esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Laurel (_Laurus mobilis_ L.) Leaf Polyphenols for Modulation of Body Composition
## 4 Influence of Laurel Leaf Polypphenols on Body Composition
### Influence of Laurel Leaf Polyphenols on Adipose Tissue
#### 4.3.3 Apigenin  
Apigenin showed mostly antiobesity and anti-inflammatory effects related to adipose tissue. It was shown that apigenin can reduce visceral adiposity in obese mice by reducing the phosphorylation of the signal transducer and activator of the transcription 3 (STAT3) in adipocytes and its target genes, resulting in reduced expression of the PPAR-\(\gamma\) receptor, which is the critical nuclear factor in adipogenesis [110]. In addition, apigenin ameliorated insulin resistance and glucose intolerance in obese mice and produced increased energy expenditure and activation of lipolysis without higher cycling of free fatty acids due to the upregulated oxidation, thermogenesis and browning of the adipose tissue [109]. The stimulating effects of apigenin on the browning of white adipose tissue were connected with the expression of vascular endothelial growth factor A and upregulation of Prdm16 signaling cascade in murine white adipocytes [111]. It was also shown that apigenin can reverse the inflammatory suppression of human adipose tissue browning through the activation of the COX-2-PGE2 axis, which results in UCP1 induction [107]. In the same study, IL-1\(\beta\)-induced inflammation in adipocytes was repressed by apigenin's inhibitory effects on inflammatory markers and the NF-kB signaling pathway. In another study [112], apigenin activated PPAR\(\gamma\), which resulted in blocked p65 translocation into nuclei and consequently decreased NF-\(\kappa\)B activation, resulting in decreased levels of proinflammatory cytokines in obese mice. In addition, levels of hepatic enzymes, triglycerides, total cholesterol, and liver and muscular steatosis were reduced and glucose resistance was improved. In another study [106], apigenin prevented colonic inflammation and motor dysfunctions in obese mice through reduction of the levels of malondialdehydedehyde and proinflammatory cytokines, eosinophil infiltration, substance P and iNOS expression and the normalization of electrically evoked tachykininergic and intergic contractions [106]. Also, antibody effects of apigenin glycoside were observed in which vitexin (apigenin-8-C-glucoside) ameliorated adipogenesis and obesity in male C57 BL/6J mice by affecting AMP-activated protein kinase-\(\alpha\), CAATT element binding protein-\(\alpha\) and fatty acid synthase in white adipose tissue [108].  
The animal studies clearly point to the beneficial effects of the reviewed components of the laurel leaves. However, studies in humans are warranted in order to switch from translation research to clinical trials and prove the noted benefits.
## 8 Conclusions  
This paper discusses widely used important MPPT methodologies, including traditional and advanced techniques, in solar PV systems based on their performance under uniform and non-uniform irradiance conditions. While traditional MPPT algorithms may still be effective under uniform insolation, they often fail to achieve optimal performance in complex environments, such as partial shading. Advanced techniques, though more complex and costly, offer greater efficiency and accuracy in tracking the true MPP. The choice between traditional and advanced MPPT algorithms ultimately depends on the specific requirements of the PV system, including its scale, environmental variability, and the desired level of optimization. The comprehensive comparison of various MPPT methods provided in this paper offers valuable insights, enabling researchers and practitioners to make informed decisions when selecting the most suitable approach for specific PV applications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Current Trends** & **Applications** \\ \hline Traditional & Widely used; some hybrid versions & Common in small to medium-scale \\  & are emerging. & PV systems. \\  & Increasing popularity in dynamic & Used in residential and commercial \\  & conditions. & PV systems. \\  & Using with predefined tables for & Applied in scenarios with stable \\  & known conditions & solar conditions. \\  & Less common; used in stable & Limited to specific scenarios with \\  & environments. & stable conditions. \\ \hline Advanced & Growing integration with smart & Employed in advanced PV systems \\  & grids and complex systems. & and smart grids. \\  & Increasing use due to advancements & Used in predictive maintenance and \\  & in AI and data analytics. & optimization. \\  & Rising interest in high-dimensional & Applied in large-scale PV systems \\  & and complex optimization & and energy management. \\  & Focus on balancing exploration and & Used in logistics and dynamic \\  & exploitation. & energy systems. \\  & Effective solutions sought for & Applied in various engineering and \\  & continuous optimization and & energy optimization tasks. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Current trends and applications for traditional and advanced MPPT algorithms [105,106].  
**Author Contributions:** Conceptualization, M.S.E.; Investigation, M.S.E. and G.G.; Writing--original draft, M.S.E.; Writing--review & editing, G.G. and A.E.C.; Supervision, A.E.C. and H.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
**Data Availability Statement:** No new data were created or analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Problem Statement
### Network Model  
In this section, the network model of the topologies is described as follows. The network topology which runs the protection strategy is represented by a graph named \(G(V,L)\). \(V\) is the set of nodes, and \(L\) is the set of direct links. Every two nodes \(a\) and \(b\) are connected by one link \(l_{a,b}\): thus, \(l_{a,b}\in L\). The service arriving in the QKDN is denoted as \(r_{k}(s_{r},d_{r},M_{r},t_{a},t_{r})\). \(R_{k}\) is the set of all services. \(s_{r}\) and \(d_{r}\) respectively represent the source node and destination node of the service in the network topology. The number of secret keys required by the service in unit time is represented by \(M_{r}\). \(t_{a}\) represents the arrival time of service, and \(t_{r}\) is the duration of the service. \(P_{v,w}\) represents the working path selected by the scheme, while \(P_{x,y}\) is the selected protection path. The detailed parameter definitions are all shown in Table 1.  
Topology-Abstraction-Based Protection Scheme in Quantum Key Distribution Networks with Partially Trusted Relay  
The overall steps of the topology-abstraction-based protection scheme in the QKDN with the partially trusted relay (TAPS-PTR) algorithm are as shown in Algorithm 1. First, the topology abstraction of the QKDN with partially trusted relay is introduced. Second, if the service arrives, the working path resources of the service are allocated. Lastly, protection path resources are allocated, and the key channel link resource status is updated. When the service leaves, the key resources are released. The specific process of the algorithm is as follows. In the process of network topology abstraction, there are four steps. Firstly, the strategy needs to traverse each node of the network topology for initialization and record the physical location of each trusted node and untrusted node in the topology. In particular, untrusted nodes need to be distinguished from trusted nodes. Secondly, the links that are untrusted nodes on both sides needs to be unconnected. The reason is that the service cannot be transmitted between two untrusted nodes. Thirdly, a virtual direct link is formed between two trusted nodes with an untrusted node in the middle. They are trusted node pairs that can transfer key services to each other. The untrusted node should also be abstracted. Finally, each untrusted node forms a corresponding virtual direct link group, and their resources are shared within the group. After that, network topology abstraction is completed.
## 3 Results and Discussions
### Lowering Velocity Effect  
To analyze the lowering velocity effect, a series of simulations are conducted. In these simulations, the system is configured with the parameters listed in Table 1. The payload is lowered, with a payout speed ranging from 0.1-0.9 m/s, while the top of the cable is imposed with a sinusoidal excitation (\(H_{s}\) = 0.3 m, \(T_{s}\) = 9 s). Envelopes of the traction at the top of the cable, in the cases with payout speeds of 0.1, 0.3, and 0.7 m/s, are plotted in Figure 7. It is shown that the maximum top traction during the  
Figure 5: Comparison of the traction under the sinusoidal crane tip excitation (\(H_{s}\) = 0.3 m, \(T_{s}\) = 9 s) with a payout speed of 0.2 m/s at 1500 m depth: (**a**) at the top of the cable, (**b**) on the equipment.  
Figure 6: Traction envelopes under the sinusoidal crane tip excitation (\(H_{s}\) = 0.3 m, \(T_{s}\) = 9 s) with a payout speed of 0.2 m/s: (**a**) at the top of the cable, (**b**) on the equipment.  
lowering process decreases with the lowering velocity, and that the resonance depth changes with the lowering velocity.  
As pointed out by [PERSON] et al. [17], the top traction consists of the static force and the dynamic force. The static force is considered to be the force acting on the cable when it is lowered, with the vessel remaining stationary. It is determined by the payout cable length and the lowering velocity. It also increases in a quadratic way with the lowering velocity. By subtracting the static force, the top traction is transformed into the dynamic force. The amplitude of the dynamic force is plotted against the lowered depth for different lowering velocities in Figure 8. It can be seen that the depth of the peak dynamic force decreases with the lowering velocity. However, the peak value tends to first decrease and then increase with the lowering velocity.
# Crowdsourcing Framework for QoE-Aware SD-WAN +
###### Abstract
We study how dynamic changes in the network could affect the performance of different streaming protocols, and how the streaming protocol adjusts to network changes and, consequently, the perceived QoE of the streaming content. The main protocols used in our model are RTP, RTP over TCP, SCTP, and UDP. The proposed framework is based on real-time alerts of quality degradation from the user side during live video streaming over a cloud-based SD-WAN environment. A QoE-rating application is deployed on the user devices to collect user feedback during a real-time streaming session. Through this application (which can be deployed as a plugin on web browsers or multimedia players), users send individual or cumulative feedback to the SD-WAN controller (directly or indirectly through a data summarization gateway) to inform the controller about potential problems in the video streaming. This QoE feedback would enable the SD-WAN controller to investigate problems in the service path and to take corrective action by making changes to the topology, resources, or routes of the content delivery network. The main feature of our proposed framework is the ability to identify changes in the QoE of video streams with dynamically changing network conditions in real time.
## 2 Background and Related Works
### Fourth Industrial Revolution  
The information and communication technologies (ICTs) that emerged in the 1960s and 1970s have become, over time and space, the material, digital, and technological basis and basic infrastructure of Industry 4.0 (I4.0). This digital technology is based on computers and the Internet [10]. This phase is considered the third industrial-technological revolution. The material and conceptual basis for the first revolution was the steam engine. In the second revolution, it was the combustion engine and electricity. Industry 4.0, which emerged in Germany between 2011 and 2015, is based on the three previous revolutions, i.e., machines, automation, systematization, and data processing. Specifically, I4.0 integrates the elements of the physical context with those of cyberspace [11]. However, it is still in the process of development and is still manifesting itself as a paradigm for the productive processes of the economy, companies, and countries [12].  
The literature on this subject considers the most relevant I4.0 technologies to be the Internet of Things (IoT), cyber-physical systems (CPS), big data (BD), radio frequencies (RFID), human-machine interface (HMI), manufacturing execution system (MES), augmented reality (AV), virtual reality (VR) and simulation, collaborative robotics, artificial intelligence (AI), cloud computing (CC), collaborative platforms (CP), digital twin (DT), 3D virtual environments, 3D additive manufacturing (AM), cybersecurity, wireless sensor networks (WSNs), and drones [13; 14; 15; 16].  
The application of I4.0 through virtual technologies such as simulation and VR has allowed to explore this field complex applications of automation and information that can be integrated into companies. In [17], an operator training system was proposed based on I4.0 and VR, whilst in [18], an I4.0 laboratory was designed based on virtual systems and computer simulation, giving an approximation to the operation of the intelligent factory through virtual technologies and a prototype. Moreover, in [19], the authors present the technological aspects of VR to help VR developers create industrial environments based on I4.0 to simulate real machine and process behaviors by means of computer simulation.
## 2 Materials and Testing Procedure
### Testing Procedure  
After 28 days of curing in standard curing chamber, the dumbbell-shaped specimens with 330 mm length and cross-sectional dimensions in the narrowed middle region of 30 by 30 mm were used to investigate the effect of steel reinforcing bar and steel fibers on the direct tensile and self-sensing performances of PE-SHCCs. As shown in Figure 2, direct tensile test was performed in accordance with the recommendations for design and construction of HPFRCCs with multiple fine cracks by the Japan Society of Civil Engineers (JSCE) [28]. Each dumbbell-shaped specimen was aligned in the tensile loading rig and clamped at both ends using wedge-type mechanical grips. Two strain displacement transducers (SDTs) were mounted at the opposing sides of the monitoring portion to monitor the tensile strain and cracks within the narrowed middle region.  
In addition to stress and strain measurement during the tensile loading process, the electrical resistivity of the PE-SHCC specimen was measured in real-time by a four-probe alternating current (AC) method using a digital LCR meter (Keysight Technologies E4980 AL). The electric current (I) was given to the specimen by using two outer contacts and the two inner contacts were used to measure the voltage difference (V). The setup is shown in Figures 1b and 2.  
The bulk resistivity (\(\rho\)) can be obtained from the following relationship:  
\[\rho=\frac{V\times A}{I\times L}=\frac{R\times A}{L} \tag{1}\]  
where \(V\) is the applied AC voltage; \(I\) means the electrical current value in ampere; \(R\) means the measured electrical resistance in ohm; \(A\) indicates the cross-sectional area of the narrowed middle region of the tensile specimen (0.03 \(\times\) 0.03 m); \(L\) denotes the distance between voltage measurement electrodes (0.19 m)  
Figure 2: Experimental setup for uniaxial tensile testing and resistivity measurement of dumbbell-shaped specimen.  
Sensing performance of cement-based sensors is generally assessed by the fractional change in resistivity (FCR) during loading process. The FCR can be represented as:  
\[\text{FCR}=\frac{\Delta\rho}{\rho_{0}}=\frac{(\rho_{t}-\rho_{0})}{\rho_{0}} \approx\frac{\Delta R}{R_{0}} \tag{2}\]  
The sensing sensitivity can be evaluated by gauge factor (GF), which is defined as FCR per unite strain (\(\varepsilon\)). The GF is calculated through the following relationship:  
\[\text{GF}=\frac{FCR}{\varepsilon}=\left(\frac{\Delta\rho}{\rho_{0}}\right)/\varepsilon \tag{3}\]
## 3 Results
### Limitations and Future Works  
Although GBNSS performs well in biological network similarity searches, several areas require improvement.  
Firstly, the use of one-hot encoding for topological features poses scalability challenges. As the dataset size grows, the dimensionality of these vectors increases, leading to higher memory usage and lower computational efficiency. To address this, we plan to explore more efficient feature representations. In future work, we aim to incorporate intrinsic topological features such as the number of neighbors, in-degree, out-degree, and betweenness centrality to represent nodes more effectively.  
Secondly, GBNSS performed well in predicting accuracy for topological similarity queries and biochemical similarity queries of biological networks. There are too many similarity metrics used by our model to determine the similarity of two biological networks, which leads to the complicated training process of the model. To simplify our model and methodology, we plan to design a metric that can represent the biochemical similarity and topological similarity of biological networks in our future work.  
Thirdly, GBNSS focuses on completed biological networks to ensure the biological relevance and reliability of the predictions. We will carry out future studies on incomplete biological networks.  
Finally, although GBNSS predicts similarity scores to assess network similarity, these predictions have not yet been experimentally validated. A thorough evaluation and analysis will be essential to confirm the reliability and robustness of the model. Future work will involve comprehensive experiments to verify the accuracy and effectiveness of the predicted similarity scores.
# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 2 Conventional Approaches
### Supervised Methods
#### 2.2.1 Random Forest (RF) and k-Nearest Neighbors (KNN)  
Although RF and KNN algorithms can be used for both supervised and unsupervised statistical analyses, here, we deal with the supervised aspects.  
Random forest, as the name itself suggests, is composed by a proper number of decision trees working as an ensemble but individually depict a class from which the most representative corresponds to the model's prediction. Therefore, the idea behind the random forest algorithm is to correct the error obtained in one selection tree by using the predictions of many independent trees and by using the average value predicted by all these trees [58]. RF can deal with categorical features by treating both high dimensional spaces and a large number of training examples. In detail, the first step in a RF scheme is to create a selection tree; then, by using the observations \(\{Y_{j},X_{j}\}_{1<j<k}\), where \(X_{j}\) is usually a vector and \(Y_{j}\) is a real number, different sets can be obtained using different splitting criteria which operate on the considered vectors. Each criterion allows the initial subset to be divided into two subsets. An example of the selection tree is shown in Figure 7:  
Given an observation \(X_{i}\), and known selection tree, one determines in which final node the vector \(X_{i}\) is classified in order to predict \(Y\).  
Figure 6: ROC curves and corresponding AUC values for three classifiers: no predicting power (red dashed line with AUC = 0.5), perfect classifier (green dotted line with AUC = 1) and some predictive power (blue solid line with AUC\(\sim\)0.8).  
Figure 7: Example of decision tree with a different action corresponding to a different conditions set.  
Instead, the k-nearest neighbors (KNN) algorithm considers that similar outcomes lie near each other. Given again an observation \(X_{i}\) and with the aim to predict \(Y\), the KNN algorithm selects the k-nearest observations of \(X_{i}\) in \(\{Y_{j},X_{j}\}_{1<j<K}\). Let \(i_{1},...,i_{k}\) be the \(k\) values which provide the \(k\) minimum values of the function: g(j) = d(\(X_{j}\) - \(X_{i}\)). These minimum values can be equal if there are multiple values of \(X_{j}\) at the same distance from \(X_{i}\)[59]. There are at least the three possibilities for the distance (Euclidean, Manhattan and Minkowski). So, the value predicted for \(Y_{i}\) is the mean value of the \(k\) values Y\({}_{j}\) for the \(k\) nearest neighbors of \(X_{i}\):  
\[\hat{Y}_{i}=\frac{1}{k}\sum_{1}^{k}Y_{ik} \tag{3}\]
# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
## 3 Literature Review of Related Works  
Datasets play a crucial role in intrusion detection to test, compare, and evaluate IDSs. They are also essential in statistical approaches, ML, artificial intelligence (AI), genetic algorithms, and data mining, which aim to identify the relationships between instances in an aggregated dataset. For instance, many researchers have created and suggested various HTTP datasets to aid in the compression, evaluation, and testing of detection systems when dealing with HTTP-based attacks.  
HTTP-GET datasets can be classified as network-traffic-based or log-based datasets. But, most of them were collected from conventional network environments. Publicly available HTTP-GET log-based datasets from actual CCEs are rare. As a result, researchers must rely on publicly benchmarked datasets such as DARPA, NSL-KDD, and KDD to evaluate the effectiveness of HTTP-GET flood DDoS attack-detection systems. However, many realistic datasets lack the necessary features to construct an accurate IDS approach, such as confidential and personal data that are not publicly available. Moreover, current datasets may not represent contemporary technology.  
Researchers use one or more datasets depending on the solution's demands to evaluate a detection system's ability to differentiate normal and abnormal requests. However, most of the current datasets can only evaluate and test conventional environment solutions because they do not cover the characteristics of CCEs. Additionally, most solutions that depend on network-traffic-based datasets cannot detect the HTTP flood DDoS attack traces buried inside the WS logs.  
CCE security researchers often face a major challenge when securing the application layer: the lack of HTTP-GET flood DDoS attack datasets. This scarcity forces them to create custom HTTP flood DDoS attack datasets for research purposes. However, these self-generated custom datasets often fall short of benchmark datasets regarding accuracy, reliability, and validity, limiting their usefulness for scholarly endeavors. Based on the datasets used in the previous works, we present the related existing works as the following categories: