esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effects of Vitamin D\({}_{3}\) on Dental MSCs
### Vitamin D\({}_{3}\) and Antimicrobial Activity of Dental MSCs  
Vitamin D\({}_{3}\) is a well-known activator of antimicrobial peptides' (AMPs) production by different cells [86]. In the oral cavity, antimicrobial peptides are produced mainly by oral epithelial cells, and their production is regulated by the oral microbiota [87]. Different vitamin D\({}_{3}\) metabolites are potent inducers of AMPs' production and antibacterial activity of oral epithelial cells [80; 88; 89; 90; 91; 92]. MSCs of different tissues also produce various AMPs, but this ability seems to be somewhat limited compared to epithelial and immune cells [93]. Only a few studies reported the expression and production of AMPs in hGFs and hPDLCs [47; 80; 94]. The effect of vitamin D\({}_{3}\) on the AMPs' production by dental MSCs was investigated by two studies. [PERSON] et al. showed that 1,25(OH)\({}_{2}\)D\({}_{3}\) enhances both basal and _P. gingivalis_-induced production of human beta-defensin 3 by hPDLCs [80]. Moreover, 1,25(OH)\({}_{2}\)D\({}_{3}\)-treated hPDLCs inhibit the growth of _P. gingivalis_, as well as its adhesion to a Matrigel-coated polystyrene surface [80]. [PERSON] et al. demonstrated that both 25(OH)D\({}_{3}\) and 1,25(OH)\({}_{2}\)D\({}_{3}\) stimulate the production of LL-37 by hGFs and hPDLCs [47].
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 4 Analysis of Parameters Affecting Pipeline Response under Fault Action
### The Influence of Pipeline Internal Pressure
Furthermore, with the increase in the internal pressure, the pipe will appear more and more early convex, the pipe's convex degree will eventually be larger and larger. This behavior reflects that the existence of internal pressure in the pipeline can enhance the pipeline's deformation resistance ability. However, it should also be noted that an excessive internal pressure may make the pipeline more prone to damage under the action of fault displacement. Therefore, an appropriate operating internal pressure should be applied to pipelines according to actual requirements, in order to ensure transportation efficiency and fully maximize the pipe performance.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Security Attributes of ECSSP-SC
#### 6.1.1 Confidentiality  
This attribute ensures that the message cannot be understood by the _Attacker_\(A\). In the mutual authentication step of the proposed protocol, two messages, \(\{c,T,w,T_{U}\}\) and \(\{A_{S}.T_{S}\}\), are transmitted by the \(USR\) and \(SVR\), respectively. In the first message \(\{c,T,w,T_{U}\}\), an _Attacker_\(A\) cannot break the ciphertext \(c\) as per Assumption 3. According to Assumption 4, the _Attacker_\(A\) cannot deduce \(r\) from \(T\) since \(T=rG\). Moreover, an attacker would fail to deduce any secret form \(w\), since it is the division of \(v\) and \(r\). In the second message \(\{A_{S}.T_{S}\}\), according to the Assumption 3, \(A_{S}\) cannot be broken by the _Adversary_\(A\) and the key \(K^{\prime}\) cannot be obtained. The second and the last quantity is the timestamp \(T_{S}\). It may be concluded that from both the messages \(\{c,T,w,T_{U}\}\) and \(\{A_{S}.T_{S}\}\), no secret can be revealed by an _Attacker_\(A\), and therefore, the proposed ECSSP-SC satisfies the confidentiality property.
## 4 Integrated Design Methodology of \"Rood Cutting and Pressure Relief + Floor Grouting with Bolts\"
### Analysis of Results of Control Scheme
#### 4.2.2 Analysis of Scheme A (Floor Grouting with Bolts)  
This kind of scheme is analyzed and compared with the change in anchor support force, floor stress distribution, and floor heave volume under different floor lithology reinforcement coefficients. Figure 18 shows the analysis results of the A-type scheme.  
(1) Analysis of anchor support force curve under different coefficients of reinforcement of base-plate lithology: Under the conditions of 1.2 times, 1.4 times, and 1.6 times the reinforcement coefficients of base-plate lithology, the force of base-plate anchors shows an upward trend with an increase in lagging distance from the working face, and the force at the lagging distance of 200 m from the working face is 52 kN, 72 kN, and 91 kN, respectively, which are 38% and 75% higher compared to the anchor with the coefficients of 1.2 times of reinforcement, respectively. Compared with the 1.2 times reinforcement factor anchor, the force is increased by 38.46% and 75%, respectively.  
(2) Stress distribution of base plate with different reinforcement coefficients of base-plate lithology: Compared with the original measures in the field, the stress of the base plate with different reinforcement coefficients of base-plate lithology is basically unchanged.  
(3) Analysis of the change curve of floor heave under different coefficients of floor lithology strengthening: Under the conditions of the original measures on site and floor lithology strengthening coefficients of 1.2 times, 1.4 times, and 1.6 times, the amount of floor heave in the range of 86 m-223 m of lagging workforce shows a rapid growth trend, and in the range of 223 m-250 m of lagging workforce, the amount of floor heave tends to be stabilized. With a lagging workforce of 250 m, the amount of floor heave is 480 m, 480 m, and 480 m, respectively. At 250 m of lagging face, the amount of floor heave is 480 mm, 407 mm, 371 mm, and 321 mm respectively, and the maximum reduction in floor heave is 33.13% under the conditions that the coefficient of reinforcement of floor lithology is 1.2 times, 1.4 times, and 1.6 times compared with the original measures on site.  
(4) Comparison results of different base-plate lithology strengthening coefficients: Under the condition of an unchanged number of anchors, with an increase in the base-plate lithology strengthening coefficient, the stress of the base plate increases gradually; the force of the base-plate anchor support also increases greatly, with a maximum increase of 75%, and the amount of floor heave reduces significantly, with a maximum reduction of 33.13%.
## 5 Conclusions  
The study demonstrated the feasibility and acceptance of serious games for telerehabilitation in conflict settings. Among 186 children, 89% expressed willingness to continue use, and many reported improvements in mood, balance, and functional mobility. Although this feasibility study did not aim to quantitatively assess therapeutic efficacy, reported therapeutic gains--based on caregivers' and children's perceptions--included improved balance, enhanced walking, better mood, and increased engagement. These perceived improvements support the platform's relevance and usability in disrupted clinical contexts. The platform, specifically created to facilitate customized telerehabilitation, demonstrated its advantages through a 58% adherence rate and 89% of users expressing a desire to continue. These findings support the use of serious games as a reliable, enjoyable, and scalable tool for rehabilitation, particularly in conflict-affected regions where traditional services are disrupted.  
Ukraine is currently dealing with the significant consequences of the ongoing conflict. The knowledge and insights gained from handling rehabilitation difficulties during exceptional conditions can be valuable for other areas confronting similar situations. The study's suggestions and factors to consider, such as the requirement for lightweight and energy-efficient technology specifically designed for children with impairments, add to the current discussion on enhancing healthcare services in conflict-affected regions.  
Conceptualization, A.K. and B.B.; data curation, A.K.; formal analysis, B.B.; investigation, A.K. and O.K.; project administration, B.B.; resources, O.K. and B.B.; supervision, O.K. and B.B.; writing--original draft, A.K.; writing--review and editing, A.K., O.K., and B.B. All authors have read and agreed to the published version of the manuscript.  
The development of the platform was funded by the Horizon 2020 European projects AbleGames and GABLE. This study was supported by the Special Research Fund (BOF) of Hasselt University under the number BOF23 DCBL04.  
The use of the platform, including its testing and gathering of feedback, was approved by the local ethical commission in Truskavets, Ukraine (protocol code A150323, date of approval: 15 March 2023).  
**Informed Consent Statement:** Informed consent was obtained from all subjects and their legal representatives involved in the study.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, Prof. dr. [PERSON], upon reasonable request.  
**Acknowledgments:** The authors express their gratitude to all the families who were willing to participate in the study.  
**Conflicts of Interest:** The first author is a clinical consultant of the software development company who maintains the platform. The other authors declare no conflicts of interest.
## 3 The Working Scenario and Problems Statement  
### The Working Scenario  
The wireless sensor networks that we discuss here is intended to work in coal mines. A typical coal mine consists of several layers, as shown in Figure 1. The vertical well connects different layers together through mine tracks. The railroads paved on different layers lead the way to different pit faces where the mining operation proceeds. At certain locations of interest, different types of wireless sensor nodes are installed to gauge the humidity, temperature and gas density and to send these data back to a base station or a data center for future analysis. The data transmitting and receiving procedure will definitely consume a large portion of battery energy. Therefore, sooner or later, sensor nodes will encounter the problem of insufficient energy supply no matter what energy-saving techniques are adopted.  
In order to make sensor nodes work properly without the problem caused by insufficient energy supply, recharging locomotives with wireless energy transfer devices mounted on them will cyclically stop by each sensor node and recharge it with a certain amount of energy, such that the energy of each sensor node will not fall below a certain level before the next round recharging task.  
Figure 1: The sketch map of a coal mine.
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Research Goals
#### 7.1.3 Traffic Classification  
The last main top-level goal in NTA is traffic classification (about 40% of the studied papers). Whereas attack detection tends to use supervised machine learning and anomaly detection usually resorts to unsupervised algorithms, more varied options are developed for traffic classification. Here, the use of heuristics, schemes designed ad-hoc, and pre-knowledge not extracted by algorithms (i.e., known rules) are common. The frequent methodological weaknesses mentioned for the research coin the previous top-level goals also appear in traffic classification, yet this type of research is more heterogeneous, and therefore, finding common peculiarities is also more difficult.
# SEA and GATOR 10 Years Later
## 5 Function of the SEA and GATOR in Nutrient Sensing and Responding
### SEA/GATOR Recruitment to the vacuolar/Lysosomal Membrane
Cryo-EM structures of the human FLCN-FNIP-RAG-RAGULATOR complex containing an inactive form of the RAG heterodimer confirmed that the FLCN-FNIP2 heterodimer binds to the GTPase domains of both RAGA and RAGC [122; 123]. GATOR1-RAGA interactions are controlled by several kinases and E3 ubiquitin ligases, which are not present in lower eukaryotes. For example, an oncogenic non-receptor tyrosine kinase, SRC, disrupts GATOR1-Rags interactions, promoting mTOR1 recruitment and activation at the lysosomal surface [124]. Currently, it is not known what the mechanisms that activate SRC in response to amino acids are and whether GATOR1 subunits or RAGs can be phosphorylated by SRC. On the other hand, DEPDC5 can be phosphorylated by Pim1 kinase at S1002 and S1530, and by AKT also at S1530 [125]. This phosphorylation seems not to affect the ability of DEPDC5 to interact with neither NPRL2 nor SZT2, but elevated Pim1 expression during amino acid starvation overcame mTOR1 suppression. Two lysosome localized E3 ligases, RNF152 and SKP2, mediate K63-linked polyubiquitination of RAGA at different sites, which promote GATOR1 recruitment to RAGA and the consequent inactivation of mTOR1 [111; 116]. Remarkably, SKP2 ubiquitinates RAGA at K15 during prolonged amino acid stimulation [126], while, quite opposite, RNF152 ubiquitinates RAGA at a different set of lysines (K142, 220, 230, 244) during amino acid starvation [111]. SKP2 provides a negative feedback loop, where RAGA ubiquitination and GATOR1 recruitment restrict mTOR1 activation upon sustained amino acid stimulation. Inversely, during amino acid starvation, it is RNF152-dependent RAGA ubiquitination, which enhances GATOR1-RAGA interaction. Interestingly, RNF152 can also ubiquitin RTEB, sequestering RHEB in its inactive RHEB-GDP form and promoting its interaction with TSC2, which leads to mTOR1 inactivation [127]. Thus, RNF152 acts a negative mTOR1 regulator in both amino acid and growth factor brunches of mTOR1 signaling.
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 2 Computational Method
### Aerodynamic Model
#### 2.1.3 Coupling Algorithm  
The \"integrated vorticity approach\" [21] is utilized to transmit the vorticity from the CFD solution to the VWM. In this approach, the blade spanwise load distribution is first calculated using the Navier-Stokes equation; the bound vortex circulation \(\mathbf{\Gamma}_{b}\) is calculated by the Kutta-Joukowski theorem:  
\[\mathbf{\Gamma}_{b}=\frac{1}{2}\mathbf{v}_{b}\mathbf{C}_{l} \tag{6}\]  
where \(\mathbf{C}_{l}\) is the lift coefficient of the blade section obtained by integrating the pressure on the blade surface. The vorticity of new vortex passed to the viscous wake method (VWM) is calculated by the tip vortex strength using Equations (4) and (5).  
The \"outer boundary correction\" approach [21] is adopted to pass the induced velocity from the VWM which is only imposed on the outer boundary of the blade grid to the CFD domain. At the outer boundary of the CFD domain, the velocity, density, energy, and pressure are obtained from the following equations:  
\[\mathbf{V}=\left(\begin{array}{c}u\\ v\\ w\end{array}\right)=\left(\begin{array}{c}u_{a}-u_{g}+u_{i}\\ v_{a}-v_{g}+v_{i}\\ w_{a}-w_{g}+w_{i}\end{array}\right) \tag{7}\]  
\[\rho=\left(\frac{a^{2}}{a_{\infty}^{2}}\right)^{1/(\gamma-1)} \tag{8}\]  
\[e=\rho\bigg{[}\frac{a^{2}}{\gamma(\gamma-1)}+\frac{u^{2}+v^{2}+w^{2}}{2}\bigg{]} \tag{9}\]  
\[p=(\gamma-1)\rho\bigg{[}e-\frac{u^{2}+v^{2}+w^{2}}{2}\bigg{]} \tag{10}\]  
\[a^{2}=a_{\infty}^{2}+\frac{\gamma-1}{2}\Big{[}V_{\infty}^{2}-\left(u^{2}+v^{2} +w^{2}\right)\Big{]} \tag{11}\]  
here, \((u_{a},v_{a},w_{a})\) are the free-stream velocities, \(\left(u_{g},v_{g},w_{g}\right)\) are the blade moving velocities, and \((u_{i},v_{i},w_{i})\) are the wake induced velocities. \(\gamma\) is the ratio of specific heats and \(a\) is the local Mach number. The subscript \(\infty\) denotes the free stream.
# An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems
## 3 Hardware Design of the AHS  
### Proximal Soil Sensor Device and Power Supply  
Soil moisture is not only an important part of soil fertility and an important factor affecting plant growth and development, but it is also an important parameter for studying agricultural drought and crop drought. Therefore, data acquisition devices and systems based on various soil moisture sensors have been widely used [14]. In this paper, a commercial soil moisture sensor was used as the research object and was taken as the sensing module of the AHS. Its accuracy and reliability have been tested in practice and in the market for a long time. The volumetric moisture content of the soil was measured with an RS-485 standard communication interface, with a working voltage of 12 V and a response time of less than 1 s. Under the condition of no external load, the maximum working current was less than 25 mA, and the average was no more than 10 mA. More parameters are shown in Figure 2.  
Figure 1: Overall structure of the system.  
Figure 2: Soil moisture sensor.  
The system power supply adopted a lithium thionyl chloride battery with an output voltage of 3.6 V and a battery capacity of 3500 mAh. It has the characteristics of high energy density, long service life, and excellent low-temperature performance. It is especially suitable for all-weather battery-based power supply devices in the field [15]. To simplify the hardware structure and facilitate application deployment, the system adopted a single-battery global power supply and an efficient power management scheme. The sensor device adopted a 12 V DC power supply, and the MCU, flash chip, RS-485 transceiver, and other chips adopted 3.3 V power supplies. Therefore, the battery voltage was boosted to 12 V in the circuit hardware, to supply power to the sensor. A stable 3.3 V was output by the multichannel linear voltage regulator to supply power to the chips, in which the main controller (3.3 V) and the peripheral circuit (3.3 V) were independently supplied to eliminate the interaction between loads.
## 2 Materials and Methods
### Methods
#### 2.2.4 Oxidation-Reduction Potential (ORP), TBARS (Thiobarbituric Acid Reactive Substances) Index  
The oxidation-reduction potential (ORP) was determined following the method outlined by [PERSON] and [PERSON] [33]. A digital pH meter (Delta 350, Mettler Toledo, Schwerzenbach, Switzerland) equipped with an In Lab Redox Pro electrode (Mettler Toledo, Greifensee, Switzerland) was used. Measurements were conducted at 20 \({}^{\circ}\)C and the standard hydrogen electrode EH(mv) was used. The results were calculated into the value of ORP. The ORP value of the reference electrode at a temperature of 20 \({}^{\circ}\)C Eref = 207 mV was summed up with the value obtained with the equipment.  
The lipid oxidation in the sausages was assessed according to [PERSON] et al. [34]. The amount of thiobarbituric acid reactive substances (TBARS) was measured. The absorbance at 532 nm at approx. 20 \({}^{\circ}\)C using a Nicolet Evolution 300 spectrophotometer (ThermoElectron Corp., Waltham, MA, USA) was read. The values were expressed as mg of malondialdehydedehyde (MDA) per kilogram of sample, as shown in the following equation:  
TBARS [mg MDA/kg] = 5.5 \(\times\) absorbance value of the tested sample
## 2 Literature Review
### Data-Driven Solutions
#### 2.2.2 Unsupervised Methods  
These are methods that do not require labeled data for their development [16]. In this case, the algorithm identifies patterns and aims to gain experience from available data. These techniques are used when the distribution company does not have labeled data or when one of the two classes is much larger than the other [66].  
The need for distribution companies to have labeled data makes supervised methods impractical. Therefore, there are research lines focused on defining techniques that use unsupervised data against NTLs [61]. [PERSON] and [PERSON] proposed a dynamic model called recursive transform learning for the online unsupervised detection of NTLs. This model has been shown to achieve results that are comparable or even better than supervised methods [61]. Along the same lines, [PERSON] et al. developed a fraud detection system for the company Endes in Spain [41]. The work utilizes data analytics and is based on five different algorithms for the detection of NTLs using the Pearson coefficient, Bayesian networks, and DT. They achieved a success rate of 38% in the inspection of real customers with the help of the presented algorithms.  
[PERSON] et al. proposed a model for using data analysis to detect electricity theft and a new metric that leverages the model to evaluate and compare anomaly detection [80]. To validate the approach, real data from a system with advanced metering infrastructure were used. Honest consumer data were considered, but unfortunately, no metric was defined to evaluate whether the collected data were free from theft. [PERSON] et al. presented three distributed algorithms to solve a system of linear equations for detecting whether a particular consumer is fraudulent or not by determining coefficients of honesty [81]. Neural networks were applied and achieved effective results, with 75% of true negatives, 95% of true positives, 6.25% of false negatives, and 25% of false positives [52]. It is even recommended to improve them by using the hyperparameters with which the neural network is trained.  
[PERSON]'s analysis was also employed for the initial diagnosis of big data from smart meters, along with a Stackelberg game theory model. The framework was validated by using real electricity usage data from an electric service company in Florida to filter out fraudulent meters within a community. From the results, the probability of detecting energy theft in each smart meter was obtained. However, the false positive rate was quite high compared with existing techniques in the literature [82]. Bayesian risk structures, RF, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were also applied to recover lost revenue due to NTLs and detect potential irregular consumers based on their consumption profiles [8].
# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 3 Methodology
### Learning Representations  
The goal of this section is to learn a function, i.e., \(f_{G}:V\to R^{d}\), to generate informative road network representations that are both topology and mobility aware. Here, we are inspired by the idea of word2 vec [12] to obtain the project function \(f_{G}\) based on random walk sequences. Specifically, we can define the objective function \(f_{O}\) as follows:  
\[f_{O}=\frac{1}{N\times R\times L}\sum_{k=1}^{R}\sum_{i=1}^{N}\sum_{l=1}^{L}log \prod_{j=l-nei}^{l+nei}P\Big{(}f_{G}(seq(i)^{(k)}{}_{j})\Big{|}f_{G}(seq(i)^{(k )}{}_{l})\Big{)}, \tag{8}\]  
Equation (8) indicates the likelihood of a predictor using a node \(seq(i)^{(k)}{}_{l}\) to predict its neighbors \(seq(i)^{(k)}{}_{j}\in[l-nei,l+nei]\) in the sequence. \(nei\) is the hyperparameter denoting the size of the neighborhood. We can optimize this neighborhood-preserving likelihood objective function \(f_{O}\) to choose the best \(f_{G}\) value via the stochastic gradient descent (SGD) method with negative sampling [25].  
In this way, the learned representations of road networks exhibit the following properties: 1 if two road segments are topologically close, then their representation vectors are close in latent space; 2 if two road segments are frequently visited sequentially during human mobility, then the distance between their representation vectors is also small in latent space.