esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## 3 Results and Discussions
### Blood Flow and Platelets Migration in Aneurysms  
The geometries of the aneurysm vessels in the current study were adopted from [1,2], as shown in Figure 4. The hematocrit was specified as 42%. The inlet boundary condition was prescribed based on the steady-state results in the straight vessel, as described above. The initial conditions for the RBCs and platelets were set as 0. To evaluate the influence of blood flow rate and neck size of the aneurysm, two geometries and three flow rates were studied. Figure 4a shows the bulbous saccular aneurysm, and Figure 4b represents the bulging saccular aneurysm.  
Figure 3: The numerical and experimental results for the relative platelets concentration along the y-direction [40]. The relative concentration is defined as the local concentration divided by the bulk concentration of the platelets. The simulated hematocrits are 36% and 42% and the experimental hematocrit is 36%. The inlet velocity is 0.5 m/s (Re = 50). The platelets bulk concentration is \(3.0\times 10^{14}\) PLTs/m\({}^{3}\).  
Figure 2: Schematic of the straight vessel.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 5 Testbed Deployment and Experimentations
### Error Correction  
In erasure codes, the coding rate defined by the ratio between the number of uncoded packets and (the number of uncoded packets + the number of coded packets) is a crucial parameter as it determines the redundancy introduced in the network. Thus, we evaluate in Figure 14 the impact of the parameter \(\beta\) (number of coded packets). We base our experiments on the MED_N_ADAP scenario as it is a trivial solution for a challenging WSN scenario. \(\alpha\) is set to 10. We vary the redundancy levels (0 means no redundant packets are sent). Results are the average of 8 images of the same size.  
From Figure 14, we can notice two main results. First, the number of decoded packets is higher than the number of received packets. This highlights the added value of the LDPC codes used in ODYSSE. With image transmissions for instance, losing some jpeg packets may prevent its decoding; the image cannot be displayed. Second, we notice that, the higher \(\beta\) is, the higher the number of decoded packets is: when \(\beta\geq 30\), 100% of lost packets are recovered (see Figure 15 which depicts the percentage of recovered packets).  
Please note that despite our optimized implementation of LDPC code as discussed in Section 3.7, we were limited in the maximum value of \(\beta\). Our experience witnesses the importance of the optimized erasure codes.  
We now evaluate the cost of the erasure codes in terms of delays and duty cycle. Figure 16 shows that the end-to-end delays slightly varies with \(\beta\). This means that coded packets do not create a particular congestion phenomena in the network compared to uncoded packets. Thus, they do not contribute to high variations of the average end-to-end delays per packet.  
Figure 14: Decoding performance. The decoded packets is the total number of packets retrieved at the gateway that will be reassembled to re-constitute the image.  
Figure 17 plots the duty cycle of nodes for \(\beta=0\) and \(\beta=30\). The usage of the erasure code inevitably decreases the duty cycle as nodes have to transmit more packets. However, this increase is small and mainly visible at nodes close to the source; the overall energy consumption of the network is almost constant. Notice that in ODYSSE, the coded packets computation is done only at the source, which reduces the computational energy consumption. For comparison, our findings are coherent with those in [16] which claims that the erasure codes have notable reliability improvement at a small energy and delay cost.
# An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems
###### Abstract  
In recent years, the Internet of Things (IoT), based on low-power wide-area network (LPWAN) wireless communication technology, has developed rapidly. On the one hand, the IoT makes it possible to conduct low-cost, low-power, wide-coverage, and real-time soil monitoring in fields. On the other hand, many proximal soil sensor devices designed based on conventional communication methods that are stored in an inventory face elimination. Considering the idea of saving resources and costs, this paper applied LPWAN technology to an inventoried proximal soil sensor device, by designing an attachment hardware system (AHS) and realizing technical upgrades. The results of the experimental tests proved that the sensor device, after upgrading, could work for several years with only a battery power supply, and the effective wireless communication coverage was nearly 1 km in a typical suburban farming environment. Therefore, the new device not only retained the original mature sensing technology of the sensor device, but also exhibited ultralow power consumption and long-distance transmission, which are advantages of the LPWAN; gave full play to the application value and economic value of the devices stored in inventory; and saved resources and costs. The proposed approach also provides a reference for applying LPWAN technology to a wider range of inventoried sensor devices for technical upgrading.  
IoT; LPWAN; proximal soil sensor device; conventional communication methods; ultralow power consumption; long-distance transmission; economic value; inventoried sensor devices +
Footnote †: journal: Sensors
# Characterization of Sorghum Processed through Dry Heat Treatment and Milling
## 3 Results and Discussion  
### Functional Properties and Proximate Composition  
#### 3.1.1 Influence of Factors  
The experimental data obtained for sorghum flour fraction functional properties and chemical properties were successfully fitted to the 2 FI model (Table 3). The ANOVA results revealed that the model proposed was significant for all the responses, since the \(F\)-values were significant at \(p<0.05\), while \(R^{2}\) values were 0.58-0.97. HC was positively influenced by both factors--treatment temperature and fraction dimension--and their interaction (Table 3), but only in the case of the fraction factor was the effect significant (\(p<0.05\)). The increase in HC with temperature and fraction-dimension increase (Figure 3a) could be related to the increase of damaged starch induced by gelatinization during heat treatment [15]. As the determination method implied the application of an external force to the sample, as stated by [PERSON] et al. [30], the nanopores caused by fractionation present on the surface of the sorghum flour particles could play an essential role. A similar trend of HC was reported by these authors for wheat bran with different particle sizes. A negative effect of fractionation was observed for sorghum flour OAC (Table 3). OAC decreased with treatment temperature and fraction dimensions decreased, as can be seen in Figure 3d. [PERSON] et al. [31] also reported a decrease in OAC of sorghum flour when microwave treatment was applied, probably as a result of the changes in protein hydrophobic properties, oil-absorption capacity being affected by starch-protein-lipid linkages, sequence of polypeptides, various conformational properties of macromolecules, and quantity of apolar amino acids [32]. Furthermore, OAC has been proved to be influenced also by the ability of oil physical entrapment of the sample, surface area, size of macromolecules, charge, and hydrophobicity [33]. Sorghum flour SP was positively influenced by treatment temperature and negatively by fractionation (\(p<0.01\)), as shown in Table 3. A similar increasing trend of SP was obtained by [PERSON] et al. [34] for thermally treated maize starch, which could be explained by the modification of starch granule surface, which determines higher water absorption, SP also being influenced by amylose/amylopectin ratio, molecular weight, and starch-lipid complexes. The milling process affects starch, protein, and cell wall components of sorghum grains, leading to changes in their capacity to bind water and release soluble components [20].
## 3 Results
### Findings
#### 3.2.1 Demographics
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group/Category**} & \multicolumn{6}{c}{**Face-to-Face**} & \multicolumn{6}{c}{**Online**} \\ \cline{2-10}  & **M** & **Med** & **Std Dev** & **Min** & **Max** & **M** & **Med** & **Std Dev** & **Min** & **Max** \\ \hline Age group & 20.96 & 21 & 1.97 & 18 & 24 & 20.65 & 20 & 2.21 & 18 & 24 \\ Experience with computer-based games & 3.43 & 3 & 0.99 & 1 & 5 & 4.22 & 4 & 0.67 & 3 & 5 \\ Experience with virtual 3D virtual environments & 2.91 & 3 & 1 & 1 & 5 & 2.83 & 2 & 0.98 & 2 & 5 \\ Perceived Quality of the Virtual Environment & 3.35 & 3.45 & 0.85 & 2 & 5 & 2.95 & 3 & 0.82 & 1 & 4 \\ Adoption Perception & 2.5 & 2.67 & 0.67 & 1 & 3 & 2.43 & 3 & 0.66 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics for participant demographics and prior experience. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Group/Category**} & \multicolumn{2}{c}{**Face-to-Face**} & \multicolumn{2}{c}{**Online**} \\ \cline{2-5}  & \(n\) & **Percent** & \(n\) & **Percent** \\ \hline Gender & & & & \\ Males & 14 & 60.87 & 16 & 69.57 \\ Females & 9 & 39.13 & 7 & 30.43 \\ Age group & & & \\ 18–20 years old & 8 & 34.78 & 12 & 52.17 \\
21–23 years old & 12 & 52.17 & 8 & 34.78 \\
24 years old and above & 3 & 13.04 & 3 & 13.04 \\ Experience with computer-based games & & & & \\ No experience & 1 & 4.35 & 0 & 0 \\ Beginner & 2 & 8.7 & 0 & 0 \\ Intermediate & 9 & 39.13 & 3 & 13.04 \\ Advanced & 8 & 34.78 & 12 & 52.17 \\ Expert & 3 & 13.04 & 8 & 34.78 \\ Experience with Virtual Reality & & & & \\ No experience & 2 & 8.7 & 0 & 0 \\ Beginner & 5 & 21.74 & 12 & 52.17 \\ Intermediate & 10 & 43.48 & 4 & 17.39 \\ Advanced & 5 & 21.74 & 6 & 26.09 \\ Expert & 1 & 4.35 & 1 & 4.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics for participant demographics and prior experience. be differentiated: one focusing on familiarizing students with the specific VR tools and another enhancing the VR interaction skills [33]. Interestingly, as illustrated in Table 5, despite the online group reporting more experience with computer-based games and less experience with VR (M = 2.95, SD = 0.82), they rated the virtual environment less favorably than the face-to-face group (M = 3.35, SD = 0.85). This discrepancy could be attributed to their higher expectations for VR quality stemming from their video game experience or their relative discomfort with the unfamiliar VR technology. However, both groups reported similar perceptions of the learning effectiveness of the VR simulations (Face-to-Face: M = 2.5, SD = 0.67; Online: M = 2.43, SD = 0.66) suggesting that despite the differences in user experience, the educational impact of the VR simulations was comparable.
# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 2 Materials and Methods
### Localization  
The second stage forms network heads for generating binary masks. The region proposals from RPN are assigned to several specific regions of feature maps generated from FPN. These mapped regions are fed to the RoIAlign module [41], followed by convolutional layers and fully connected layers to predict the location and size of the predicted mask to fit the object. The RoIAlign module uses bilinear interpolation to properly align extracted features with the input image. Accurate mask segmentation is observed with the use of the RoIAlign module. This is implemented using TensorFlow's crop and resize function. The mask generator network is built with the ROIAlign module followed by a stack of four convolutional layers with \(3\times 3\) receptive field filters and stride 1 with 256 channels. Later, a transposed convolution layer with \(2\times 2\) filter and stride 2 is included, and the final pixel-level probability mask output is generated from a \(1\times 1\) convolution layer with stride 1 and sigmoid activation function. All the convolution layers except for the final layer are built with the ReLU (rectified linear unit) activation function.  
The resultant binary mask (\(28\times 28\)) corresponds to each region proposal, and the regions that have a class probability of 0.8 or higher are considered true predictions. During training, the ground truth masks for each instance are scaled down to \(28\times 28\) to compute the loss and backpropagate. During prediction, the predicted \(28\times 28\) binary masks are scaled up to the size of the corresponding region proposal bounding box. Based on the location information from the region proposals, all the successful mask predictions are stitched together to generate a final mask of the entire image. The output pixel-wise probability mask is further processed to locate the best pixel indicating the highest probability of being a polyp. If a region is identified as having the highest probability of being a polyp, then the centroid of the highly probable region is marked to the best location of the polyp from the mask.  
We have used stochastic gradient descent (SGD) to optimize the loss function on each RoI region in the training phase as defined by L = L\({}_{\text{mask}}\)[41].
# Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia
## 2 Materials and Methods
### Antigaging Activity
#### 2.8.2 ABTS Radical Scavenging Assay  
The method of [PERSON] et al. (2018) was adopted to perform ABTS radical scavenging assay. The stock solutions included a 7 mM ABTS solution (2,2' Azinobis-(3 ethylbenzothazoline 6 sulfonic acid)) and a 2.4 mM potassium persulfate solution. The working solution was prepared by mixing the two stock solutions in equal quantities and allowing them to react for 12-16 h at room temperature in the dark. The solution was then diluted by mixing 1 mL ABTS solution with the amount of methanol necessary to obtain an absorbance of 0.7 at 734 nm. Serial dilutions of studied extracts, fractions or reference compounds (trolox, 100 \(\upmu\)g/mL (400 \(\upmu\)M)) were carried out in 96-well microplates, at different concentrations, ranging between 0.244 \(\upmu\)g/mL and 250 \(\upmu\)g/mL in methanol. ABTS solution was then added to the microwells, and after 8 min of incubation the absorbance was recorded at 405 nm with a BioRad Microplate Reader Model 680 (Bio-Rad Laboratories, Inc., Hercules, CA, USA). In each assay, a negative control was prepared, in which the sample or fraction was substituted by the same amount of solvent. Percentage of antioxidant activity (% AA) was calculated as:  
\[\text{Chelating effect (\%)}=[(\text{Abs}_{\text{control}}-\text{Abs}_{\text{sample }})]/(\text{Abs}_{\text{control}})]\times 100 \tag{4}\]  
where \(\text{Abs}_{\text{control}}\) is the absorbance of ABTS radical + methanol; \(\text{Abs}_{\text{sample}}\) is the absorbance of ABTS radical + sample/standard.  
All assays were carried out in triplicate and results expressed as IC\({}_{50}\), i.e., as the concentration yielding 50% scavenging of ABTS, calculated by interpolation from the % AA vs. concentration curve.
# Spatial Distribution Pattern and Influencing Factors of Homestays in Chongqing, China
## 3 Results
### Spatial Heterogeneity of Factors Affecting the Spatial Distribution of Homestays
In terms of the areas outside the central urban area, the average altitude had a negative impact, while class A scenic spots, forest coverage, air-quality rate, urbanization rate, and the total number of tourist receptions had a positive impact. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Model Parameters**} & \multicolumn{1}{c}{**Coefficient**} & \multicolumn{1}{c}{**\(t\)-Value**} & \multicolumn{1}{c}{**\(p\)-Value**} & \multicolumn{1}{c}{**VIF**} \\ \hline \multicolumn{1}{c}{**Central Urban Area**} & & & & \\ \hline A-level scenic spot & 0.662 & 2.474 & 0.023 * & 2.959 \\ GDP & 0.512 & 1.677 & 0.031 * & 3.001 \\ Tertiary output value & 0.149 & 3.376 & 0.015* & 2.223 \\ Per capita living expenditure & 0.648 & 4.069 & 0.017 * & 1.987 \\ Metro station density & 0.818 & 4.106 & 0.009 ** & 1.108 \\ Colleges and universities & 0.203 & 5.936 & 0.004 ** & 1.107 \\ \hline \multicolumn{1}{c}{**The areas outside the central urban area**} & & & \\ \hline Average altitude & 0.325 & 3.602 & 0.016 * & 2.892 \\ Forest cover & 0.276 & 1.513 & 0.025 * & 3.584 \\ Air quality & 0.401 & 1.251 & 0.046 * & 5.468 \\ A-level scenic spot & 0.461 & 2.017 & 0.032 * & 5.179 \\ Urbanization & 0.336 & 5.193 & 0.008 ** & 4.866 \\ Total number of visitors & 0.201 & 5.891 & 0.005 ** & 2.401 \\ \hline \hline \end{tabular} Note: *\({}^{\star}\) means passing the significance level test of 5%, and **\({}^{\star\star}\)** means passing the significance level test of 1%.  
\end{table} 
Table 3: The fitting results of OLS model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Independent Variable**} & \multicolumn{1}{c}{**Minimum**} & \multicolumn{1}{c}{**Upper Quantile**} & \multicolumn{1}{c}{**Median**} & \multicolumn{1}{c}{**Lower Quantile**} & \multicolumn{1}{c}{**Maximum**} & \multicolumn{1}{c}{**Average**} \\ \hline
**Central Urban Area** & & & & & \\ \hline A-level scenic spot & \(-\)0.356 & \(-\)0.188 & 0.073 & 0.111 & 0.296 & 0.126 \\ GDP & 0.973 & 1.114 & 1.453 & 1.569 & 1.701 & 1.356 \\ Tertiary output value & 1.405 & 1.432 & 1.563 & 1.626 & 1.708 & 1.557 \\ Per capita living expenditure & 0.132 & 0.196 & 0.341 & 0.369 & 0.592 & 0.336 \\ Metro station density & 0.512 & 0.565 & 0.726 & 0.814 & 0.961 & 0.728 \\ Colleges and universities & 0.275 & 0.286 & 0.318 & 0.326 & 0.356 & 0.313 \\ \hline \multicolumn{1}{c}{**The Areas Outside the Central Urban Area**} & & & & & \\ \hline Average altitude & \(-\)0.991 & \(-\)0.971 & \(-\)0.752 & \(-\)0.266 & \(-\)0.264 & \(-\)0.662 \\ Forest cover & 0.021 & 0.066 & 0.355 & 0.745 & 0.952 & 0.428 \\ Air quality & 0.103 & 0.137 & 0.176 & 0.277 & 0.316 & 0.202 \\ A-level scenic spot & 0.351 & 0.359 & 0.401 & 0.424 & 0.432 & 0.395 \\ Urbanization & 0.335 & 0.449 & 0.639 & 0.769 & 0.846 & 0.617 \\ Total number of visitors & 0.104 & 0.128 & 0.223 & 0.797 & 0.881 & 0.371 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical results of the GWR model coefficient values.
## 4 Results and Discussion
### Experimental Results of 1D Raw Data
#### 4.2.1 Results for Machine Learning Models with 1D Raw Data  
The results of the experiment can be found in Table 4 and Figure 4. The results reveal that RF achieves the highest accuracy of 92.42%, surpassing all other ML models. It is followed by the SVM, ET, and GB models, each with an accuracy of 90.91%. The NB model performs the worst on the balanced dataset, achieving 54.55% accuracy. The RF model also excels in other evaluation metrics, including 93.08% precision, 92.42% recall, and 91.69% F1 score. As previously noted, data upsampling techniques enhanced the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frameworks** & **Pytorch, Sci-Kit Learn** \\ \hline Language & Python3.8 \\ Ram & 256 G \\ OS & Ubuntu 20.04 LTS \\ CPU & Intel Xeon Platinum 8362 @ 2.80 GHz \\ GPU & NVIDIA A40 48 G \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of the experimental setup.  
performance of NB and LR, but their results remain less impressive compared to other tree-based ensemble models.
## 4 Caries Pathogenesis  
For a thorough perception of the role of probiotics in caries prevention and therapy, an analysis of the mechanism via which caries lesions develop is imperative. The oral cavity constitutes a habitat for a wide variety of microorganisms [84]. The latter are found to colonize both the oral mucosa and stable surfaces, such as teeth, fixed and removable prosthodonic appliances, etc. It is those microorganisms that colonize tooth surfaces to which dental caries is attributed. These microorganisms conglomerate, thereby constituting a complex, tolerant antimicrobial agent mass called 'oral biofilm' or 'dental plaque'.  
Oral biofilms are made up of a plethora of microorganisms. Some of them are harmless when present in the oral cavity, and some others possess a facultative pathogenic potential, which is called 'opportunistic pathogens' [85]. According to the 'Ecological Plaque Hypothesis', in the presence of health, all of them are in a state of symbiosis with each other as much as with the host. In fact, they play a crucial role in the host's health. Caries disease results from this symbiotic relationship's subversion, where a shift toward pathogens occurs, a state characterized as dysbiosis. In that case, sugars consumed through diet are taken up by pathogenic bacteria and are metabolized to lactic acid. Acids produced by dental plaque solubilize apatite crystals of the hard dental tissues (i.e., demineralization). Once acidic residues are removed, remineralization occurs (Figure 2).  
However, caries onset is not as simple as presented above. Many factors interplay among the host (salivary flow, apatite solubility), its oral microbiome synthesis, and the type of nutrients being taken up (high/low-sugar diet) [86]. Thus, the effects of acids produced during a sugar-rich diet of low frequency can be neutralized by saliva or alkali produced in the dental plaque, meaning that demineralization and remineralization phenomena are in equilibrium. In the case that a sugar-abundant diet is consumed more frequently, the constant low-pH conditions exert evolutionary pressure toward acid-adapted and acidogenic bacteria, such as _Streptococci_ and _Actinomycetes_. If this frequency is even higher,  
Figure 2: The process of caries development.  
the most efficient acid-tolerating and acid-producing bacteria dominate. In the last two cases, demineralization of the tooth surfaces prevails at the expense of remineralization [85].  
Several microorganisms have been correlated with different types, stages, and sites of cariogenesis [84]. The complex microbial composition of cavities at various stages is not consistent with the specific plaque hypothesis and supports a polymicrobial origin. Nowadays, dental caries cannot be considered a classical infectious disease that follows the conventional [PERSON]'s model. The microbial 'players' involved change through time depending on the tissue affected, and multiple species are responsible for the progressing lesion at different stages of the caries process [87].
## 2 Modeling of the Human Arm  
It is assumed in this research that the human arm is modeled as a radially stratified cylinder of 50 mm radius. The geometry of the arm model consists of five concentric cylindrical layers, of which thicknesses are 1.5 mm (skin), 8.5 mm (fat), 27.5 mm (muscle), 6 mm (bone) and 6.5 mm (bone marrow) [19, 20, 21], as shown in Figure 1.  
Figure 1: Geometry of the problem with an internal object \(\mathcal{D}\).  
As it is well known, the electromagnetic properties of human tissues depend generally on frequency and, hence, they are characterized as dispersive media. The constitutive parameters of frequency-dispersive biological tissues are commonly evaluated by a variety of relaxation simulations including Debye and Cole-Cole models. The suitability of the Debye model for fast evaluation of wideband fields was reported in [22] by comparing with the Cole-Cole model. Taking into account the dispersive nature of the tissues in the layers of the arm model, their dielectric properties are determined in this study according to the three-pole Debye model expressed by  
\[\epsilon_{r}(\omega)=\epsilon_{\infty}+\sum\limits_{k=1}^{3}\frac{\Delta \epsilon_{k}}{1-i\,\omega\,\tau_{k}}, \tag{1}\]  
where \(\omega\) is the angular frequency, \(\epsilon_{\infty}\) is the high frequency permittivity, \(\Delta\epsilon_{k}\) is the magnitude of the dispersion and \(\tau_{k}\) is the relaxation time constant. These parameters for five tissues considered in this work are extracted for each tissue from the data set reported in [23; 24] for the frequency range from 100 MHz to 2 GHz and listed in Table 1.