esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Processing  
The MRI images were registered using the statistical parametric mapping software SPM12 (Wellcome Centre for Human Neuroimaging, UCL, London, UK) using default values. Correct registration was verified with the open-source medical image viewer ITK-SNAP ([[http://www.itksnap.org/pmwiki/pmwiki.php](http://www.itksnap.org/pmwiki/pmwiki.php)]([http://www.itksnap.org/pmwiki/pmwiki.php](http://www.itksnap.org/pmwiki/pmwiki.php)), accessed on 13 October 2021). Post-processing was performed with MATLAB (Mathworks, Natick, MA, USA). Three regions of interest (ROI) were outlined manually (H.B.) for calculation of oxygenation and perfusion parameters: CET and central necrosis on all slices of the acquired images where the respective region was present, as well as NET2 (Figure 2). The propensity of the central necrotic region to hemorrhage and its very low to non-existent perfusion can lead to an unphysiological spike in OEF (cf. Figure 3) [5]. Therefore, the necrotic region was subtracted from CET to exclude non-vital parts of the tumor in metabolic assessment. For the peritumoral edematous area, a 15-20 mm wide ROI was defined in NET2 on three consecutive FLAIR images with the largest peritumoral hyperintensity, leaving an approximately 3 mm wide margin to avoid partial volume effects [31]. All ROIs were adapted to the tumor border, subtracting a cerebrospinal fluid mask of ventricles and sulci generated automatically in SPM12. The ROIs were audited by an experienced neuroradiologist (H.W.).  
Figure 2: Examples of the different regions of interest (ROIs) assessed in this study. (**a**) Axial T1-weighted contrast-enhanced image of a 71-year-old male patient with glioblastoma in the frontal lobe of the left hemisphere. The outer ROI marks contrast-enhancing tumor while the ROI inside the tumor indicates the central necrosis. (**b**) Axial FLAIR image of a 66-year-old male patient with a left hemispheric metastasis in the perirolandic region from unknown primary. The ROI marks the proximal edema bordering the solid-appearing tumor.
# Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect
## 1 Introduction  
The study of the spreading process on social networks has attracted a lot of attention recently for its great practical value in word-of-mouth marketing or viral marketing [1; 2]. [PERSON] et al. [3] and [PERSON] et al. [4] first treated the viral marketing problem as an influence maximization problem, which chooses a limited number of initial nodes to spread information of a product in a social network such that the resulting number of customers that bought the product is maximized. They discarded some important properties of viral marketing in order to simplify the problem. Recently, some properties, such as time, budget and memory networks, have been carefully studied [5; 6; 7; 8; 9]. However, none of that research considered the memory and social reinforcement effects, which are two common and significant characteristics of social networks.  
Memory effect means that previous contacts in a social network could affect the information spread in real time [10]. Social reinforcement effect means that if more than one neighbor approves the information and transfers it to you, there is a high probability that you will approve it [11]. For example, in viral marketing, if a person receives a product advertisement recommended by his or her neighborstwice, his/her approval probability for the product should be more than twice as large as the probability with a single recommendation. Once he/she has bought a certain kind of product, the possibility of buying the same product again will be greatly increased.  
Online human interactions take place within a dynamic hierarchy, where social influence is determined by qualities such as status, eloquence, trustworthiness, authority and persuasiveness. In this paper, we extend the traditional influence maximization problem to a more realistic viral marketing problem considering memory effect and social reinforcement effect. We first propose a new propagation model, Dependent Cascade Model for Viral Marketing (DCM4 VM), to capture the dynamics of memory effect and social reinforcement effect. Then, we present new algorithms to solve the problem under DCM4 VM and prove their effectiveness through theoretical and experimental analyses. Finally, we try to explain why the actual effect of current celebrity micro-blogging marketing is not as good as expected through experimental simulation.  
The rest of this paper is organized as follows. We introduce related work in Section 2. In Section 3, we propose a novel Dependent Cascade Model for Viral Marketing and redefine the influence maximization problem under the model. Section 4 gives two modified algorithms and a new algorithm to solve the influence maximization problem. In Section 5, we discuss the corresponding experiments. We make some conclusions and describe our future work in Section 6.
## 4 Analytical Method for the Approximation of Flight Time and Initial Costates
However, extensive numerical simulations indicate that the method proposed in this work still gives an accurate approximation of the initial costates when \(n\geq 2\). In fact, as underlined by [PERSON] et al. [8], when \(n<2\), the intensity of the propulsive acceleration becomes comparable to the gravitational acceleration of the primary body, and the shape of the transfer trajectory (i.e., the characteristics of the spacecraft osculating orbit) is substantially influenced by the thrust vector magnitude and direction. The value of \(n\) can be estimated using an analytical expression obtained through the combination of Equations (6), (25), and (28). Indeed, assuming \(\bar{a}_{p}\,\sin\alpha\simeq\bar{a}_{m}\,\text{sign}\Big{(}\bar{r}_{f}-1 \Big{)}\) during the transfer, with the aid of Equation (28), the second and the last of Equation (6) give  
\[\frac{\text{d}\theta}{\text{d}\bar{t}}=\frac{\tilde{h}}{\bar{a}_{m}\,\text{ sign}\Big{(}\bar{r}_{f}-1\Big{)}\,\bar{r}^{3}}\equiv\frac{1}{\tilde{a}_{m}\,\text{ sign}\Big{(}\bar{r}_{f}-1\Big{)}\,\tilde{h}^{5}} \tag{36}\]  
which can be easily solved to obtain the final value of the spacecraft polar angle \(\theta(\bar{t}_{f})\) as a function of both the (dimensionless) maximum propulsive acceleration magnitude \(\tilde{a}_{m}\) and the target orbit radius \(\bar{r}_{f}\), viz. \[\theta(\bar{t}_{f})=\frac{1}{4\,\tilde{a}_{m}\,\text{sign}\Big{(}\bar{r}_{f}-1 \Big{)}}\left(1-\frac{1}{\bar{r}_{f}^{2}}\right) \tag{37}\]  
Accordingly, the value of \(n\) is obtained from the previous equation as  
\[n=\text{floor}\Bigg{[}\frac{\theta(\bar{t}_{f})}{2\pi}\Bigg{]}=\text{floor} \Bigg{[}\frac{1}{8\,\pi\,\tilde{a}_{m}\,\text{sign}\Big{(}\bar{r}_{f}-1\Big{)} }\left(1-\frac{1}{\bar{r}_{f}^{2}}\right)\Bigg{]} \tag{38}\]  
where \(\text{floor}[\Box]\) is the floor function [20]. Therefore, the procedure can be summarized as follows. For a given (dimensionless) value of the target orbit radius \(\bar{r}_{f}\) and the maximum propulsive acceleration magnitude \(\tilde{a}_{m}\), the estimated number of complete revolutions during the transfer \(n\) is calculated through Equation (38). If the obtained value is \(n\geq 2\), the proposed analytical method is valid, and the expression of the triplet \(\{\bar{I}_{f},\delta,\lambda_{r}(\bar{I}_{0})\}\) given by Equations (26), (29), and (33) can be used to obtain a reasonable approximation of the actual values that resolve the associated TPBVP. Otherwise, the actual value of the unknown initial costates can be very different from those determined analytically. On the other hand, Equations (26), (29), and (33) can still be used to rapidly obtain a (very rough) guess to initialize the TPBVP numerical solution through standard methods [20].
## 3 Results and Discussion
### Dominant Phyla of Soil Bacteria Under Horse Beans Cultivated in Different Systems
[44] showed that the high concentration of nutrients stimulated the numbers of _Gemmatimonadota_ and suggested that the bacteria could play a key role in soil ecosystems. In our study, there was an increased relative OTU number of _Gemmatimonadota_ due to the applied cultivation systems and the crop, i.e., horse bean (Figure 2). These results were in line with the properties of _Gemmatimonadota_ described in the reference publications. Legumes have been widely studied due to their significant ecological benefits. They enrich the soil with organic matter by leaving a large mass of crop residues (straw and roots), i.e., 3-6 t ha\({}^{-1}\). They are capable of biological nitrogen fixation by establishing symbiosis with bacteria from the _Rhizobiaceae_ family. This is particularly important on light soils, because it increases the sorption complex [45] and creates a better environment for the biodiversity of the soil microbiome. The analysis of differences (Figure 2) showed that _Actinobacteria_ in the soils cultivated in different systems (except CT) was statistically significant (\(p<0.05\)), greater than in sample \"0\". In CT, _Actinobacteria_ was statistically (\(p<0.05\)) smaller than in FA (Figure 2). _Acidobacteria_ in the RT was statistically (\(p<0.05\)) greater than in sample \"0\". According to [PERSON] et al. [46], the occurrence of the _Acidobacteria_ phylum is correlated with the physicochemical properties of soils. It is characteristic of soils on which rice is grown as well as orchard soils, with limited oxygen access due to soil compaction and generally deteriorating conditions for bacteria. [PERSON] et al. [47] conducted a fifteen-year experiment, in which the no-tillage cultivation system was mostly used. The researchers observed an increased number of the _Acidobacteria_ phylum and concluded that conservation tillage increased the biodiversity of the microbiome. Our observations of changes in the numbers of bacterial communities belonging to specific phyla in the soil samples collected from different cultivation systems under the horse bean plantation were in line with the findings of the aforementioned studies. Statistically significant differences were also observedfor _Firmicutes_, _Gemmatimonadetes_, _Plantomycetes_, and _Proteobacteria_ between CT and FA; for _Chloroflexi_, _Firmicutes_, _Gemmatimonadetes_, _Plantomycetes_, and _Proteobacteria_ between RT and FA; for _Proteobacteria_ between ST and FA; as well as for _Chloroflexi_, _Firmicutes_, _Gemmatimonadetes_, _Plantomycetes_, and _Proteobacteria_ between NT and FA (Figure 2). The first two principal coordinates explained 96.45% of the variance in the cultivation systems based on the percentages of operational taxonomic units' dominant phyla of bacteria (Figure 3A). The cultivation systems formed three groups. One of them was FA and CT, another was RT and NT, and the other was ST, which was clearly different from the others (Figure 3A).
# In Vitro Analysis of Organic Ester Functional Groups in Carious Dentine
## 3 Results  
Two hundred and forty-six examined points from 10 sections of carious dentine were analysed using Raman spectroscopy and characterised into infected, affected and sound dentine zones. The results showed a significant difference in the ester functional group intensities between the three histological dentine zones (Table 1, Figure 1). The descriptive analysis showed that the mean intensity of ester functional groups in the caries-infected dentine was almost three times higher than that in the caries-affected dentine (mean intensities: 3.3, 0.8 and 0.04 in infected, affected and sound, respectively).  
Lucifer yellow (LY) was observed on the whole tooth section surface after the initial 24 h immersion in LY solution, with more noticeable discolouration found within the carious dentine lesion (Figure 2). However, after immersion in the salt and alkaline solutions, the LY staining was observed only in the carious dentine lesion (covalently bound).  
\begin{table}
\begin{tabular}{l l c} \hline \hline  & Mean & & 3.39 * \\ \cline{2-3} Infected Dentine ** & 95\% Confidence Interval for Mean & Lower & 2.95 \\ \cline{2-3}  & & Upper & 3.84 \\ \cline{2-3}  & Std. Deviation & & 2.11 \\ \hline \hline  & Mean & & 0.87 \\ \cline{2-3} Affected Dentine ** & 95\% Confidence Interval for Mean & Lower & 0.76 \\ \cline{2-3}  & & Upper & 0.98 \\ \cline{2-3}  & Std. Deviation & & 0.58 \\ \hline \hline  & Mean & & 0.04 \\ \cline{2-3} Sound Dentine ** & 95\% Confidence Interval for Mean & Lower & 0.02 \\ \cline{2-3}  & & Upper & 0.06 \\ \cline{2-3}  & Std. Deviation & & 0.07 \\ \hline \hline \end{tabular}  
* Peak ratio (ester/phosphate). ** These zones were characterised by using the amide I/phosphate ratios infected > 1.76, affected <1.76 [19]. The sound dentine readings were taken from pure clinically dentine sound areas.  
\end{table}
Table 1: A descriptive analysis was used to measure the differences in ester functional group intensities at 1740 cm\({}^{-1}\) between infected, affected and sound zones (characterised according to [19]), which shows a significant difference between the three zones as the confidence intervals do not overlap between the zones.  
Figure 1: One of the sections showing three Raman spectra: infected, affected and sound dentine after normalising the intensities to the phosphate peak, showing differences in the ester functional group peak ratio (1740/960 cm\({}^{-1}\)) between the three zones (indicated by the arrows).
## 2 Materials and Methods
### Antifungal Effacies of EOs
#### 2.4.2 Antifungal (_In Vitro_) Properties of EOs  
_In vitro_ antifungal efficacy of analyzed EOs was evaluated by the agar disc diffusion technique, as previously described by [PERSON] et al. [45]. In brief, an aliquot of culture media (100 \(\upmu\)L) was inoculated on the SDA surface followed by the application of discs of filter paper (6 mm) impregnated with 10 \(\upmu\)L of the EO samples (each in four concentrations: 62.5, 125, 250, and 500 \(\upmu\)L/L, diluted in ethyl acetate). Consequently, the microscopic filamentous fungi were aerobically incubated at 25 \(\pm\) 1 \({}^{\circ}\)C for a period of 5 days. After the process of incubation, the inhibition zone diameters (mm) were measured. The following ascending scheme was used to assess the inhibitory potential of the EOs: weak anti-pencillium efficacy (5-10 mm) \(<\) moderate anti-pencillium efficacy (10-15 mm) \(<\) very strong anti-pencillium efficacy (zone \(>\) 15 mm).
# Closed Form Solutions for Thermal Buckling of Functionally Graded Rectangular Thin Plates
## 3 Closed Form Solutions of Thermal Buckling
### Case 3 Temperature-Dependent Homogeneous Plates for a Uniform Temperature Field  
Referring to Equations (14) and (17), in this case \(\hat{G}_{0}\) can be written as  
\[\hat{G}_{0}=\frac{12\alpha_{0}\Big{(}1+\
u_{0}\text{T}_{i}\Big{)}\text{T}_{ \Delta}T}{h^{2}\Big{[}1-\alpha_{0}\Big{(}1+\
u_{0}\text{T}_{i}\Big{)}\text{T}_ {\Delta}T\Big{]}} \tag{40}\]  
where  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Integral**} & \multirow{2}{*}{
\begin{tabular}{c} **Uniform** \\ **Temperature Field** \\ \end{tabular} } & **Linear** & **Nonlinear** \\  & & **Temperature Field** & **Temperature Field** \\ \hline \(J_{0}\) & \(\int_{-h/2}^{h/2}\frac{\alpha E}{1-v}\text{d}z\) & \(\int_{-h/2}^{h/2}\frac{\alpha E}{1-v}\bigg{(}\frac{1}{2}+\frac{z}{h}\bigg{)} \text{d}z\) & \(\int_{-h/2}^{h/2}\frac{\alpha E}{1-v}f(z)  
\[\begin{split}\Upsilon_{1}=&\quad\frac{\
u_{-1}}{T_{0}+ \Delta T}+1+\
u_{1}(T_{0}+\Delta T)+\
u_{2}(T_{0}+\Delta T)^{2}+\
u_{3}(T_{0}+ \Delta T)^{3}\\ \Upsilon_{2}=&\quad\frac{\alpha_{-1}}{T_{0}+\Delta T }+1+\alpha_{1}(T_{0}+\Delta T)+\alpha_{2}(T_{0}+\Delta T)^{2}+\alpha_{3}(T_{0} +\Delta T)^{3}\end{split} \tag{41}\]  
It can be proved that \(\Delta T_{\rm cr}\) is one of the roots of the following ninth-order algebraic equation  
\[A_{9}x^{9}+A_{8}x^{8}+A_{7}x^{7}+A_{6}x^{6}+A_{5}x^{5}+A_{4}x^{4}+A_{3}x^{3}+A_ {2}x^{2}+A_{1}x+A_{0}=0 \tag{42}\]  
where the coefficients are given in Appendix A. Equation (42) can be solved by the numerical methods to obtain the critical buckling temperature for a uniform temperature field. In general, the critical buckling temperature should be the smallest positive solution of the nine-order algebraic equation. It can be seen that the initial temperature \(T_{0}\) affects the critical buckling temperature of temperature-dependent homogeneous plates under a uniform temperature field. The results provided later demonstrate the effect of \(T_{0}\) for temperature-dependent FGM plates under all kinds of thermal environments.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 2 Review of Fake News Detection Approaches and Multimodal Learning Methods
### User Comment Analysis for Credibility Assessment  
User comments provide valuable information for news credibility assessment through mechanisms including collective intelligence, crowdsourced fact-checking, and early warning signals [51; 52]. [PERSON] et al. [30] conducted pioneering research showing that user replies on Twitter contain rich credibility cues that effectively assist rumor detection. [PERSON] et al. [53] further validated the phenomenon that domain experts, fact-checkers, and informed citizens in comment sections can quickly identify and expose misinformation.  
Temporal dynamics of comments also provide important detection signals. [PERSON] et al. [31] found that comment patterns for legitimate news typically show predictable correlations with news cycles and public interest, while fake news often exhibits artificial engagement patterns or abnormal temporal distributions. [PERSON] et al. [54] confirmed the crucial role of early comments in rumor detection through Weibo data analysis.  
Semantic analysis of comments reveals additional credibility indicators. [PERSON] et al. [55] discovered that comments on legitimate news tend to focus on substantive discussions about reported events, while comments on fake news often exhibit characteristics such as emotional manipulation, topic deflection, or coordinated messaging campaigns. [PERSON] et al. [56] further analyzed the impact of stance detection and sentiment orientation in comments on credibility assessment.  
However, comment analysis faces numerous challenges requiring effective solutions: noise filtering, relevance assessment, temporal dynamics modeling, and scalability issues [4,57]. Existing research still lacks sufficient exploration of comment information potential, particularly in cross-modal fusion and real-time processing capabilities.
## 3 Results and Discussion
### Biosorption Characteristics of 1 M NaOH-Treated \(H\). fusiformis
#### 3.4.3 Effect of Solution pH  
The effect of solution pH (2-9) on the removal of heavy metals by pretreated _H. fusiformis_ was also studied (initial metal concentration: 10 mg/L; pH: 2.0-9.0; adsorbent dosage: 1.67 g/L; reaction time: 1440 min; agitation speed: 100 rpm; temperature: 25 \(\pm\) 0.5 \({}^{\circ}\)C). The results show that the uptake of heavy metals significantly increased in the pH range of 2-4 (Figure 7). The biosorption of Cd\({}^{2+}\), Cu\({}^{2+}\), and Pb\({}^{2+}\) plateaued above pH 4, and the Ni\({}^{2+}\) biosorption continued to increase until pH 9. In acidic pH conditions (pH < 4), the active surface sites of the adsorbent were protonated [63], lowering the biosorption of heavy metals at these pH values [25]. However, as the pH increased, the adsorption of heavy metals increased as the competition between H\({}^{+}\) ions and heavy metals decreased [64]. Furthermore, at pH > 6, these metal ions can be adsorbed to negatively charged surfaces of pretreated _H. fusiformis_ via electrostatic interaction because the point of zero charges of algae has been reported in the range of pH 4-6.1 [65; 66; 67; 68].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metals**} & \multicolumn{4}{c}{**Langmuir Model**} & \multicolumn{4}{c}{**Freundlich Model**} \\ \cline{2-9}  & \(q_{m}\) **(mg/g)** & \(K_{L}\) **(L/mg)** & \(R^{2}\) & **SSE** & \(K_{F}\) **(fm/g)-(L/mg)\({}^{14}\)** & **1/n** & \(R^{2}\) & **SSE** \\ \hline Cd\({}^{2*}\) & 38.390 & 0.0158 & 0.882 & 118.0 & 6.127 & 0.272 & 0.965 & 34.5 \\ \hline Cu\({}^{2*}\) & 42.250 & 0.2879 & 0.952 & 96.0 & 14.844 & 0.186 & 0.956 & 88.3 \\ \hline Ni\({}^{2*}\) & 41.872 & 0.0079 & 0.933 & 66.0 & 3.198 & 0.369 & 0.970 & 29.5 \\ \hline Pb\({}^{2*}\) & 162.201 & 0.1127 & 0.967 & 1054.9 & 42.060 & 0.222 & 0.950 & 1382.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Langmuir and Freundlich model parameter of Cd\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\) sorption by 1 M NaOH-treated _H. fusiformis_.  
Figure 7: Effect of pH on Cd\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\) sorption capacity of 1 M NaOH-treated _H. fusiformis_.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results  
### Datasets and Metrics  
#### 3.1.1 Datasets  
Our study is based on two publicly available MOT datasets, DanceTrack [32] and MOT20 [33], with experiments conducted under the \"private detection\" protocol. The DanceTrack dataset is a large-scale MOT dataset, mainly featuring group dance videos. Compared to other datasets, the objects in DanceTrack have highly similar appearances, more complex motion patterns, and frequent occlusion and overlap, posing significant challenges for motion modeling. The DanceTrack dataset contains 100 video sequences, divided into training, validation, and test sets, with 40 training sequences, 25 validation sequences, and 35 test sequences. The ablation study was conducted on the validation set of DanceTrack.  
The MOT20 datasets are provided by the MOT Challenge as benchmark datasets, primarily featuring pedestrians in various busy street scenes, and are among the most widely used public datasets for MOT tasks. The MOT20 dataset comprises eight video sequences, including four training sequences and four test sequences, with no separate validation set.