esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bioactivity of _Chlorella vulgaris_ Compounds
### Antidiabetic Effects  
_C. vulgaris_ has been shown to enhance glycemic control, especially in individuals with type 2 diabetes. Its bioactive compounds, including polysaccharides and carotenoids, play a key role in regulating blood glucose levels and improving insulin sensitivity [93]. Studies indicate that supplementation with _C. vulgaris_ can lead to reductions in fasting blood glucose and HbA1c levels, highlighting its potential as an effective adjunct in diabetes management [94; 95; 96; 97; 98]. These compounds are believed to contribute to the hypoglycemic effects of _C. vulgaris_ through various mechanisms.  
One of the primary mechanisms involves the modulation of insulin-signaling pathways. Research indicates that _C. vulgaris_ can enhance insulin sensitivity and prevent insulin resistance, particularly in the context of high-fat diets [94]. This modulation is likely facilitated by the activation of key signaling molecules in the insulin pathway, which promotes glucose uptake and utilization in peripheral tissues [99]. Moreover, the antioxidant properties of _C. vulgaris_ play a crucial role in its ability to regulate blood glucose levels. The presence of polyphenic compounds in _C. vulgaris_ has been shown to reduce oxidative stress, a condition often associated with insulin resistance and type 2 diabetes [100]. By mitigating oxidative stress, _C. vulgaris_ may help preserve pancreatic \(\beta\)-cell function, thereby improving insulin secretion and action [101]. Additionally, the antioxidant enzymes, such as SOD and CAT, are significantly upregulated in response to _C. vulgaris_ supplementation, further enhancing the body's defense against oxidative damage [102]. Furthermore, the polysaccharides found in _C. vulgaris_ may also contribute to glycemic control by influencing the gut microbiota and enhancing the absorption of glucose. These polysaccharides can act as prebiotics, promoting the growth of beneficial gut bacteria that are associated with improved metabolic health [103]. This interaction between _C. vulgaris_ and gut microbiota may lead to enhanced glucose metabolism and reduced postprandial blood glucose levels. In summary, the bioactive compounds in _C. vulgaris_, including polysaccharides and carotenoids, regulate blood glucose levels through multiple mechanisms: enhancing insulin sensitivity, reducing oxidative stress, and potentially modulating gut microbiota. These findings underscore the potential of _C. vulgaris_ as a functional food in the management of type 2 diabetes.
## 2 Theoretical Model
\tag{5}\]  
where \(n(I)=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}R(x-x\prime,y-y\prime) \cdot I(x\prime,y\prime)dx\prime y\) depends on the 2D nonlocal response function \(R(x,y)=\frac{1}{2\pi\sqrt{d}}\exp(-\sqrt{\frac{x^{2}+y^{2}}{d^{2}}})\) and the intensity \(I(x,y)=\big{|}U(x,y)\big{|}^{2}\). Thus, the variation of \(n\) is \(\Delta n=\int\int_{-\infty}^{+\infty}R(x-x\prime,y-y\prime)[q(x\prime,y\prime )G(x\prime,y\prime)+q^{*}(x\prime,y\prime)F(x\prime,y\prime)]dx\prime y\). By calculating the eigenvalue \(\lambda\), the linear stability analysis is as follows. If the real part of \(\lambda\) is zero, the soliton is linearly stable; otherwise, it is linearly unstable. The value of the real part of \(\lambda\) indicates the degree of linear instability, and thus \(\lambda\) is named the perturbation growth rate. To intuitively show the influences of different degrees of nonlocalities, we present three profiles of the nonlocal responses with \(d=0.05\), \(d=0.5\), and \(d=5\) corresponding to the weak, intermediate, and strong nonlocalities, respectively. As shown in Figure 1, the response of nonlinearity is nearly local when \(d=0.05\), and the nonlinearity affects the incident point and the area close to the point. When \(d=5\), the nonlinearity affects a wide area around the incident point, which is strongly nonlocal.
### Purpose, Contribution, and Paper Structure  
This research work aims to implement an unconventional method of feature extraction and feature selection on a combined Cleveland and Statlog heart datasets to improve the result of coronary heart disease risk prediction. As an initial stage, the principal components of the combined heart dataset were determined, and then, the best principal components were obtained by applying the Correlation-based Feature Selection (CFS) method with the Best First search procedure. Thenceforth, the single and ensemble machine learning algorithms were trained on full and selected principal components. This entails the hyperparameter optimization analysis to attain the best hyperparameters that impact the performance of the classifiers significantly in view of improving the prediction outcomes. The key contributions of this research work are summarized as follows:  
* An accurate coronary heart risk prediction system has been developed based on selected principal components and machine learning algorithms.
* The impact of selecting significant principal components using the CFS technique to improve the prediction results for heart disease diagnosis by reducing the training time has been investigated.
* The performance of the single classifiers such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and ensemble classifiers including AdaBoost M1 (AB), Bagging (BG), and Rotation Forest (RTF) has been examined with and without hyperparameter optimization.
* A performance comparison of state-of-the-art works on coronary heart disease risk prediction with the proposed method has been provided.  
The rest of the paper is organized as follows: Section 2 details the related works, and Section 3 focuses on materials and methods. Results and analysis are detailed in Section 4;a discussion is provided in Section 5. Finally, the conclusion and future work is given in Section 6.
## 3 Results  
### Connection with the Striatum  
The tractography reliably identifies the classical pathway from the SNc to the striatum [35; 57; 58; 59] (see Figure 2). In a considerable number of studies, nigrostriatal fibers have been repeatedly visualized and exhibited different diffusion properties in both healthy subjects and patients with PD [11; 14; 60; 61].  
In the multicenter Parkinson's Progression Markers Initiative study, 50 patients with PD and 27 controls exhibited systematic abnormalities in the diffusion parameters of the nigrostriatal tract, including reduced fractional anisotropy (FA) and changes in radial/axial diffusivity, indicative of loss of fiber integrity [62]. Furthermore, these changes were found to be associated with the severity of motor symptoms and a decline in striatal dopaminergic marker uptake on nuclear medicine methods, thereby substantiating the functional significance of the pathway damage. Tractography has been demonstrated to quantify the integrity of nigrostriatal connections [62] with the caveat that this is closely related to motor deficits. In a study with 21 PD patients, [PERSON] et al. (2015) utilized deterministic DTI tractography to reveal a substantial decrease in FA and streamline count within the nigrostriatal pathway when compared to a control group [11]. Magnetic resonance imaging  
Figure 2: Nigrostriatal connectivity visualized using diffusion MRI. The substantia nigra pars compacta (SNc) are shown in cyan, the dopaminergic projection pathway in red, and the striatum in green.  
alterations are indicative of dopaminergic fiber degeneration. Furthermore, an association was observed between streamline count and motor impairment scores, thereby substantiating the clinical significance of connectivity metrics. Current 7T MRI techniques facilitate more detailed imaging of SNs and their projections. However, the increased resolution of these techniques poses a significant challenge in terms of standardization. A study by [PERSON] and [PERSON] (2022) revealed discrepancies in detected fiber counts between 3T and 7T systems, attributable to variations in acquisition parameters [63].
## 4 Conclusions
The results showed that the average ablation volume after three-antenna ablation was 3 times that of the two-antenna ablation, and the three-antenna ablation had higher sphericity, it was more suitable for treating liver tumors larger than 3 cm in diameter. In conclusion, under the conditions of reasonable ablation power and ablation time, compared with single-antenna ablation, multi-antenna ablation can produce a larger ablation area, and the ablation area have higher sphericity, so it is more suitable for treating large tumor. However, there is currently less experience using multi-antenna ablation in clinical treatment, and further scientific research is still needed. There are certain differences between in vivo and in vitro. Lung is a porous tissue consisting of millions of tiny alveoli and air sacs, and air inhalation and exhalation occur all the time. To obtain more accurate simulation results, it is necessary to consider the distribution of alveoli and capillaries and other factors, this will be a daunting challenge in finite element simulation experiments. The simulation model in this paper simply treats lung tissue as a homogeneous material, which has certain limitations. These questions need to be further investigated through complementary experiments and more theoretical studies. In this study, microwave ablation in vitro experiments and simulation experiments were established to study the effect of lung tissue inflated on the ablation area, although there are some differences between the in vitro experiments and simulation results, the effect of inflated lung on the ablation area is still similar, which is expected to provide reference for future clinical medicine and simulation experiments. H.H. conceptualization, investigation, performed the experiments, data analysis, writing--original manuscript preparation, review and editing; Q.N. supervision, conceptualization, investigation, project administration, funding acquisition, visualization, validation, writing--review and editing; Z.T. conceptualization, review of the manuscript; X.G. conceptualization, review of the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (31771021, 11832003), Beijing Municipal Natural Science Foundation (3162006) and Jiangsu Science and Technology Department (Jiangsu Science and Technology Committee) (BE2017758). Not applicable. The authors declare no conflict of interest.
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 5 Heuristic Algorithms
### List Scheduling Algorithms  
List scheduling algorithms (LSAs) perform well in solving parallel machine scheduling problems with regard to machine utilization criteria [3; 32]. The main idea of LSAs is to assign each job in the list of waiting jobs to the machine with the minimum workload [16]. In practice, there are different strategies for selecting jobs from the waiting job list. We denote the list scheduling algorithm based on the SPT (shortest processing time first), LPT (longest processing time first) and RPT (random processing time) rules by \(LSA_{SPT}\), \(LSA_{LPT}\) and \(LSA_{RPT}\), respectively. For convenience, we show the pseudo-code of LSAs in Algorithm 2.  
```
0:\(m,n,\{p_{1},p_{2},\ldots,p_{n}\}\)
1:Sequence the jobs in the order of LPT, SPT or random.
2:for\(j=1:n\)do
3: Assign job \(j\in\mathcal{J}\) to the machine with the currently minimum workload
4:for\(i=1:m\)do
5: Calculate the current workload of machine \(i\), that is, the sum of the processing time of the jobs currently assigned to machine \(i\)
6:endfor
7:endfor
8:return The objective value and the corresponding schedule
```  
**Algorithm 2** List scheduling algorithms (LSAs)
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.2 Realistic UAV Mobility Model  
According to our survey, all routing protocols except PARRoT [29] consider generic mobility models, such as RWP and Gaussian Markov mobility models. However, according to the discussion in Section 2.2, the mobility models in FANETs should be application dependent and should adopt the behavior of swarm intelligence to achieve realistic results in the simulation environment. Mobility control algorithms, such as bold flocking [9], virtual force [10; 11; 17], virtual spring [18; 19], and APF [12; 16; 46] produce a realistic mobility model for a UAV swarm in a software simulation environment considering the type of mission. Thus, designing and evaluating routing protocols that consider a realistic mobility model can be an interesting research concept.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 1 Introduction
The major research contributions can be summarized as follows. * We develop a novel analytical model to investigate the link connectivity and the coverage probability of SUs in UCANs. We find that the link connectivity and coverage of SUs depends on both the spectrum availability and the topological connectivity, while the spectrum availability has been ignored in most existing works. * We conduct extensive simulations to verify the accuracy of our proposed model. The simulation results match the analytical results, implying that our proposed model is fairly accurate. * We observe that the probability of connectivity and the probability of coverage are affected by acoustic signal frequency, various ambient factors (spreading factor and wind speed), and the activity of PUs. Our results also offer some useful insights in designing QoS-aware UCANs. The rest of this paper is organized as follows. We summarize related works in Section 2. Section 3 presents the system model of UCANs. We analyze the link connectivity and the probability of coverage of SUs in UCANs in Section 4. Section 5 gives the simulation results, considering various factors. Finally, we conclude the paper in Section 6.
# Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
## 3 Experimental Results and Analysis
### Homogeneous Double Defect Inversion Imaging  
The homogeneous double-defect model means that the parameters of the two defects in the xylem are identical, and the coaxial means that the circle centers of the two defects are at the same coordinate coaxial. Figure 5 shows the inversion results of the homogeneous coaxial double-defect model for each algorithm.  
Figure 4: Concentric single defect model inversion imaging. (**a**) Concentric defect model with a radius of 2 cm. (**b**) CSI inversion results. (**c**) BP neural network inversion results. (**d**) Model-driven deep learning network inversion results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Contrast Source** & **bp Neural Network** &
\begin{tabular}{c} **Model-Driven Deep** \\ **Learning Networks** \\ \end{tabular} \\ \hline Mean Square Error & 0.2826 & 0.1732 & 0.0825 \\ Single Detection Time & 116s & 0.059s & 0.063s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean square error and average single detection time for each algorithm.  
As shown in Figure 5, the boundary transition phenomenon in the inverse map of the contrast source is serious, which makes it impossible to identify the media boundary between the two defects in the inverse map, and the _IOU_ value is only 0.872, reducing the inverse accuracy of the defect size and location. The BP neural network can reflect the defect size and location better, and the _IOU_ value of the inverse map is 0.963. However, the media boundary between the wood and air in the inverse map is not clear enough. The model-driven deep learning inversion not only has less noise but also clearly inverts the defect size and location, as well as the media boundary between wood and air, with an _IOU_ value of 0.975.  
Table 4 shows the average single detection time and mean square error for each algorithm. When the number of defects increases to two, the CSI is unable to invert the defect and xylem media boundaries, where the relative permittivity in the inverse map is between 5 and 40. This mean square error is 0.3526 for the CSI, while the model-driven deep learning network inversion is the most accurate with a mean square error of 0.0937. In terms of time cost, the detection time required by the model-driven deep learning network is 0.066 s; the average detection time of the CSI increases by 3 s compared to the single defect detection time.  
Figure 5: Homogeneous coaxial double defect model inversion imaging. (**a**) Homogeneous double defect model with a radius of 2 cm. (**b**) CSI inversion results. (**c**) BP neural network inversion results. (**d**) Model-driven deep learning network inversion results.
## 3 Results and Discussion
The modified Arrhenius equation provides excellent fits for the integrated PL intensity data corresponding to the \(0^{\circ}\) and \(15^{\circ}\) samples and for data obtained at temperatures above 60 K for the \(2^{\circ}\) sample, as shown by the red lines in Figure 11. The parameters used to obtain the optimal fits are listed in Table 3.  
Two different nonradiative recombination processes have been typically described. The first process involves thermally activated recombination in traps that are related to the incorporation of impurities [48; 49], whereas the second involves the thermal emission of the carriers out of the located states with an activation energy related to the depth of the located states potential, followed by effective nonradiative recombination [38]. The fitting results revealed that all investigated samples (\(0^{\circ},2^{\circ}\), and \(15^{\circ}\)) possessed nearly similar values of \(E_{A}\) and \(C_{1}\). This suggests that the types of impurities in the three samples were similar because of them being grown under identical growth conditions. However, the \(E_{B}\) of the \(2^{\circ}\) sample was significantly higher than those of the \(0^{\circ}\) and \(15^{\circ}\) samples, because of the deep localized potential formed by the In-rich cluster during its epitaxial growth. The \(E_{B}\) of the \(15^{\circ}\) sample was lower than those of the other samples, because the quantum effect of quasi-nanocrystals caused by the severe relaxation of stress during the growth of the \(15^{\circ}\) sample and the diffusion of In atoms from the well to the barrier in nanodots reduce the potential barrier [50]. The high \(C_{2}\) values of the \(2^{\circ}\) and  
Figure 10: Relationship between photon energy and FWHM for the \(2^{\circ}\) sample. The red solid line represents the curve fit to the presented data. \(15^{\circ}\) samples indicate the high defect density that was caused by stress relaxation during the growth process.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 4 The Proposed Solutions  
In this section, details of the proposed CRT Map and the CBR are presented. We first describe how each vehicle collects real-time traffic information of nearby roads with low overheads. Vehicles then use inter-vehicle communication to exchange and share their traffic information. Finally, how vehicles utilize the CRT Map for the delivery of data packets are described.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 3 Bioceramic Root Repair Material
### EndoSequence Root Repair Material  
EndoSequence root repair material (Brasseler USA, Savannah, GA, USA) is a pre-mixed putty or syringeable paste mainly composed of calcium silicate, calcium phosphate, zirconium oxide and tantalum oxide [10]. As an alternative to MTA, EndoSequence root repair material has been considered bioactive, which is the ability of a material to form an apatite-like precipitate on its surface when brought into contact with tissue fluids [51].  
[PERSON] et al. [52] showed that MTA and EndoSequence root repair material putty had similar sealing ability. Both materials had detectable viable bacteria in the root canals in a bacterial (_E. faecalis_) nutrient leakage model after a 30-day incubation period [53]. EndoSequence root repair material demonstrates its antibacterial activity during its setting reaction due to its highly alkaline pH and mineral ion release [5]. EndoSequence root repair material putty (Figure 3b) was used as a retro-fill material in a clinical study using photodynamic therapy to disinfect the cutting surface and root-end cavity in apicoectomy surgeries. The 12-21-month success rate was reported to be up to 93% [49]. [PERSON] et al. showed there was no significant difference in terms of antimicrobial activity against _E. faecalis_ between iRoot BP Plus and MTA [54].  
EndoSequence root repair material has been available in two different consistencies, including a fast-set putty and a regular-set paste. A direct contact test showed that EndoSequence root repair material putty and paste possessed antimicrobial properties against 10 clinical strains of _E. faecalis_ during their setting time [55]. Moreover, EndoSequence root repair material and MTA had comparable antifungal activity against _C. albicans_ biofilm [56].
## 5 Conclusions  
In this article, we design a comprehensive simulation platform combining UWA channel modelling with OFDM channel estimation. The simulation platform is presented in a GUI and interfaced from various databases, allowing the user to model realistic UWA channels in most areas of the ocean and estimate channels with configurable inputs. Three simulations are conducted based on the simulation platform. Realistic UWA channels in the East China Sea are modelled to study the influence of receiving depth, sea bottom boundary, and sea surface boundary on OFDM channel  
Figure 14: (**a**) BER performance of channels in different sea surfaces boundaries. (**b**) MSE performance of channels in different sea surfaces boundaries.  
Figure 15: (**a**) Distribution of BER curves with difference SWH. There are 20 curves for each SWH value. (**b**) Mean BER performance of channels in different SWH.  
estimation. The simulations present that different environmental factors have specific effects on rays tracing, which result in the change of time delay and amplitudes, causing the specific effect on the performance of channel estimation. The results show that: (1) The UWA channel with 10 m receiving depth has more concentrated normalized CIR and better channel estimation performance than the other channels with deeper receiving depth. When SNR is 30 dB, the gap of performance reaches around 20 dB in LS algorithm and 30 dB in LMMSE algorithm. (2) The UWA channels with complicated sea bottom boundaries yield poor channel estimation. The BER performance of the channels in LS algorithm is around 0.0075, which is similar to the worse BER value in the first simulation. A worst-case performance bound exists in LS algorithm in which the UWA channels can hardly get worse performance of channel estimation. (3) The sea surface modelled in Gauss-Lagrange waves only affects the interfering eigenrays and has a stochastic effect on the performance of channel estimation. With the increase of SWH, the average performance gradually gets better because of the worst-case performance bound and increasing range of stochastic effect. Though the effect is stochastic, the rough surface channels get worse average performance than the flat one. When SNR is 30 dB, the 2 m SWH channels get 7.95 dB better mean BER performance than the 0.5 m SWH channels and 8.06 dB worse mean BER performance than the flat surface channel in the LS algorithm.  
Conceptualization, [PERSON] ([PERSON]) and R.J.; Formal analysis, [PERSON] ([PERSON]) and Q.C.; Methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Resources, [PERSON] and [PERSON]; Software, [PERSON] ([PERSON]); Writing--original draft, [PERSON] ([PERSON]); Writing--review & editing, [PERSON] and [PERSON]. ([PERSON]).  
This research received no external funding.  
The authors declare no conflict of interest.