esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Polymer Types on the Mechanical Properties of Polymer-Modified Cement Mortars
## 4 Test Results  
### Effect of Polymer Type on Compressive Strength  
Table 5 and Figure 2 show the compressive strength results for the PCMs, and the result of the control specimen is included in the same figure for better comparison. PCM samples ranked EVA, PVA, SBR, and AC in compressive strength from high to low. The PCM containing EVA showed the highest compressive strength at 28 days and was approximately 33% higher than that of the control specimen. Furthermore, the estimated \(p\)-value between the control and the PCM containing EVA was less than 0.05. The compressive strength of the PCM containing PVA was 48.9 MPa, and this value was quite similar to the previous result of 48.7 MPa reported by [PERSON] et al. [15]. The PCM containing SBR showed a 36% higher compressive strength than that of PCM containing AC, and this result shows good agreement with previous studies reported by [PERSON] et al. [5] and [PERSON] et al. [28]. By contrast, the 28-day compressive strength of the PCM containing AC was 14% lower compared to that of the control specimen.  
All PCM samples exhibited a rapid increase in compressive strength between seven and 28 days when compared to that of the control specimen. Specifically, the increase in strength of the PCM samples between seven and 28 days ranged from 15% to 25%, while the control specimen showed only a 5% increase in measured strength. This difference might be attributed to the effects of the incorporated  
Figure 1: Mechanical test set-ups: (**a**) compressive test; (**b**) flexural test; (**c**) pull-off bond test; (**d**) attached dollies on the surface of the polymer-modified cement mortar (PCM) overlay.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compressive Strength** & **Control** & **AC** & **PVA** & **SBR** & **EVA** \\
**(Days)** & & & **MPa** & & \\ \hline
7 & 35.15 & 27.32 & 41.61 & 34.86 & 44.03 \\
28 & 37.72 & 32.34 & 49.91 & 44.14 & 50.27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Averaged compressive strengths measured at seven and 28 days.  
polymers. It can be seen that the PCM containing SBR showed a slower strength development compared to others. The reason for the low compressive strengths from the PCM containing AC at seven and 28 days is not clear at this time. However, some researchers pointed out that the inclusion of polymer in cement mortar can reduce the strength due to a lower mechanical capacity of polymer film with regard to cement paste [2; 29; 30].
## 4 MHAR-OLSR Protocol
### CPN MHAR-OLSR Model
#### 4.2.1 Sub-Model \"Node_Identification\"  
The figure below (Figure 3) illustrates the module of the identification step, where each node of the network must identify proportionally its first neighborhood according to two main criteria: the range and the instantaneous relative speed. These characteristics are inserted in the triplet (N1, S, r) of the \"detected nodes\" place tokens.  
The identifier at each node indicates the network nature of its neighbors. It can take one of three values: 1, 2, or 3, respectively refered to as MANET, VANET, or FANET.  
Figure 3: CPN modeling of node identification.  
Figure 2: MHAR algorithm model.  
In Figure 4, the RANGE_FANET value depends on the type of MAC protocol used, when HUMAN_SPEED could take the maximum speed score ever achieved by men. In other terms, these limits would take values accommodated to the domain application needs.
###### Abstract
The simulation results of the CFSO system with the JCSCR in different turbulence channels demonstrated the improved receiver sensitivity and system noise-tolerance capability of the proposed scheme. Therefore, using the proposed technique, we can achieve lower complexity, higher noise immunity, and higher receiver sensitivity. The effectiveness and feasibility of this scheme were verified with a 10 Gbps quadrature phase-shift keying (QPSK) CFSO communication transmission system.
# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 2 Proposed Method
### New Map Points Processing Strategy
#### 2.2.1 Distance Check Operation  
While ORB-SLAM3 runs in a dynamic environment, if the dynamic objects occupy most of the field of view, the new map point will be generated at an error coordinate caused by a mismatch or wrong triangulation. They are clustered around the camera's photo center, as shown in Figure 2.  
Therefore, after generating new map points, we need to perform a distance check operation (called DC) to calculate the Euclidean distance between the map point and the camera, which is described as follows:  
\[D_{p-c}=\|P_{\text{C}}-P_{xyz}\|_{2} \tag{1}\]  
where \(D_{p-c}\) is the distance between map point and camera, \(P_{\text{C}}\) is the coordinate of the map point, and \(P_{xyz}\) is the translation of the camera at this time. Then, the map points whose distance is lower than the threshold are eliminated.
## 3 Methodology
### Model Parameterization
#### 3.1.1 Specifying Neighborhood Functions  
The _Neighborhood Definition_ tool supports GUI-based neighborhood function specification (Figure 1(a)). The most common neighborhood configurations--Moore, Von Neumann, and circular [53]--are available as preset options. Additionally, a _Custom_ neighborhood option supports manual specification of alternatives like linear [69], ring-shaped [70], and simple directional configurations [25] (Figure 1(d)). Neighborhood functions are saved as Figure 1: An overview of ArcGIS Pro 3.2.1 with (**a**) the _Geographic Automata_ add-in and its main features situated in the main tool ribbon. Subsets of GUI-based tools shown are used to support (**b**) basic binary CA and (**c**) advanced CA model-building activities.  
Figure 2: The _Neighborhood Definition_ tool with various function settings, including (**a**) a \(5\times 5\) Moore neighborhood shown in the tool interface, (**b**) a \(5\times 5\) Von Neumann neighborhood, (**c**) a \(5\times 5\) circular neighborhood, and (**d**) a linear neighborhood.
## 1 Introduction
This is due to the abundance of these metals, the role of anthropogenic factors in their release into the environment, and their known potential toxicity to ecosystems and humans. Nevertheless, many more metals come from different sources that can accumulate in soils, have toxic effects, or are suspected of having toxic effects, such as barium (Ba), gallium (Ga), lanthanum (La), scandium (Sc) or vanadium (V) [33; 34; 35]. These metals often receive less attention, or have only recently become a research topic as climate and industrial processes change and environmental awareness develops [36; 37; 38]. Therefore, this study focuses on the multi-metal distribution patterns of a large number of different metals in the soils of the Sacramento River floodplain in California, USA, to identify individual metal patterns to represent groups of metals, multi-metal distribution patterns, and the controlling features that explain the spatial variability of these patterns. This study site is especially suitable for this research since it has a very high density of existing data sets from soil samples and a wide range of measured soil metal concentrations [39; 40]. This richness of data makes this floodplain well suited for a fundamental investigation of multi-metal distribution patterns. In the first step, groupings of individual metal distribution patterns were identified. Then, a principal component analysis was conducted to reduce the complexity of the metal concentration data and identify multi-metal distribution patterns. Finally, the environmental factors best describing the spatial variance of these patterns were identified using variable importance in spatial Random Forest models. In selecting the factors, the focus was on those assumed to be stable over long periods, such as soil, vegetation, and terrain.
# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 2 Modeling the Stroke Possibility
### Classification Methodology
By incorporating resampling techniques and contemporary models, we were able to improve the accuracy of stroke prediction, which has important implications for stroke prevention and patient care. In summary, our methodology represents an effective approach for dealing with imbalanced data and improving the prediction of stroke occurrence. We believe that our findings can be applied to other public healthcare domains, where imbalanced data are a common challenge, and contribute to the development of more accurate and reliable prediction models. Our algorithm is described as follows:  
1. Downloading and importing the data;
2. Data processing and cleaning: 2.1. Data preparation for classification; 2.2. Detach randomly 40% of observations from the majority class with the command train_test_split (..., test_size = 0.4, random_state = 86); 2.3. Data resampling for the minority class using the resample command with parameters: replace = True, n_samples = constant (constant values specific for each model);
3. Define a subset using observations obtained in list items 2.2 and 2.3;
4. Splitting the data between train and test splits using the train_test_split command with test_size = 0.5;
5. Applied models within the algorithm are: 5.1. Random Forest Model: RandomForestClassifier (class_weight = 'balanced'); 5.2. Decision Tree Model: DecisionTreeClassifier (class_weight = 'balanced'); 5.3. SVms Model: SVC (C = 10, kernel = 'rbf', gamma = 'auto', class_weight = 'balanced');
6. Fitting the model on the train subset;
7. Computing the evaluating parameters: accuracy on the test subset, recall values of each class, and AUC values;
8. End of the algorithm. The algorithm is illustrated in Figure 1.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison
### Improvement of Computational Efficiency of Parallel Approach
#### 3.2.2 Improvement of Efficiency with Different Number of Threads  
The computational efficiency of the parallel parameter elimination process with a different number of threads based on OpenMP is further analyzed. According to the hardware configuration of the platform, the number of threads is sequentially increased from 2 to 16 at an interval of 2, and the computational time for the parameter elimination process of each parallel scheme is compared, which is shown in Figure 4.  
It can be seen from Figure 4 that as the number of threads increases, the computational time of parameter elimination in GNSS POD gradually decreases and the scheme with 16 threads has the shortest computational time. However, the speed of efficiency improvement gradually decreases, most likely because when the number of threads is close to or greater than the number of cores, queuing round calculations occur, and the opening and ending of threads take more time. Therefore, further increasing the number of threads beyond the hardware core is of little significance to the improvement of parallel efficiency.
# Physico-Chemical Characterization of Sesame/Rapeseed Oil Mixtures
## 3 Results
### Refractive Index  
The refractive indices of two standard oils (sesame and rapeseed oils) and their mixtures were determined by using an Abbe refracometer with an accuracy of \(\pm 1\times 10^{-4}\). The measurements were performed at a temperature of 20 \({}^{\circ}\)C. The refractive indices of all investigated oils are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Crystallization**} & \multicolumn{3}{c}{**Melting**} \\ \hline
**SO/RSO** & \(\mathbf{T_{1}}\)\({}^{\circ}\)C & \(\mathbf{H_{1}}\)/J/g & \(\mathbf{T_{2}}\)\({}^{\circ}\)C & \(\mathbf{H_{2}}\), J/g & \(\mathbf{H_{3}}\)/J/g & \(\mathbf{T_{3}}\)\({}^{\circ}\)C & \(\mathbf{T_{4}}\)\({}^{\circ}\)C & \(\mathbf{T_{2}}\)\({}^{\circ}\)C & \(\mathbf{H_{J}/g}\) \\ \hline
100/0 & \(-9.3\) & \(-5.575\) & - & - & \(-27.85\) & \(-53.2\) & \(-28.8\) & \(-15.0\) & 65.48 \\
90/10 & \(-9.7\) & \(-5.625\) & - & - & \(-29.24\) & \(-52.3\) & \(-27.9\) & \(-14.9\) & 64.06 \\
70/30 & \(-11.3\) & \(-4.101\) & - & - & \(-35.29\) & \(-50.4\) & \(-26.6\) & \(-14.9\) & 66.91 \\
50/50 & \(-11.8\) & \(-2.184\) & - & - & \(-37.02\) & \(-47.8\) & \(-26.0\) & \(-14.4\) & 63.99 \\
30/70 & \(-14.9\) & \(-1.444\) & - & - & \(-46.6\) & \(-46.1\) & - & \(-13.6\) & 69.63 \\
10/90 & \(-16.9\) & \(-1.267\) & \(-35.0\) & \(-1.140\) & \(-49.86\) & \(-44.7\) & - & \(-12.5\) & 69.38 \\
5/95 & \(-17.5\) & \(-0.652\) & \(-35.0\) & \(-2.059\) & \(-53.6\) & \(-41.8\) & - & \(-11.8\) & 71.35 \\
0/100 & \(-18.0\) & \(-0.308\) & \(-35.1\) & \(-2.412\) & \(-58.38\) & \(-40.9\) & - & \(-12.1\) & 74.21 \\ \hline R\({}^{2}\) & 0.9838 & 0.9459 & & & 0.9741 & 0.9655 & & 0.9123 & 0.7463 \\ \hline \end{tabular}
\end{table}
Table 5: Melting and crystallization phenomena of sesame oil, rapeseed oil and their mixtures.  
The results presented in Table 6 show that the refractive index value of the sesame oil is 1.4729, and for the rapeseed oil, 1.4718. The refractive index values of different investigated mixtures are not significantly different from the pure oils, and they are in the range from 1.4711 (mixture 5/95) to 1.4724 (mixture 90/10) [38]. This may be due to the specific gravity, molecular weight, increase in saturation and linear autoxidation stage. Analogous results are obtained in [39]. The refractive index is an indication of the purity of oils. According to [40], the refractive index is mainly used to measure the change in unsaturation as the fat or oil is hydrogenated. The refractive index of oils depends on their molecular weight, fatty acids chain length, degree of unsaturation and degree of conjugation. The determination of the refractive index is indicative of the likelihood of rancidity due to oxidation; it is essential for assessing oil cleanliness and detecting adulteration [41].
# Nutritional and Functional Properties of Gluten-Free Flours
## 3 Results
### Functional Properties of GF Flours  
Table 7 presents the functional properties of GF flours, which are influenced by the flour constituents and the relationships between them. Water absorption capacity reflects the amount of water that the flour can absorb and retain. The water absorption capacity was highest (\(p<0.05\)) for chickpea flour (1.92 g/g) and lowest for teff and amaranth flours with values around 0.95 g/g. For an improved food texture of bread products, higher values for water absorption are desired. Higher water absorption values were attributed to the higher content of starch and fiber [27; 69]. [PERSON] and [PERSON] [69] also stated that higher protein content tends to increased water absorption. However, in the present study, no good correlation was found between water absorption and protein content (r = 0.2099) or starch content (r = \(-\)0.4392). Of all the GF flours studied, amaranth, tiger nut, and chickpea flours exhibited the highest oil absorption capacity (1.8-1.9 g/g), being suitable for retaining the flavor and enhance the mouthfeel when used in foods. For the other GF flour, oil absorption capacity ranged between 1.66 and 1.80 g/g, with higher value for brown rice flour than rice. However, [PERSON] et al. [37] did not find any significant difference in the different GF studied regarding the oil absorption capacity. The water and oil absorption capacity depends on the type of protein, amino acid composition and protein polarity and hydrophobicity [70]. Moreover, variation in the amylose/amylopectin ratio contributes to differences in the water as well as oil absorption capacity of flour [71]. The degree of starch from the flour that absorbs water is expressed by the swelling power property. The swelling power of the GF flours varied from 3.14 to 7.32 g/g, where the lowest values were obtained for the tiger nut flour, while the highest were for rice,
## 4 Tropospheric profiling with neural networks
### Cross validation technique  
Finally, we have applied the cross validation technique, together with the early stopping, useful in the case the available data set contains few profiles for training and testing neural networks. We have decided to use this approach for the shortage of profiles belonging to the desert data sets. Cross validation technique consists in dividing the whole considered data set in K subsets by training the neural networks with the profiles belonging to K-1 subsets and validating them with the profiles of the remaining subset. This process recurs K times by changing the validation set every time. The percentage of profiles used to train the neural networks is 1-1/K and, in this way, all the profiles are used in the learning and validating phase in turn. The only drawback of this technique is the need to repeat the learning K times, increasing the computational costs. For this reason, only profiles reduced with the PCA technique were employed, so cutting down the overall time processing.  
Therefore, we have trained the neural networks with the cross validation technique applying both K=4 and K=8, that is with a validation subset corresponding to 25% and 12.5% of training data set.  
Then we have divided the training desert data set (88 samples) and the training vegetation data set (312 samples) in the training subset and in the validation subset by assigning them, for K=4, the 75% (66 and 234 samples for desert and vegetation lands respectively) and the 25% (22 and 78 samples) of the whole data sets. Similarly, for K=8, the 87.5% (77 and 273 samples for desert and vegetation lands respectively) and the 12.5% (11 and 39 samples) of data sets.
# Adipose Stem Cell Response to Borophosphate Bioactive Glass
## 4 Discussion  
Because alkaline glasses can be toxic to cells and tissues, they are often pre-reacted before use in cell culture. Even BPBGs have been pre-reacted to evaluate ion release on ASC viability [45]. This makes cell culture and potential clinical applications of BGs complicated. To understand the role of BGs in the clinic, evaluating them in direct contact with cells is essential. Furthermore, it is also necessary to understand if the effects of BGs are due to the change in pH or due to different ion release rates. For example, the CCM samples with X0 and X40 particles had similar values of pH after 5 h of incubation with ASCs, but the phosphate content of the CCM with the X40 particles was about 68% larger than the phosphate content of the CCM with the X0 particles. In addition, the X40 particles released a significant concentration (126 ppm) of borate species to the CCM. Therefore, differences in cell phenotype and function between X0 and X40 treatment are probably attributable to ion release rather than solely to the change in pH levels of the entire media. At 2.5 mg/mL, the phosphate-rich acidic X0 glass maintained cell viability over 72 h, better than the glasses with lower phosphate contents. However, at 10 mg/mL, X0 glass was considered non-viable at the same time point. This correlates with other studies showing that increasing extracellular inorganic phosphate levels promotes cell growth and proliferation, while increasing the concentration (above 16 mM) triggers cell death by activating both intrinsic and extrinsic apoptotic pathways [46]. Our results show that ASCs act as buffers, making the extracellular environment more pH-neutral when in the presence of BGs. Though little is known about the ability of ASCs to regulate extracellular pH, it is understood that cells can maintain a narrow range (7.1-7.2) of intracellular pH through the actions of membrane proton pumps and transporters, which are controlled by intra-cytoplasmic pH sensors [47]. These sensors can recognize and induce cellular responses to maintain the intracellular pH, often at the expense of acidifying the extracellular pH. In turn, extracellular acidification impacts G-protein-coupled receptors (GPCRs). There is evidence that the expression of proton-sensing GPCRs can regulate the migration and invasion of cells, which may explain why acidic X0 increased ASC migration the most. On the other hand, elevated levels of extracellular inorganic phosphate have been
## 2 Materials and Method
### Remote-Sensing-Based Carbon Flux Model
#### 2.2.5 NEP Estimation  
Net ecosystem production (NEP) is the difference between GPP and ecosystem ER. The Lloyd-Taylor respiration function is widely used in ecosystem respiration calculation [30]. Here, the Lloyd-Taylor respiration function was modified in two ways. First, in order to consider the impact of seasonal changes in biomass on respiration, _a_\(\times\)_EVI_ was added to the respiration rate at the reference temperature [11]. Second, air temperature was replaced with LST. The formula is as follows:  
\[NEP=GPP-ER=GPP-(Rref+a\times EVI)\text{e}^{E(\frac{1}{56.02}\ \frac{1}{( LST+46.02)})} \tag{8}\]  
where Ref (g C/m\({}^{2}\)/day) is the respiration rate at the reference temperature, and a (g C/m\({}^{2}\)/day) is an empirical coefficient. E0 (\({}^{\circ}\)C) is the activation energy parameter, which determines sensitivity of the respiration to temperature.
## 2 Materials and Methods
### Subjects and Dataset Composition  
This study utilized pre-acquired axial MRA images of the brain. The dataset consisted of 20 TOF-MRA cases, each containing approximately 160-172 axial slices, resulting in a total of approximately 3284 images. While the images were pre-acquired, we provide details on the acquisition parameters for reproducibility and to highlight the imaging conditions under which our segmentation model was trained. The MRI scanner's acquisition parameters are summarized in Table 1.  
All TOF-MRA datasets were captured using standard clinical protocols that rely on the inflow effect of fresh spins rather than exogenous contrast agents. Following acquisition, the raw DICOM files were converted to 512 \(\times\) 512 pixel JPEG format using \"XTREK View\" by J-MAC Corporation (J-Mac System, Inc., Sapporo, Japan). This conversion was performed to facilitate subsequent steps, including data annotation and network training. While JPEG compression can introduce minor artifacts, the goal of automating tissue removal in MIP images meant that slight losses in dynamic range were unlikely to significantly affect semantic segmentation performance. A workstation equipped with the Windows 11 operating system, an Intel Xeon Gold 5115 CPU (2.4 GHz, 10 cores, 20 threads) (Intel, Santa Clara, CA, USA), four NVIDIA Quadro P5000 GPUs (16 GB VRAM each) (NVIDIA, Santa Clara, CA, USA), and 128 GB of DDR4 2666 MHz RDIMM memory was employed for image processing and deep learning tasks.  
These TOF-MRA datasets serve as the target for the image processing described in the next section, with the aim of clearly visualizing intracranial vessels crucial for evaluating the internal carotid system. The dataset characteristics directly influence the training and performance of the semantic segmentation model discussed later.