esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance Analysis
#### 3.3.3 Performance Results after Cross-Validation  
Cross-validation was applied to confirm the results and check for overfitting. For this purpose, the initial dataset was separated into ten subsets, and the performance of each one was analyzed, with average performance finally being obtained. Table 8 shows these results for the mode matrix for all years, while Table 9 shows the results for the mode matrix for every three years.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Random Forest** & **SVM** & **Logistic Regression** \\ \hline Accuracy & 0.80 (Train); 0.78 (Test) & 0.79 (Train); 0.79 (Test) & 0.74 (Train); 0.73 (Test) \\ F1 Score & 0.74 (Train); 0.71 (Test) & 0.72 (Train); 0.70 (Test) & 0.70 (Train); 0.69 (Test) \\ Recall & 0.71 (Train); 0.70 (Test) & 0.71 (Train); 0.70 (Test) & 0.68 (Train); 0.67 (Test) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance values obtained for each model for the matrix created by calculating the mode by year for each variable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Random Forest** & **SVM** & **Logistic Regression** \\ \hline Accuracy & 0.92 (Train); 0.81 (Test) & 0.86 (Train); 0.85 (Test) & 0.84 (Train); 0.83 (Test) \\ F1 Score & 0.90 (Train); 0.75 (Test) & 0.70 (Train); 0.69 (Test) & 0.71 (Train); 0.69 (Test) \\ Recall & 0.88 (Train); 0.70 (Test) & 0.66 (Train); 0.65 (Test) & 0.62 (Train); 0.60 (Test) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance values obtained for each model for the matrix created by calculating the mode every 3 years per variable.  
Table 8 shows the cross-validation results, which indicate that there was no overfitting in the accuracy, F1 score, or recall results. In addition, it confirms that the random forest model has the best performance. On the other hand, the use of the mode matrix every three years (results in Table 9) leads to a better performance than the models trained using the mode matrix for all years (results in Table 8). These results corroborate the idea that adding a temporal change factor to the variables can improve the predictive ability of the algorithms.  
Figure 4: ROC curve of SVM model that receives the mode matrix every 3 years as input.  
Figure 3: ROC curve of random forest model receiving the mode matrix every 3 years as input.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 4 UWSNs: Communication Models
### Transmission Loss Model: Deep and Shallow Water  
Transmission loss also known as propagation loss, defines the decrease in energy level of signal as wave propagates through the specified medium. For shallow water, the propagating acoustics signals experience cylindrical spreading [41; 42].  
For shallow water transmission loss is calculated as follows:  
\[TL=10 log(R)+a_{u}(R)\ \times\ 10^{-3} \tag{3}\]  
For deep sea transmission loss is calculated as follows:  
\[TL=20 log(R)+a_{u}(R)\ \times\ 10^{-3} \tag{4}\]
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Oxidative Stress  
As people age, there is an increase in oxidative damage and a decrease in antioxidant defenses in the body [84]. Oxidative stress also plays a major role in age-induced hair graying via multiple effects on the viability and functionality of MSCs and mature melanocytes [85; 86; 87]. Cross-sectional studies support that systemic redox imbalance or oxidative stress can affect the incidence and degree of premature hair graying [88; 89]. Specifically, serum malondialdehydedehyde (MDA) levels increased while serum glutathione (GSH) and superoxide dismutase (SOD) levels decreased in patients with premature canities compared to controls [89].  
Various factors, including cellular melanin synthesis reaction and ultraviolet (UV) irradiation, can cause the production of reactive oxygen species (ROS) in melanocytes [90; 91]. Furthermore, the age-related decrease in BCL-2 expression makes melanocytes more vulnerable to oxidative stress and apoptotic death [92].  
The reduction in catalase (CAT) expression and activity in hair follicles causes hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) accumulation, impairment of methionine sulfoxide repair, melanocyte dysfunction, and cell death [93; 94]. In addition to CAT downregulation, hydroxyl radical scavenging activity becomes repressed in unpigmented hair follicles compared to normally pigmented hair follicles [50]. Ataxia telangiectasia mutated (ATM) protein, a serine/threonine protein kinase involved in the protection against oxidative DNA damage, is highly expressed within the nuclei of melanocytes in anagen hair follicle bulbs, but its expression is reduced proportionally to the degree of canities [95]. Thus, the balance between oxidative stress and antioxidant defense can affect human hair follicle growth, differentiation, and pigmentation.  
The role of oxidative stress as the main causal mechanism in hair graying is also supported by hair depigmentation in animal models irradiated with UV, \(\gamma\), or X-rays [96; 97]. Hair graying can be induced by irradiating animals or organ-cultured hair follicles with \(\gamma\)-rays or X-rays, providing useful experimental models for hair graying research [97; 98; 99]. HFKSCs are more prone to oxidative DNA damage induced by X-ray irradiation than follicular MSCs [100]. When PUVA phototherapy (a combination treatment using psoralen and UVA) was applied to the backs of black mice after their hair had been removed, gray or white hair grew instead of black hair on the affected bare skin, and this effect was attenuated by pretreatment with a gel containing SOD [96]. Dietary restriction delayed hair graying in C57 BL/6 J mice induced by \(\gamma\)-ray irradiation, but the effect was temporary [101]. Dietary restriction prolongs the quiescence of DNA-damaged hair follicle stem cells and does not prevent their eventual death and depletion.
# Analysis of Nanoindentation Test Results of Asphalt Mixture with Different Gradations
## 3 Results and Discussion
### Effect of Different Rates of Filler and Asphalt
As can be seen from the underlined data in Figure 12. This further confirms that the nanoindentation modulus of open-graded asphalt mixture is twice as high as that of gap-graded mixtures. This may also be because the interfacial interaction mechanisms between asphalt and mineral powder include chemisorption, physical adsorption, and mechanical embedding. The three main bonding mechanisms work together to influence the final bond strength of the mastic. When the RFA and the mineral powder amount increase, the adhesion between asphalt and mineral powder is enhanced, resulting in an increasing in the strength of the asphalt mastic, which leads to the modulus increase. To substantiate this, a scanning electron microscope [7] study was carried out on the samples after nanoindentation. The results are, thus, shown in Figure 13.  
Figure 11: Different rates of filler and asphalt load depth curves before and after polishing. It can be seen from Figure 13 that when the RFA is low, the distance between the mineral particles in the asphalt slurry is farther with more free asphalt. The enhancement effect of mineral powder on the asphalt mastic is smaller (as shown in Figure 13a). The binding interface of the asphalt mastic is clear and distinct. With the increase of RFA, the total surface area of mineral powder particles in the mastic increases. The more structural asphalt is formed by the adsorption of mineral powder, the greater is the enhancement effect of mineral powder on asphalt mastic. The bonding interface of the asphalt mastic tends to be stable, and there is smoother transition morphology. The mineral filler is well-coated by the asphalt, which matches the results of [PERSON] et al. [28] regarding the microscopic interface of the asphalt mastic. When the RFA was increased to 1.6, the structural asphalt on the surface of different mineral powder particles interconnected to form a mesh structure, and the enhancement effect of mineral powder on the asphalt mastic reached the maximum (as shown in Figure 13e). According to their dispersion state(as shown in Figure 14), the above results are similar to the existing studies that classify mineral powders into suspended volume-filled, particle-structured, and particle-clustered failure states [29]. It can be concluded that the results of the SEM's plot correspond to the minimum nanoindentation modulus at the RFA of 0.8 and the highest value of the nanoindentation modulus at the RFA of 1.6. The reason for this result is related to the enhanced interaction between the asphalt and the mineral powder, as has been explained earlier. Figure 12: Nanoindentation modulus of asphalt mastic specimens. (As can be seen from the underlined data, the nanoindentation modulus of the RFA 1.6 was approximately twice the modulus of RFA 1.2). Figure 13: _Cont._  
Table 4.0 SEM diagram of asphalt mastic with different rates of filler and asphalt. (**a–e**) 500\(\times\), (**f–j**) 3000\(\times\).
###### Abstract  
In all developing countries, the application of biomedical signals has been growing, and there is a potential interest to apply it to healthcare management systems. However, with the existing infrastructure, the system will not provide high-end support for the transfer of signals by using a communication medium, as biomedical signals need to be classified at appropriate stages. Therefore, this article addresses the issues of physical infrastructure, using Hadoop-based systems where a four-layer model is created. The four-layer model is integrated with Fuzzy Interface System Algorithm (FISA) with low robustness, and data transfers in these layers are carried out with reference health data that are collected at various treatment centers. The performance of this new flanged system model aims to minimize the loss functionalities that are present in biomedical signals, and an activation function is introduced at the middle stages. The effectiveness of the proposed model is simulated by using MATLAB, using a biomedical signal processing toolbox, where the performance of FISA proves to be better in terms of signal strength, distance, and cost. As a comparative outcome, the proposed method overlooks the conventional methods for an average percentage of 78% in real-time conditions.  
biomedical signals; Hadoop systems; healthcare; fuzzy interface system; optimization Article
# Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation
## 6 Conclusions  
Recently, classifying food, estimating food quantity and nutritional content, and recording dietary intake have become important issues. In order to obtain accurate results, Mask R-CNN is applied for food instance segmentation and calorie estimation. Gimbap is chosen as an example of solid-volume food to show the effectiveness of the proposed methods. Firstly, based on the posture of Gimbap in plates, the labeled approach for the Gimbap image datasets is described, in order to process the Gimbap food segmentation and calorie estimation system. Secondly, an optimized model is created by fine-tuning Mask R-CNN architecture to obtain classification, bounding box, and mask information. In order to apply the method in real life, distinguishing from other methods using 3D LiDAR data or RGB-D images data, this work uses RGB images to train the model and uses a simple monocular camera to test the result. After training, the model shows 88.13% for Gimbap1 in AP (0.5 IoU) and 82.72% for Gimbap2 in AP (0.5 IoU). mAP at 0.5 IoU reaches 85.43%. Thirdly, a novel calorie estimation approach is proposed, combining the calibration result and the instance segmentation result. the calorie estimation approach can be extended to any solid-volume food, such as pizza, cake, burger, fried shrimp, oranges, and donuts. Finally, the effectiveness of the proposed approaches is proved. The Gimbap can be detected well. Based on Gimbap's segmentation instance result, Gimbap's calorie is correctly estimated. The calorie estimation result is close to Gimbap's calorie definition. Compared to other vision-based methods, the method in this paper outperforms in the accuracy of food segmentation and calorie estimation, since it uses the mask information from the Mask R-CNN algorithm and considers unseen food.  
Conceptualization, Y.D. and K.L.; methodology, Y.D.; software, Y.D.; validation, Y.D.; formal analysis, Y.D.; investigation, Y.D.; resources, Y.D.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D. and K.L.; visualization, Y.D.; supervision, K.L.; project administration, S.P.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTID)(P0008473, HRD Program for Industrial Innovation).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Calorie estimation result based on Faster R-CNN algorithm. Because it does not consider the unseen Gimbap, the calorie estimation result is much less than normal.
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 4 Proposed Indoor Positioning
### Limiting of Initial Search Region  
The method of limiting the initial search region described in this subsection is the main contribution of this paper. The PSO is a technology to find the global optimum based on intelligent particles. When positioning a user's location through the PSO, it is very important to limit the initial search region. Therefore, in this paper, we propose a scheme of limiting the initial search region to highly correlated SPs derived through fingerprinting and WFM algorithms.  
First, the closeness between the user and each SP can be known based on the Euclidean distance vectors obtained through (7). After that, the Euclidean distance vectors are sorted in descending order from the largest value. The sorted values can be expressed as follows.  
\[d_{k,c}=[d_{k,c,1},\ d_{k,c,2},\ldots,\ d_{k,c,n},\ldots,\ d_{k,c,N}] \tag{12}\]  
where \(d_{k,c,1}\) is the SP closest to the user among all SPs. To limit the initial search region, three or more SPs should be selected. Therefore, the proposed method limits the initial region by selecting four SPs from among the SPs sorted in descending order based on the results in Figure 3.  
Figure 3 shows the initial particle distribution of the PSO in both cases with a limited initial search region and a non-limited initial search region. As shown in Figure 3, a high positioning accuracy can be obtained if the region is limited based on four SPs. \(S_{SP}\) represents the number of SPs selected for region limitation. When the initial search region is limited and not limited, the initial distribution region of particles can be expressed as in (13) and (14), respectively.  
\[A_{limited}=d_{SP}^{2} \tag{13}\]  
\[A_{non\_limited}=d_{w}\times d_{l} \tag{14}\]  
where \(d_{w}\) represents the width of the search region, \(d_{l}\) represents the length of the search region, and \(d_{SP}\) represents the distance between SPs. In general, the range of \(d_{SP}\) is \(0<d_{SP}<d_{w},d_{l}\), so if the region is limited by SPs, it is possible to narrow the region that the particle needs to search to find the global optimum.  
Figure 4 shows the initial particle distribution of PSO in the case where the initial search region is limited and in the case where the initial search region is non-limited. As shown in Figure 4, when the region is limited, it can be confirmed that the particles are distributed close to the actual user's location \(X_{R}\). Based on this, the PSO process can be performed to precisely position the user's location. The next subsection describes the PSO.  
Figure 3: Positioning error according to number of selected SPs.
## 2 OFRD Method
### Fiber Protection Test  
To address potential fiber damage caused by stress concentrations during the concrete curing process, pre-testing was conducted prior to the shaft lining model experiments. Selected fibers were embedded in concrete specimens (Point A), protected by adhesive-sealed plastic tubes at the concrete-air interface (Point B), while others remained exposed (Point C). Continuous signal monitoring was performed throughout the concrete curing process. The test configuration is illustrated in Figure 2, with corresponding monitoring results shown in Figure 3. Figure 3 shows the following results: at Point A, the fiber was embedded in concrete. Initially, tensile strain increased due to heat from concrete hydration. Later, compressive strain developed as the concrete hardened and shrank. At Point B, the fiber was not embedded. The strain remained stable throughout the test. At Point C, the fiber was at the concrete-air interface without protection. Strain increased over time due to stress concentrations. This confirms that protective measures can reduce stress concentrations effectively. The results prove that protecting fibers minimizes damage from stress concentrations. It also ensures stable and reliable signal collection during monitoring.
# Highly Active under VIS Light M/TiO\({}_{2}\) Photocatalysts Prepared by Single-Step Synthesis
## 3 Results and Discussion
### Photocatalytic Studies
#### 3.4.1 Copper Loading Effect on Different Substrates  
The effect of copper loading on titania rutile was investigated to determine the optimum copper loading on the latter substrate regarding the material's photocatalytic activity. Four different photocatalysts were prepared by changing the metal loading concentration on the substrate from 0.1 up to 1% \(w/w\). Interestingly, 0.1 and 0.25% Cu-supported photocatalysts were found to be superior during the MB decolorization under visible light compared to pure TiO\({}_{2}\) (R). Cu loading of 0.1% appeared to be the most effective doping, as it achieved the highest degradation at 4 h under visible light. At 4 h irradiation, almost 100% (97%) MB decolorization was accomplished (Figure 6).  
Titania anatase was also tested as a substrate for the photocatalyst. Metal loadings of 0.1 and 0.25% copper were tested on anatase titania substrate. The results are presented in Figure 7. According to Figure 7, the increase in the irradiation time results in the MB concentration decreasing at a different rate depending on the time interval. The degradation reaction is much faster during the first hour of irradiation. Kinetics in heterogeneous catalysis is highly non-linear due to vastly different rates among elementary steps and competition for active sites among intermediates [42]. The obtained curves dictate that the catalysts prepared with titania anatase exhibited significantly lower photocatalytic activity towards the MB decolorization.  
According to the latter findings, it is obvious that 0.1% Cu loading on rutile titania is the most suitable amongst the examined catalysts, as it possesses the highest activity under visible light.  
Metal loading above 0.5% is not beneficial for either titania form. Possibly, the metal copper particles act as sites for the recombination of the generated electron holes and, hence, decrease the photocatalytic activity [43].  
Figure 6: Cu loading concentration influence on photocatalytic activity under visible light.  
Likely, the Cu addition has opposite effects on the photocatalytic efficiency of Cu/TiO\({}_{2}\) catalysts. Thus, the catalytic activity of the samples is a synergistic effect of those parameters.  
Titania rutile is a more efficient substrate for copper addition. Both the photocatalysts prepared with the same % metal loading but with rutile as substrate were significantly superior in terms of photocatalytic MB degradation. Titania anatase has a 3.2 eV energy band gap that is higher than that of the Rutile TiO\({}_{2}\) (3.0 eV) [44]. Since the metal addition further decreases the photocatalysts' energy band gap, it could possibly justify its enhanced efficiency towards the MB degradation under visible light.
## 1 Introduction
For external sources of blood loss, advanced hemostatic dressings and tourniquets can be used to control the bleeding, but no such tools are available for the control of internal hemorrhage. As an example, one area where the medical profession has failed to alter the mortality rate remains in the period immediately following injury, including the pre-hospital phase. The majority of these deaths are due to hemorrhagic shock (HS) or traumatic brain injury (TBI) [2; 3; 4; 5]. Of these two conditions, bleeding is more apparent and treatable, which makes it the number one cause of preventable deaths. Despite numerous advances in trauma care, a recent multi-national trial of more than 20,000 patients [6] demonstrated that the majority of deaths occur within a few hours after injury, with less than 2.5 percent of the injured succumbing to multiple organ failure in a delayed fashion. To control internal hemorrhage, resuscitative endovascular balloon occlusion of the aorta (REBOA), which may control internal bleeding by stopping the blood flow below of the inflated balloon, has recently gained prominence as a technique for controlling non-compressible torso hemorrhage [7; 8; 9]. Even though the use of REBOA may lead to early survival advantages for HS, concerns have been raised regarding the risk of end-organischemia (and a reperfusion injury following balloon deflation) with the application of complete REBOA [10]. Partial REBOA might be an alternative, but to adjust the degree of occlusion, instrumentation is needed to verify the downstream tissue and organ condition. Another promising approach to controlling bleeding is to administer drugs concurrently (with REBOA inflation), and this approach has been shown to decrease ischemic injury and preserve organ function. Valproic acid (VPA) is one such drug that has been shown to sustain life despite massive blood loss and that has been in clinical use for over 50 years. When given in an appropriate dose, VPA quickly upregulates numerous cytoprotective pathways [11] and improves survival in models of ischemia-reperfusion [12; 13], lethal hemorrhage, TBI with hemorrhage, and polytriuma [14; 15; 16; 17]. VPA is safe, shelf-stable, and easy to administer, and its co-administration with REBOA may attenuate the adverse consequences of aortic occlusion. However, to understand how to use partial REBOA effectively, adding VPA also optimally requires the ability to monitor the brain, tissue, and organ metabolic states as well as the hemodynamic response.
## 5 Future Work
### Error Analysis of CMLs Applied to Mountainous Areas  
A strength of the CML rainfall measurement technique, which has been widely mentioned in many literatures [41; 42; 43], is that it is available in mountainous areas where the deployment of radar and rain gauges is difficult. However, due to the unique topographic and climatic conditions of mountainous areas, the CML rainfall measurement technique may need to be adapted. First, the elevation angles of CMLs in mountainous areas are no longer negligible and can even reach tens of degrees [193]. Therefore, the calculation of the raindrop extinction cross-section needs to take into account the tilt angle of the raindrop with respect to microwave propagation, but little attention has been paid to this in the calibration of the \(\gamma\)-\(R\) relationship [28]. Second, the rainfall distribution on the inclined path is more heterogeneous because the variation of rainfall in the vertical direction needs to be considered in addition to the horizontal direction [194; 195]. In order to validate that CMLs can substitute radar and rain gauges in mountainous areas and to promote the application of this technology, a systematic analysis of their performance on inclined paths must be performed based on experiments or simulations in the future.