esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 2 Related Knowledge
### Network Architecture  
As we know, the energy consumption is important in UWSNs, which may limit the long lifecycle. Hence, the network topology is an essential aspect that needs to be well designed to decrease the severe impact on the network performance. In addition, both the network's reliability and capacity rely on the topology. Therefore, how to organize such a network topology is a challenging job to which the researchers need to pay more attention. Here, we categorize the network architecture as 2D-UWSNs and 3D-UWSNs. For 2D-UWSN architecture: in [17], the authors conclude that this type of architecture is as follows: a group of sensor nodes (cluster) consists of UWSNs, and a cluster head (anchor node) is required for each cluster. Anchor nodes collect the information which is gathered by each member of the cluster and relays it to the surface buoyant nodes. The 2D communication contains a horizontal communication link and a vertical communication link. The communication between each cluster communicates, and the communication between related anchor node and the sensor node are horizontal communication links; however, the communication between the anchor node and surface buoyant node is a vertical communication link. 3D-UWSN architecture: some ocean phenomena cannot be adequately observed only by utilizing the sensor nodes in the ocean bottom. Hence, different depths have deployed the sensor nodes in the 3D-UWSN architecture. Each sensor node conveys the local underwater activity. In [2], three types of communications (3D) are described as follows: (1) inter-cluster node communication at different depths; (2) sensor-anchor node communication; and (3) anchor-buoyant node communication.
# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
## 3 Materials and Methods  
In this study, a methodology based on logistic regression and vulnerability analysis for identifying critical locations in the WDN is developed using real-time data collected by SEWA throughout the WDN of the City of Sharjah. The methodology adopted for this research is used to overcome the limitations of other leak detection methods. In this study, leakage is the dependent variable, and the independent variables are the conditioning factors, which include the network's operational and physical parameters. The study enhanced current solutions by integrating vulnerability analysis into the prediction model. The proposed model overcomes the challenges of previous prediction models by considering fixed physical factors and varying operational factors. The proposed model also advances the current state of the art in leak detection by considering static and dynamic factors in leak detection modeling.  
This study employs the Analytic Hierarchy Process (AHP), which is a decision-making technique that involves breaking down complex problems into smaller, more manageable parts [34; 35]. AHP is widely used in fields such as business, engineering, and social sciences. The method allows decision makers to compare and evaluate the relative importance of different criteria or alternatives by assigning them numerical values. One of the main advantages of the AHP method is its ability to handle complex decision-making problems by breaking them down into smaller, more manageable components. It also allows for the consideration of multiple criteria and perspectives, providing a more comprehensive evaluation of alternatives. However, AHP does have limitations, including the potential for subjectivity and the need for extensive data collection and analysis. Overall, the AHP method is a powerful tool for decision making, particularly in situations where multiple criteria must be considered [34; 35].
## 5 Case Studies  
We conducted our case studies in two different companies in Germany. The first installation was done at a retailer and the second at an automobile manufacturer. Both case studies were based on universal EMS software, in which numerous other data sources, measuring instruments, and data loggers are connected. In this case, the term \"universal\" refers to the EMS integrating its energy meters across manufacturers and connectors for common communication standards, such as Modbus TCP, being already available in the software.  
In both cases, the energy sensor must be implemented as part of a CPES. This provides us with networked sensors, which are necessary for forming smart microgrids and with which we can realize further added value, for example for smart building functions. For non-standardized systems, such as manufacturer-specific data loggers, gateways, or IoT clouds, additional connectors must be developed in the EMS if required.
## 3 Experimental Set-Up
### Data Acquisition and Preprocessing
#### 3.4.2 Preliminary Data Process  
In the preliminary data process, the data contained in all driving decision data sets are calculated to obtain the driving decision samples. From the driving trajectory data, we can obtain \(\Delta L_{\text{M}_{\text{bf}},0}\), \(\Delta L_{\text{M}_{\text{bf}},0}\), \(\Delta L_{\text{M}_{\text{bf}},0}\), \(h_{t}\) and the driving decision (free driving, car following or lane changing), from the speed information, we can obtain \(\Delta v_{\text{M}_{\text{bf}},\text{M}_{\text{bf}}}\), \(\Delta v_{\text{M}_{\text{bf}},\text{M}_{\text{bf}}}\), and \(\Delta v_{\text{M}_{\text{bf}},\text{M}_{\text{bf}}}\), from the road environment parameters, we can obtain the values of \(\mu\), \(\rho\), \(\tau\), \(\delta\). One sample includes one reference index vector \(H=\left[\Delta L_{\text{M}_{\text{bf}},0}\right]\), \(\Delta L_{\text{M}_{\text{bf}},0}\), \(\Delta L_{\text{M}_{\text{bf}},0}\), \(\Delta L_{\text{M}_{\text{bf}},0}\), \(\Delta v_{\text{M},\text{M}_{\text{bf}},\text{M}_{\text{bf}}}\), \(\Delta v_{\text{M},\text{M}_{\text{bf}}}\), \(h_{t}\), \(\mu\), \(\rho\), \(\tau\), \(\delta\right]\) and its corresponding driving decision.  
\begin{table}
\begin{tabular}{c c} \hline
**Road Parameters** & **Setting Values** \\ \hline Adhesion coefficient \(\mu\) & 0.75/0.55/0.28/0.18 \\ Road curvature \(\rho\) (m\({}^{-1}\)) & 0/3/1.67/1 \\ Road slope \(\tau\) & 0/2\%/4\%/6\% \\ Road visibility \(\delta\)/m & 1000/500/100/50 \\ \hline \end{tabular}
\end{table}
Table 1: Settings of Road Parameters.
# Consequences of Glucose Enriched Diet on Oncologic Patients
### Glucose and Its Influence on Cancer  
Glucose is the principal source of metabolic energy in most organisms. Its transport through cellular membranes, which are impermeable to polar molecules, is done through glucose transporters, which mediate glucose transport by assisted diffusion or by secondary active transport.  
Aerobic glycolysis is a crucial mechanism in cancer cells' glucose metabolism, in which glucose is first supplied into the cells by glucose transporters and subsequently transformed to pyruvate by multiple enzymes. The implication of enzymes in this pathway is highly important, with phosphofructokinase (PFK), pyruvate kinase (PK), and hexokinase II (HKII) being the most important [14].  
Facilitative sugar transporter (GLUT) and Na+/glucose co-transporter (SGLT) families are the principal glucose transporters in mammalian cells [15; 16]. The plasma membrane's existing gradients in sugar concentration between the inner and outer sides are utilized by GLUT transporters to aid in their translocation. On the contrary, SGLT proteins transfer sugars within cells against the concentration gradient with the consequent energy expenditure [17; 18].  
Glucose absorption is the step that controls the pace of its usage, underlining the relevance of the GLUT transporters in metabolism [6]. The cell has to acquire glucose in order to commence glycolysis, which is the first conversion to obtain energy. In healthy cells, glucose is fully oxidized to ATP [13; 17]. This is why glucose absorption and metabolism  
Figure 1: Cell cycle stages and checkpoints. Throughout the S period, DNA replication occurs (from 2c to 4c). After mitosis, two cells originate with a DNA content equal to 2c that repeat the cycle. Throughout the cycle, especially at the beginning of interphase and in mitosis, there are checkpoints regulated by cyclins and cyclin-dependent kinases (Cdk).  
are so critical for cell development and division [13; 17]. For this reason, sugar consumption is associated to cell proliferation and hence to the development of cancer [18].
## 5 Simulation Results and Discussion
### Discussion of Results for the Rural Network  
The results show that the proposed methodology successfully selected investment options for different levels of DSO investment funds (Table 15). The priority list, sorted by indicator reduction from the highest to the lowest, is provided in Section 5.3. This ordering allows for strategic investment planning, ensuring that the most impactful options for reliability improvement are addressed first.  
The greatest reduction in the analyzed indicators (_SAIDI_, _ENS_, and _COST_) for the rural network is achieved by the simultaneous construction of a new line and the installation of recloser REC29. This measure forms a semi-ring, improving supply flexibility and reducing interruption duration by enabling an alternative supply path. Given its impact, this option is prioritized when investment resources allow. A further reduction is achieved by installing recloser REC12, which enhances network segmentation and speeds up fault isolation. Replacing overhead lines with underground cables improves reliability due to lower failure rates. As indicated by the results, replacing overhead lines with underground cables was not selected due to higher installation costs. However, it should be noted that although underground cables require a higher initial investment, their lower maintenance costs can lead to long-term savings by reducing overall expenditures. The next indicator reduction is achieved by replacing old circuit breakers with new ones, with the greatest effect when the circuit breaker is positioned at the beginning of the feeder. Finally, replacing old disconnectors and fuses provides the smallest impact among the analyzed options.  
For the rural network, Figure 10 presents the graphical results of the calculations assuming a maximum available budget of EUR 50,000. The explanation of the symbols in Figure 10 corresponds to the explanation provided for Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Component** & **REC12-new** & **L50 + REC29-new** & **CB1** & **CB25** & **CB6** & **CB45** \\ \hline _Ratio_ [1/yr] & 1.75 & 0.48 & 0.22 & 0.22 & 0.09 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Value of _Ratio_ for the rural network options.  
Figure 10: Applied investment options for the rural network with a maximum budget of EUR 50,000.
## 2 Materials and Methods
### Benchmark Forecasting Methods  
Persistence (PE) is used as a naive benchmark forecasting method. The forecast is obtained whlie assuming that the hourly PV power profile for day \(D+1\) is the same as the one of the previous day \(D\).  
Perfect forecasts (PF) are also considered as benchmark forecasting method, and as its name suggests, it consists on assuming that the forecast of a given variable is actually the real (measured) value of that variable. This is used for PV power output, as the best possible (reference) forecast to compare against the other forecasting methods.  
The metrics utilized to measure the forecasting errors are the relative mean-bias error (rMBE), relative mean-absolute error (rMAE), and the relative root-mean-square error (rRMSE), computed as in Equations (2)-(4).  
\[rMAE=\frac{\frac{1}{H}\sum_{h=1}^{H}\left\|Fcast^{h}-Meas^{h}\right\|}{\frac{1 }{H}\sum_{h=1}^{H}Meas^{h}} \tag{2}\]  
\[rMBE=\frac{\frac{1}{H}\sum_{h=1}^{H}(Fcast^{h}-Meas^{h})}{\frac{1}{H}\sum_{h=1 }^{H}Meas^{h}} \tag{3}\]  
\[rRMSE=\frac{\sqrt{\frac{1}{H}\sum_{h=1}^{H}(Fcast^{h}-Meas^{h})^{2}}}{\frac{ 1}{H}\sum_{h=1}^{H}Meas^{h}} \tag{4}\]  
In this study, a generator convention has been used; hence, for any DER, positive values of power mean power being delivered by it, while negative values mean power being being consumed by the DER.
## 3 Simulation Results
### Success Rate  
Table 6 shows the success rates of the algorithm across different PV array configurations. Both Scenario 1 and Scenario 2 achieved an excellent success rate of 100%, producing acceptable best solutions across all 63 simulations. This highlights the reliability and stability of these configurations in consistently delivering the best results. In contrast, Scenario 3 and Scenario 4 exhibit slightly lower success rates, reaching 88.8% and 93.65%, respectively. However, Scenario 3 and Scenario 4 have a level of difficulty arising from being four-row PV configurations. These findings highlight the importance of parameter tuning to ensure consistent and reliable performance, and Scenario 1 and Scenario 2 structures, that is, three-row structures, excel in this regard.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Population** & **Crossover** & **Mutation** & **Population** & **Best** & **Convergence** & **Optimum Configuration** & **Expr.** \\
**Number** & **Rate** & **Rate** & **Diversity** & **Fitness (CVI)** & **Time (s)** & **(F: Fixed, A: Adaptive Panels)** & **Number** \\ \hline \multirow{8}{*}{100} & & 0.04 & 0.314747475 & 0.17 & 0.0012054 & F1-\(\lambda\)A1A3A6 F2-\(\lambda\)A2A4 F3-\(\lambda\)5 & 57 \\  & & 0.06 & 0.62666667 & 0.17 & 0.0054972 & F1-\(\lambda\)A1A3A6 F2-\(\lambda\)A2A4 F3-\(\lambda\)5 & 58 \\  & & 0.08 & 0.70705051 & 0.17 & 0.0046896 & F1-\(\lambda\)A1A3A6 F2-\(\lambda\)A2A4 F3-\(\lambda\)5 & 59 \\  & & 0.1 & 1.4832232323 & 0.17 & 0.0011965 & F1-\(\lambda\)A1A3A6 F2-\(\lambda\)A24 F3-\(\lambda\)5 & 60 \\  & & 0.12 & 3.127272727 & 0.17 & 0.0012361 & F1-\(\lambda\)A13A6 F2-\(\lambda\)A24 F3-\(\lambda\)5 & 61 \\  & & 0.14 & 2.699393939 & 0.17 & **0.0011394** & F1-\(\lambda\)A13A6 F2-\(\lambda\)A24 F3-\(\lambda\)5 & 62 \\  & & 0.16 & 4.330909091 & 0.17 & 0.0026603 & F1-\(\lambda\)A13A6 F2-\(\lambda\)A24 F3-\(\lambda\)5 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PV Array** & **Number of** & **Number of** & **Best Fitness (CVI)** & **Best Configuration** \\
**Configuration** & **PV Array** & **Experiments** & & \\ \hline Scenario 1 & 3 & 63 & **0.17** & F1-\(\lambda\)A1A3A6 F2-\(\lambda\)A2A4 F3-\(\lambda\)5 \\ Scenario 2 & 3 & 63 & 0.60 & F1-\(\lambda\)A5A6 F2-\(\lambda\)A1A3 F3-\(\lambda\)A2A4 \\ \hline Scenario 3 & 4 & 63 & **0.35** & F1-\(\lambda\)A7A8 F2-\(\lambda\)A1A3A4 F3-\(\lambda\)A25 F4-\(\lambda\)A6 \\ Scenario 4 & 4 & 63 & 0.56 & F1-\(\lambda\)A4 F2-\(\lambda\)A5 F3-\(\lambda\)A2A6A8 F4-\(\lambda\)A1A3A7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best configuration for CVI value.
# A Short S-Equol Exposure Has a Long-Term Inhibitory Effect on Adipogenesis in Mouse 3T3-L1 Cells
Additionally, ovariectomized Long-Evans rats treated with a subcutaneous injection of 5 mg of S-equol/kg/day showed a decrease in body weight gain [20], and ovariectomized Sprague-Dawley rats treated with 6.87 mg of equal/day for six weeks presented a reduced weight gain and abdominal fat accumulation, as well as a diminution in plasma Leptin, cholesterol, and triglyceride levels [21]. In contrast, a recent study performed in C57/BL6 mice on a high-fat diet (HFD) revealed that S-equol supplementation did not affect body weight and food intake, but exacerbated several aspects of HFD-induced metabolic disease, such as hyperglycemia and energy expenditure reduction; moreover, it also evidenced opposite effects on serum insulin and Leptin levels depending on sex [22]. To our knowledge, there are few reports about the effect of S-equol on adipogenesis in vitro. In 2010, [PERSON] et al. evidenced that the racemic mixture of (R,S)-equol at 0.1 to 20 umol/L significantly increased adipocyte differentiation and insulin-stimulated glucose uptake in C3H10T1/2 pluripotent stem cells. Moreover, (R,S)-equol at 10 and 20 umol/L significantly increased PPAR\(\gamma\) transcriptional activity in 3T3-L1 preadipocytes [23]. Later, [PERSON] et al. confirmed the stimulating effect of low concentrations of (R,S)-equol on adipogenesis. Importantly, they demonstrated that 100 um of (R,S)-equol inhibits adipocyte differentiation of the 3T3-L1 mouse cell line, observing a 40% decrease in lipid accumulation, as well as a reduced expression of the pro-adipogenic factors PPAR\(\gamma\), C/EBP\(\alpha\), and FAS, suggesting a bi-phasic effect of (R,S)-equol on adipogenesis in vitro [24]. The conflicting results about the beneficial metabolic effects of equal in in vivo and in vitro assays clearly indicate that additional studies are required to better understand the potential of S-equol for obesity and obesity-related diseases control. An additional problem is that some studies have not clarified the use of S-equol or (R,S)-equol, being a possible cause of variability in the results. Here, we aimed to characterize how the ER\(\beta\) agonist S-equol affects adipocyte differentiation and function.
# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
## 3 Results and Discussion
### Potential Markers of Grasevina Wines' Regionality
The figure was obtained using Google Earth version 6.1.  
in Australia. It appears that Grasevina, grown in a continental climate, follows the same trend. Interestingly, the compounds that were most discriminative between sub-regions were \(\alpha\)-terpinene, \(\gamma\)-terpinene, and 1,4-cineole. Grasevina wines from the Kutjevo district showed higher concentrations than Podunavlje wines. [PERSON] et al. [10] reported a higher level of 1,4-cineole in Cabernet-Sauvignon wines originating from the Margaret River with milder climates than South Australia. [PERSON] et al. [15] found similar results for \(\gamma\)-terpinene in Shiraz wines associated with cooler Australian regions. It is worth highlighting that terpinene isomers and 1,4-cineole followed the same trend, as the three compounds are connected by the same metabolic pathway [19]. These monoterpenes are also metabolically connected to 4-terpineol [19], and they seem to be all chemical markers of Grasevina wines, as suggested earlier. Their individual odors are described as herbaceous for 1,4-cineole and 4-terpineol [10; 20] and pine, citrusy, lemon, and lime odor for terpinene isomers [20]. It is worth noting that lemon and lime are descriptors used by wine experts to characterize the aroma of Grasevina wine, particularly in the Kutjevo district, which is considered the most iconic region for this variety in Croatia. Nevertheless, it is too speculative to state that these monoterpenes actually contribute directly or through perceptive interactions to the aromatic typicality of Grasevina wine. Further sensory experiments are warranted to confirm this hypothesis.
###### Abstract  
Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers.  
Internet traffic classification; machine learning; classification methods; port-based method; hybrid classifier; online Internet traffic classification; live Internet traffic
# Digital Technologies for the Assessment of Oral English Skills
## 3 Problem Research and Objectives  
The project aims to influence the development of teacher evaluation skills to enhance reflection on the practice and provide a better understanding of the increase in the chances of success of students with the implementation of a competency assessment. The main purpose of the AROSE project was to improve the knowledge about the evaluation of oral English skills in secondary education through the creation of an oral-competency assessment model and a digital assessment tool.  
The first phase of the project was focused on the creation of the model based on the common European framework reference for languages. Based on this model, and considering the suggestions collected by secondary education teachers, we designed and evaluated a web assessment tool to improve oral English communication.  
Our problem research was focused on the evaluation of this web tool. Thus, our research questions have been: Is this web tool useful for teachers? Could this web tool help them to assess the oral skills of their students? Our research objectives have been:  
* Objective 1: To know the real needs of teachers of English as a second language, in relation to the identification of methodologies and strategies for the evaluation of oral skills.
* Objective 2: To design and develop a digital platform for secondary education teachers to facilitate the oral English skills assessment.
* Objective 3: To evaluate the tool quality and teachers' satisfaction in secondary education (subject: English as a foreign language).  
At this point, it is clear (objective 3) that the evaluation has been based on two dimensions: teachers' satisfaction and quality of the web tool. The quality has been understood as both the technical quality (usability, accessibility, and navigability) and educational quality.