esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Resveratrol and Free Radicals
### Resveratrol and PTEN in the PI3K/Akt/mTOR Signaling Pathway  
The PI3K/Akt/mTOR signaling pathway is a crucial intracellular pathway in cell cycle regulation directly related to cell proliferation and apoptosis, as well as to diseases such as cancer. It also plays a critical role in breast cancer, where growth factors and hormones, such as the HER and estrogen, respectively, activate this signaling cascade [105]. Indeed, upon binding of the growth factor, the associated receptor tyrosine kinase (RTK) activates the PI3K heterodimer [106]. This kinase promotes the phosphorylation of several substrates, including phosphatidylinositol-4,5-bisphosphate (PIP2), which is then converted into phosphatidylinositol-3,4,5 triphosphate (PIP3). The PIP3 activates Akt, which, in turn, can phosphorylate numerous molecules, producing as a final effect the induction of cell growth and resistance to apoptosis [107]. The phosphatase and tensin homolog protein (PTEN) is a phosphatase capable of dephosphorylation PIP3 and acting as a tumor suppressor, negatively modulating the PI3K/Akt signaling pathway [108].  
The mTOR is a serine/threonine protein kinase located downstream of PI3K and Akt. Two distinct protein complexes, mTOR1 and mTOR2, coexist: mTOR1 is activated by Akt, while mTOR2 directly activates Akt by phosphorylation. Both complexes promote cell growth and proliferation [109]. The involvement of the PI3K/Akt/mTOR signaling pathway in tumor progression has been known for many years; therefore, it is not surprising that it represents a major therapeutic target, also in breast cancer, through the development and testing of PI3K and mTOR inhibitors [110].  
Resveratrol also targets this pathway. In fact, it regulates the expression of antioxidant enzymes through mechanisms involving the PI3K/Akt signaling pathway, increasing the levels of PTEN, which antagonizes the activity of PI3K and, consequently, its ability to phosphorylate protein substrates, including Akt [69]. Unexpectedly, the increase in PTEN proved to be associated with increased mRNA levels related to antioxidant enzymes such as CAT, GPx, and MnSOD, promoting their activity and decreasing the concentration of free radicals, particularly hydrogen peroxide [70].  
Furthermore, resveratrol demonstrates an inhibitory effect on the activation of the mTOR/p7056K pathway, likely by suppressing Akt signaling [71], and on the TOR/ULK1 pathway, thus leading to autophagy in breast cancer cells [72].
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 1 Introduction
Experimental results show that the proposed Sinc-SEGAN overtakes a set of competitive baseline models, especially on higher-level perceptual quality and speech intelligibility. Additionally, the system parameters reduce drastically; up to merely 17.7% of the baseline system. In addition, data augmentation techniques further boost the system performance across all classic objective evaluation metrics of speech enhancement. Analysis of the Sinc filters shows that the learned filters are tuned precisely to capture critical speech clues. Experimental results demonstrate the potential applications of the proposed system, e.g., portable devices, hearing aid design, and cochlear implants. Notably, the proposed Sinc-SEGAN system is generic enough to be applied to existing GAN models for speech enhancement to further improve performance.
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 4 Discussion
In our study, we used the well-established Arrhenius damage integral [44], however, we could also consider other models of thermal damage [45]. Further, the model could be applied to study hair removal while taking into account the recently reported \"avalanche-type\" enhancement of hair temperature increase under repetitive pulse exposures [46]. Our ultimate goal is that the developed FEM model of laser beam-tissue interaction would also prove useful in a clinical setting by aiding the optimal selection of treatment parameters during hair-removal procedures. Using the novelhyperspectral imaging modality [47] could aid in, e.g., collecting information about the quantity of melanin and estimating epidermal thickness, which would in turn be helpful for selecting optimal laser treatment parameters. In this study, we focused on the Nd:YAG laser, which is known for its ability to treat darker skin tones since the longer wavelength bypasses the melanin in the skin. However, our approach could also be applied to evaluate the effects of other hair removal laser wavelengths and the effects of multiple wavelengths present during IPL treatments. The IPL (Intense Pulsed Light) uses broad-spectrum light to target and remove unwanted hair and is particularly efficient in removing dark hair on fair to medium skin tones. The method is fast and inexpensive, but the major drawback is that it may lead to pigmentation of the darker skin [48]. In spite of our focusing on the Nd:YAG wavelength only, we believe that the major conclusions of our study related to the dependence of the efficacy of the hair removal on the hair density and the angle of the hair shaft apply as well to other light-based hair removal methods. Conceptualization, M.M., M.L. and U.S.; methodology, Z.K., U.S. and M.M. ; software, Z.K. and U.S.; writing--original draft preparation, R.H. and Z.K. ; writing--review and editing, R.H., M.L., M.M., Z.K. and U.S.; visualization, Z.K. and B.T.M. ; supervision, M.M. and U.S.; funding acquisition, M.M. and M.L. All authors have read and agreed to the published version of the manuscript. The authors would like to thank to the Slovenian Research Agency for their financial support (research core funding No. P1-0389). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data that support the findings of this study are available from the corresponding author upon reasonable request. M.L. and B.T.M. are affiliated with the company Fotona, d.o.o. ; the remaining authors declare no conflicts of interest. Appendix A Protocol for In Vivo Measurement of Absorption Coefficients of Human Hair and the Epidermal Layer
## 9 Porosity Evaluation and Mathematical Model Enhancement
### Porosity Evaluation in Composite Specimens--Destructive Test
#### 9.1.2 Results Analysis  
The evaluation of the specimens' porosity percentage revealed that the pores' volume average was 1.2% for specimens originating from part A and 2.3% for specimens originating from part B. This is due to the imprisonment of air bubbles at the cylinder tube specimen's lower part (part B) during the curing process. It is then worth noting that the porosity evaluation revealed a limitation on the feasible molding thickness where the process must be revised for parts with high thickness. Table 2 displays the obtained results, where \(PTF-T\) represents the theoretical results according to Equation (24), and column \(PTF-C\) represents the results according to the corrected equation resulting from calculated pressure Equation (33).  
It can be noticed that \(PTF-T\) is always higher than \(PTF-E\). This is because the mathematical model did not account for multiple factors including machining defects, surface finishes, temperature effect, and others. It also can be noticed that \(PTF-C\) is closer to the experimental value than \(PTF-T\). This is due to the correction factor \"\(k\)\" introduced to Equation (32). The implementation of Equation (32) in Equation (33) reduces the amount of pressure generated in the interference fit area, hence the allowable \(PTF\) value. Specimens T1-T11 were taken from part A and specimens T12-T15 from part B. Knowing that the average porosity percentage is 1.2% for specimens T1-T11, the correction factor is \(k=0.988\). For specimens T12-T15, the average porosity percentage is 2.3%; therefore, the correction  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \# & **Intermediate Material** & \(2R_{2}\) (mm) & \(2R_{2}\) (mm) & \(2R_{01}\) (mm) & \(l\) & \(PTF-E\) & \(PTF-T\) & \(PTF-C\) \\  & & & & & (mm) & (N) & (N) & (N) \\ \hline T1 & Steel & 8.1 & 14 & 8.02 & 5 & 717 & 884.8 & 881.2 \\ T2 & Steel & 16.17 & 24.1 & 16 & 5 & 1475 & 1482.5 & 1476.3 \\ T3 & Steel & 16.17 & 24 & 16 & 5 & 1437 & 1475.2 & 1469 \\ T4 & Steel & 16.18 & 24 & 15.99 & 5 & 1482 & 1566.5 & 1559.9 \\ T5 & Steel & 12.07 & 20 & 12 & 5 & 604 & 727 & 723 \\ T6 & Steel & 12.06 & 20 & 12 & 5 & 593 & 623.1 & 620 \\ T7 & Steel & 12.2 & 20 & 12.05 & 5 & 1305 & 1548.4 & 1542 \\ T8 & Brass & 8.1 & 14.07 & 8 & 5 & 1100 & 1110.8 & 1099.2 \\ T9 & Brass & 7.99 & 14.07 & 8.15 & 5 & 1130 & 1223.7 & 1211 \\ T10 & Aluminum & 8.14 & 14.08 & 8.06 & 5 & 741 & 869.1 & 865.7 \\ \(T11\) & Aluminum & 8.1 & 14.02 & 8.06 & 5 & 1128 & 1313 & 1299.5 \\ \(T12\) & Steel & 16.15 & 22.14 & 16 & 5 & 911 & 1083.7 & 1060.9 \\ \(T13\) & Steel & 16.15 & 22.12 & 16.03 & 5 & 806 & 860.6 & 842.5 \\ T14 & Steel & 16.22 & 24.05 & 16.1 & 5 & 953 & 1033.6 & 1012.2 \\ \(T15\) & Steel & 12.1 & 18 & 12.04 & 5 & 416 & 517.6 & 507 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(PTF\) test results.  
factor is \(k=0.977\). The application of the correction factors resulted in decreases between the experimental data and theoretical value of almost 0.5% (T1-T11) and 1.8% (T12-T15).
# An Abstract Framework for Non-Cooperative Multi-Agent Planning
## 4 Instantiated Framework
### Goal Allocation  
In this paper, we assume _goal allocation_ as the distribution of a set of goals among the different agents of the system. Depending on the type of problems, it might not be necessary to carry out this phase if there was already a previous distribution of the goals among the agents for any reason. However, when there is no pre-established distribution, and considering that the agents are self-interested, it is necessary to provide the system with a mechanism that allows goal allocation in a strategic way, that is, taking into account the preferences of the agents and their possible utility. We propose to use auctions or similar game-theoretic models for the goal allocation process.  
An auction is a mechanism by which a set of goods is distributed to the highest bidder. There is a line of research in economics devoted to _auction theory_ where the way in which people act in auctions and the properties of auctions are studied [58; 59]. Auctions are regulated by a set of rules, and auction theorists seek to improve the efficiency of auctions as well as to promote optimal and equilibrium bidding strategies.  
Figure 3: Diagram of the instantiated framework.  
The goal allocation in the framework can be done with some system inspired by that of the works [32; 36]. In both works, auction mechanisms are used to distribute the goals among the agents but trying to satisfy their private interests. However, we would have to consider a modification with respect to the work of [32] since it uses the sum of the utilities of the agents, that is, the utilitarian social welfare, whereas, in our framework of non-cooperative MAP, we do not seek to satisfy a global measure, but individual, which would be more similar to an equilibrium. Another interesting work that could inspire the goal allocation needed in our framework is the one presented in [60], which uses consensus-based auction algorithms for task allocation of autonomous vehicles fleets. However, an adaptation would be also needed since that work is intended to be applied in task allocation, which is slightly different from our target of goal allocation.  
In the instantiation that we propose for the abstract framework, whose diagram is depicted in Figure 3, the goal allocation phase is performed with an auction in which the agents bid for each of the goals to solve until having all goals allocated. Then, the strategic planning phase can start.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase  
The results of the proposed FRA6\(\sigma\) methodology are presented through three case studies, each evaluating transformer health across distinct frequency bands. By comparing the latest FRA measurements against the baseline fingerprint, statistical control limits and process capability indices (\(C_{p}\), \(C_{pk}\)) provide a structured diagnosis of mechanical and electrical stability, enabling early fault detection before physical failure manifests.
# Efficient and Sustainable Cleaning: A Comparative Analysis of Cryogenic Technology
## 4 Discussion
### Future Trends and Developments
#### 4.1.1 Innovation in Energy Efficiency  
Dry ice blasting offers substantial benefits, although significant challenges remain. The energy consumption of the process, associated with the costly production of the blasting material and the low efficiency of compressed air usage, stands out as a significant barrier [61]. This technology requires a source of compressed air to supply the necessary kinetic energy to the dry ice pellets. The quality and quantity of this kinetic energy, derived from the mixture of air and pellets, is a factor that influences the efficiency of the cleaning process. Producing sufficient compressed air and pellet extrusion to the required specifications is a highly energy-demanding process [62]. In addition, the availability of centralised compressed air distributions with these characteristics is not common in industrial facilities [23].  
There are some initiatives that lead the way towards greater sustainability and cost reduction. In 2009, a patent was published for the acceleration of dry ice particles through a centrifugal wheel independent of compressed air [25], while other authors propose cost-saving measures and efficient use of compressors to reduce energy consumption by up to 87% in specific industrial applications [23]. Addressing these challenges requires a holistic approach involving technical innovations, industrial collaboration, and the implementation of effective energy-saving measures [12].
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges
### Regulatory and Standardization Gaps  
In the clinical microbiology domain, MALDI-TOF MS has received widespread regulatory acceptance. However, its application in food microbiology is still evolving. Regulatory agencies, including the EFSA and the U.S. FDA, have yet to issue comprehensive guidance or standard protocols for the use of MALDI-TOF MS in food pathogen detection. As a result, validation and acceptance of results in official food control contexts can vary by jurisdiction [11; 12; 28; 144; 145].  
The standardization of workflows, databases, quality assurance protocols, and interpretation criteria will be critical for wider regulatory adoption and international harmonization.
## 2 Predictive Models and Methods  
Predictive models, the focus of this article, are useful when the goal of the data analysis is to accurately predict _future_ occurrences. Predictive modeling projects use familiar statistical _methods_--regression, logistic regression, clustering, and the like--yet most academic work uses those methods for explanatory or descriptive, not predictive, purposes. The distinction is subtle yet important. [PERSON] and her colleague [29, 30] provide thoughtful overviews of the differences between descriptive, explanatory, and predictive statistical models. Unlike explanatory models, predictive models are mostly unconcerned with theory building, hypothesis testing, and confidence intervals. Instead, the focus is finding models that predict future behavior as accurately as possible. In that regard, predictive modelers might shamelessly exclude statistically significant explanatory variables, or include statistically insignificant ones, if doing so improves prediction accuracy. As another example, explanatory models often seek statistical _aggregates_ (e.g., regression coefficients) while predictive models focus on the _individual_.  
Because of differences in perspective, predictive modeling exercises often unfold quite differently from descriptive or explanatory work. For example:  
* Traditional statistical inference is a secondary focus of predictive models so that diagnostics and remedies that speak to the quality of inferential evidence (e.g., multicollinearity) are of secondary importance in prediction settings; inference tends to be front-and-center in most explanatory research.
* Large numbers of observations are welcomed in predictive modeling settings, since they accommodate the use of cross-validation (holdout samples) for model selection. Large sample sizes, on the other hand, can detract from the interpretability of inferential outcomes from explanatory models since hypotheses are routinely rejected in large enough samples [31, 32].
* Explanatory research, which typically focuses on aggregates, handles outliers by minimizing their impact via exclusion or outlier-resistant methods. Outliers are of special interest in predictive models, since they are often the ones we want to find: the fraudulent credit card charge or the struggling student.
* Overfitting is a known risk with predictive modeling. Models that are overfit produce good in-sample predictions but inadequate out-of-sample predictions; _i.e._, they perform worse than expected once we put them into practice. To mitigate overfitting, predictive modelers use methods such as cross-validation that are not often seen in explanatory work.  
Analysts with experience with predictive models are familiar with these and other methods, models, and materials relevant to predictive analytics projects [33, 34, 35].
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 5 Security and Privacy of UAVs  
In this section, we go through the safety, security, and privacy concerns related to the use of drones. We specifically look at these systems' weaknesses to potential attacks that could lead to a malicious attack or drone crash, and we assess the security needs of such systems. The following specific privacy and security conditions must be satisfied for a secure safe flight operation:  
* _Authorization_: Only authorized operators should be given access to the UAV system's resources, including the ground control station and the aircraft. During communication, an ongoing authentication process between the operator and the UAV is necessary.
* _Availability_: All components of the UAS should be assured to fulfill their respective activities under defined geographical and temporal conditions, ensuring that the system's availability is maintained throughout the operational period. It is also critical to manage the repair and update activities in a way that does not compromise the UAV system's availability when it is in use.
* _Integrity_: The UAS should be designed to verify that the telemetric data, GPS, and serial communications are authentic and have not been tampered with intentionally or inadvertently.  
Table 4 presents state-of-the-art general drone security solutions and mechanisms used available in the literature. Furthermore, it analyzes security threats, targeted zones (Drones, Communication Networks, Base Stations, Ground Control Stations and Certification Authority), security considerations, parameters used, and open issues.
## 4 Software Tools for Developing XR Applications in HRI
### Communication Tools
#### 4.1.1 Robotic Middleware and Bridging Mechanism  
Communicating with game engines and external robotic modules often requires inter-process communication using socket connections. However, managing socket connections can become a cumbersome task in complex software architectures. Therefore, using robotic middleware is often recommended in order to reduce the management complexity and improve the reusability of complex robotic software architectures. We observed that the Robot Operating System (ROS) version 1.0 was the most popular middleware solution used by the authors of the reviewed articles. However, the connection between ROS1 modules and virtual environments developed in Unity and Unreal cannot be made directly due to differences in dependencies, programming languages, communication protocols, and/or operating systems. In order to deal with this issue, bridging mechanisms or modules are often used. These modules generally translate and transfer data from a game engine to a non-compatible robotic middleware that uses another message formatting or communication protocol. In the case of ROS, the most popular solution enabling the connection of non-ROS programs to an ROS network is rosbridge_suite [36], which provides a solution based on JavaScript object notation (JSON) and Websockets. However, ref. [37] reported performance limitations of rosbridge_suite when transferring large sensory messages (e.g., images and point cloud data), which specifically affects transfer speed. In this direction, ROS# [38] has been widely used by the reviewed articles for communicating ROS with Unity. This framework provides an ROS Bridge Client for C# applications, a Unified Robot Description Format (URDF) file parser that enables developers to import robot models to Unity, and a message generation tool for sending and receiving ROS messages from Unity. To overcome the performance limitations of rosbridge_suite, alternatives based on TCP/IP sockets instead of Websockets, such as [37], have been proposed. More recently, the TCP connector [39] framework for Unity has been released by the developers of ROS# (sharing many of the same tools) as an official Unity asset. However, none of the reviewed articles has reported the integration of this framework. Another alternative explored by [40; 41] proposes alternatives for connecting Unity with external robotic systems using a middleware solution based on ZeroMQ [42]. Unlike conventional sockets, which present a synchronous interface, ZeroMQ sockets are asynchronous. Moreover, as proven in [40; 41], tools based on ZeroMQ can be used to overcome the communication performance limitations of rosbridge_suite. Most recently, ros2-for-unit ([[https://github.com/RobotecAI/ros2-for-unity](https://github.com/RobotecAI/ros2-for-unity)]([https://github.com/RobotecAI/ros2-for-unity](https://github.com/RobotecAI/ros2-for-unity)) [access date: 12 December 2022]) has been proposed as an unbridged alternative to connect ROS2 and Unity. The authors of this tool state that it provides considerably lower latency than solutions using rosbridge_suite.
## 4 Proposed Methodology
### SAL Task Planning Ability Predicted Based on the Tree-LSTM Model
\[i_{m}=\sigma\Big{(}W^{(i)}x_{m}+U^{(i)}h_{m}+b^{(i)}\Big{)} \tag{4}\]  
\[f_{m,k}=\sigma\Big{(}W^{(f)}x_{m}+U^{(f)}h_{m}+b^{(f)}\Big{)} \tag{5}\]  
\[o_{m}=\sigma\Big{(}W^{(o)}x_{m}+U^{(o)}h_{m}+b^{(o)}\Big{)} \tag{6}\]  
\[u_{m}=\tan\text{h}\Big{(}W^{(u)}x_{m}+U^{(u)}h_{m}+b^{(u)}\Big{)} \tag{7}\]  
\[c_{m}=i_{m}\circ u_{m}+\sum_{k\in S(m)}f_{m,k}\circ c_{k} \tag{8}\]  
\[h_{m}=o_{m}\circ\tan\text{h}(c_{m}) \tag{9}\]  
where \(\sigma(\cdot)\) denotes the logistic sigmoid function, and \(\circ\) denote the element-wise multiplication. In the Tree-LSTM model, the state of the composite nodes is derived from the states of the nodes, as illustrated in Figure 4.  
The inclusion of these additional gates and the unique updating mechanism enables the Tree-LSTM model to effectively capture and analyze the intricacies of hierarchical data, making it a powerful tool for modeling the dynamic nature of learning tasks. This advanced functionality positions the Tree-LSTM as an ideal framework for tasks that require an understanding of nested or sequential dependencies, such as predicting a learner's ability to plan and execute complex learning tasks.