochar in soil. However, it is necessary to further explore the micromolecular mechanism of the binding of soil effective As with modified biochar and study the growth and development of crops before and after biochar addition to evaluate their stability and practicality.
## 4 Application
### Non-Clinical Application
#### 4.2.8 Driver Monitoring  
With the increase in the number of vehicles on the road, there is a potential increase in the number of accidents as well. The primary reasons for traffic accidents are physiological and psychological factors such as illness, mental illness, fatigue and the external ambient environment. Therefore, iPPG can be used to measure the physiological and psychological states of drivers. [PERSON] et al. [144] measured a driver's HR using video cameras, considering the in-vehicle environment. Another study by [PERSON] et al. [145] used a webcam to monitor HR based on ICA during various driving situations. Other studies [146; 147] have also applied the iPPG method to monitor the physiological parameters of drivers.
###### Abstract  
Over the past two decades, the Leizhou Peninsula has suffered from many geological hazards and great property losses caused by land subsidence. However, the absence of a deformation map of the whole peninsula has impeded the government in making the necessary decisions concerning hazard prevention and mitigation. This study aims to provide the evolution of land deformation (subsidence and uplift) in the whole peninsula from 1992 to 2010. A modified stacking procedure is proposed to map the surface deformation with JERS, ENVISAT, and ALOS1 images. The map shows that the land subsidence mainly occurs along the coastline with a maximum velocity of 32 mm/year and in a wide range of inland arable lands with a velocity between 10 and 19 mm/year. Our study suggests that there is a direct correlation between the subsidence and the surface geology. Besides, the observed subsidence in urban areas, caused by groundwater overexploitation for domestic and industrial use, is moving from urban areas to suburban areas. In nonurban areas, groundwater extraction for aquaculture and arable land irrigation are the main reason for land subsidence, which accelerates saltwater intrusion and coastline erosion if regular surface deformation measurements and appropriate management measures are not taken.  
The Leizhou Peninsula; land subsidence; stacking Interferometric SAR; groundwater pumping 1
To date, there has been no investigation on the impact of the high magnetic permeability and the low electrical resistivity of a steel casing on surface-to-borehole CSEM data. Thus, we incorporated the magnetic and electric properties of the steel casing into the model, in order to study its effect on the vertical electric field. Due to the advantages that composite pipes have over steel ones during geothermal operations, composite pipes will replace steel pipes in the near future [34]. To evaluate the effect of the composite casing on the CSEM response, we changed the properties of the casing from those of steel to fibreglass-reinforced epoxy. In general, this paper details a forward modelling framework suggesting the feasibility of the CSEM monitoring of low-enthalpy reservoirs with distorted time-lapse data. The remainder of this paper is structured as follows. In Section 2, we give a brief description of the geological and electrical resistivity models of the TU Delft campus sub-surface. In Section 3, we briefly mention the two CSEM forward-modelling approaches used in this study. In Section 4, we explore useful source locations for the CSEM monitoring of the TU Delft campus reservoir with preferential acquisition parameters; namely, the source-receiver setup and the source frequency. In Section 5, we assess the feasibility of using a time-lapse CSEM to track the time-lapse resistivity changes resulting from different return temperatures. In Section 6, we assess the robustness of CSEM monitoring when undesired effects corrupt the time-lapse signal. In Section 7, we discuss the merits and limitations of this feasibility study. In Section 8, we draw conclusions regarding the feasibility of CSEM monitoring the TU Delft campus low-enthalpy reservoir.
## 4 Numerical Results and Discussions  
In this section, we provide numerical results to showcase the effectiveness of the proposed approach. We first describe the simulation setup and neural network parameter selection, then demonstrate the capability of the proposed deep learning-based approaches that can produce responses similar to those produced by the optimization algorithm in changing the required SINR and the required harvested energy values. Neural network parameter selection is based on evaluation of the system performance by computing the MSE and efficiency of the computation time. In the paper, in order to calculate the parameters of the networks such as weights and biases of each layer, we use stochastic gradient descent. The stochastic gradient descent picks a randomly chosen subset of data by which neural network is trained with gradient approximation. To compute gradients, we use the back-propagation algorithm (BP). The optimal weight and bias are obtained by minimizing MSE between the actual and desired output values [44]. Besides, in order to quantitatively compare the model fitting error, the MSE is a typical metric for evaluating the accuracy of the deep learning-based model [45; 46; 47]. The MSE is a measure of prediction accuracy which is calculated between observedvalues, \(y_{i}\), taken from the optimization algorithm and the predicted values, \(\widehat{y_{i}}\), in the testing stage taken from deep learning methods, \(MSE=\frac{1}{n}\sum\limits_{i=1}^{n}{(y_{i}-\widehat{y_{i}})}^{2}\). The smaller the MSE value, the higher the predicted performance. The computation time in deep learning-based approaches includes two categories: training and testing times. Meanwhile, the computation time of the optimization scheme is attained according to the number of samples of the testing stage.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 2 Related Works
### Digital Certificate  
The Public Key Infrastructure (PKI) is a public key infrastructure based on the X.509 standard that provides a chain of trust. PKI uses public-key cryptography to authenticate users and systems [21]. It relies on digital certificates issued by trusted certificate authorities (CAs). PKI is a combination of policies, procedures, and technologies for the secure exchange of digital information online, using algorithms to protect messages and files and ensure delivery to the intended recipient. PKI authentication involves verifying the identity of users and systems by a trusted third-party certification authority. This centralized authority manages key generation, renewal, and revocation based on digital certificates. PKI employs two types of cryptographic keys: public and private keys. Together, these keys ensure the confidentiality, integrity, and authenticity of communications over the internet. Figure 3 illustrates the process of signing and verifying a message between [PERSON] and [PERSON]. [PERSON] generates a public-private key pair and sends the public key to [PERSON], who then uses it to verify the received message.  
Figure 3: Overall process of sign and verify using PKI keys ([[https://en.wikipedia.org/wiki/Digital_signature](https://en.wikipedia.org/wiki/Digital_signature)]([https://en.wikipedia.org/wiki/Digital_signature](https://en.wikipedia.org/wiki/Digital_signature))) (accessed on 1 January 2025).  
Figure 2: Structure of hash tree.
## 3 Engineering Example
### Project Parameters
#### 3.1.2 Seismic Parameters  
This seismic analysis is based on seismic waves originating from the Changjiang Nuclear Power Plant site in Hainan. Figure 8 depicts the seismic waves and frequency spectra in three dimensions. Considering the SL-2 limit for safety seismic motion, the peak accelerations are 0.3 g in the X and Y directions and 0.2 g in the Z direction. This seismic wave lasts for 30 s, with significant fluctuations occurring between 3 and 14 s. The peak times in these directions occur at 13.59 s, 11.67 s, and 6.13 s, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Static Elastic** & \begin{tabular}{c} **Dynamic** \\ **Elastic** \\ **Modulus** \\ **(E/GPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Poisson** \\ **Radius** \\ **(E\({}_{\text{d}}\)/GPa)** \\ \end{tabular} &
\begin{tabular}{c} **Dynamic** \\ **Poisson** \\ **Ratio** (\(\mu\)**d)** \\ \end{tabular} \\ \hline Concrete C40 & 32.50 & 42.25 & 0.20 & 0.20 \\ Backfill blocks & 0.26 & 0.52 & 0.33 & 0.45 \\ Backfill excavation material & 0.26 & 0.52 & 0.33 & 0.45 \\ Moderately weathered & 34.10 & 16.80 & 0.20 & 0.34 \\ biotite granite & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Immersed tunnel and soil material parameters.  
Figure 8: _Cont._  
Figure 7: Dynamic shear modulus ratio and damping ratio of soil samples.
# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 2 Materials and Methods
### Gridded Observations  
For the gridded data, we selected meteorological data, which include SW, Ps, Tair, Pre, Wind, and VPD, from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis. In addition, we calculated direct (Direct) and diffuse (Diff) shortwave radiation by combining the shortwave radiation from MERRA-2 with the satellite-based diffuse radiative fraction (DF) provided by [PERSON] et al. [41]:  
\[Direct=SW_{MERRA-2}\times(1-DF) \tag{2}\]  
\[Diff=SW_{MERRA-2}\times DF \tag{3}\]  
We also calculated site-level direct and diffuse SW using the same methods. Atmospheric carbon dioxide (CO\({}_{2}\)) concentration data were obtained from measurements at the Mauna Loa Observatory. Global annual land cover data were obtained from the MCD12C1 product of the MODIS retrieval. All the gridded meteorological data were downscaled to a spatial resolution of 0.05\({}^{\circ}\) and an 8-day temporal resolution for the period of 2001-2020 on a global scale.  
To evaluate the derived gridded GPP and ET datasets, we used the products from MODIS [42; 43], PML-V2 [5], and X-BASE [44] as the basis for comparisons. Based on the radiation-use efficiency concept, MODIS GPP (MOD17A2H) was developed combining the Leaf Area Index (LAI) and Fraction of absorbed Photosynthetically Active Radiation (FPAR) with meteorological data. MODIS ET (MOD16A2) was estimated using an improved ET algorithm [45] based on the Penman-Monteith equation, which considered soil evaporation, canopy intercepted evaporation, and plant transpiration. PML-V2 GPP and ET were estimated using the PML-V2 model, which incorporates water-carbon coupled canopy conductance and CO\({}_{2}\) fertilization and were driven by meteorological and remote sensing data. X-BASE GPP and ET were upscaled from eddy covariance flux data using a combination of several ML algorithms and meteorological forcing data [44].
## 3 Results  
### Study Selection and Inclusion Process  
After analyzing all databases (WoS: 44; PubMed: 108; SPORTDiscus: 4; ProQuest Central: 10; SCOPUS: 277) the contents of 443 articles were checked, detecting, in the initial stage, 113 duplicate articles. After duplicate removal, 330 records remained for screening. Following title and abstract screening, 250 reports were sought for retrieval, with all 250 reports successfully retrieved. Full-text assessment for eligibility was conducted on these 250 reports, resulting in the exclusion of 238 reports by exclusion criteria number one (n = 96), exclusion criteria number two (n = 138) and exclusion criteria number five (n = 4). The remaining 12 articles [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51] were included in the qualitative synthesis of the systematic review (Figure 1).  
Figure 1: PRISMA flow diagram.