esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
Many authentication and key agreement schemes for various environments have been presented over the last few years to ensure security and privacy of users [15; 16; 17]. In 2014, [PERSON] et al. [15] presented the security and privacy scheme in V2G communication. In 2016, [PERSON] et al. [16] presented an ECC-based three-factor authentication scheme for e-health cloud to ensure privacy of health information. In 2016, [PERSON] et al. [17] presented an efficient privacy-preserving scheme in the SG environments to provide secure communication and guarantee user's anonymity.  
Recently, SG has attracted much attention from academia, research institutes, industry, and government [18; 19]. In 2016, [PERSON] and [PERSON] [20] presented identity-based encryption and signature key distribution protocol for the SG. However, in 2016, [PERSON] et al. [21] showed that [PERSON] and [PERSON]'s scheme [20] does not protect against ephemeral secret leakage attack and cannot ensure the privacy of smart meter. To resolve security drawbacks of [PERSON] and [PERSON]'s scheme, [PERSON] et al. [21] presented a secure authentication key agreement scheme for SG. In 2015, [PERSON] et al. [22] proposed a secure authentication scheme between smart meter and the utility system to manage information of power consumption. In 2016, [PERSON] et al. [23] presented an authentication scheme for SG, which performs secure user authentication for SG to provide protection against various attacks. In 2016, [PERSON] et al. [24] presented ECC based lightweight anonymous key distribution scheme for SG and it was more efficient than [PERSON] and [PERSON]'s scheme [20]. In 2017, [PERSON] et al. [25] presented secure three-factor remote user authentication scheme for renewable energy in SG system to enhance security level. In 2019, [PERSON] et al. [6] presented ECC-based authentication protocol for demand response management in SG system. However, as shown below, their scheme cannot prevent a variety of attacks such as SG device stolen, masquerade, and session key disclosure attacks, and it cannot ensure secure mutual authentication. Thus, we present a privacy-preserving lightweight authentication protocol for demand response management in the SG environments to address security problems of [PERSON] et al.'s scheme.
## 2 Model Construction and Algorithm Enhancement
### Validation and Evaluation
#### 2.3.4 XG Boost Model  
The XG Boost [29] is a tree-based ensemble learning algorithm that builds decision trees using gradient boosting. Each tree is trained on the residuals of the previous ones, allowing the model to capture nonlinear relationships and handle high-dimensional data effectively.  
The model is defined as follows:  
\[y_{i}=\sum_{t=1}^{T}f_{t}(x_{i})\ f_{t}\in F \tag{4}\]  
where \(y_{i}\) is the predicted value, \(x_{i}\) is the input feature vector, \(f_{t}\) is the output of the \(t^{th}\) decision tree, and \(T\) is the total number of trees. XG Boost uses gradient boosting to iteratively train multiple decision trees, each correcting the errors of the previous tree, improving prediction accuracy.  
In summary, logistic regression offers simplicity and interpretability, making it well-suited for binary classification problems with linear relationships. Decision tree models provide flexibility in capturing complex, nonlinear patterns. Random Forest enhances predictive accuracy and robustness by aggregating multiple decision trees. XG Boost delivers high efficiency and superior performance, particularly in handling large-scale and complex datasets.
# Osteoarthritis--The Role of Mesenchymal Stem Cells in Cartilage Regeneration
## 4 MSC-Based Therapy for OA
### Sources of MSCs
#### 4.2.4 ESC-Induced MSCs (ESC-MSCs) for OA Treatment  
Exosomes made from human ESC-MSCs exhibited exceptional cartilage regeneration, including complete repair of hyaline cartilage, in rats with osteochondral defects [91].  
ESC-MSCs are developed and differentiated indefinitely into any three embryonic germ cell lines: ectoderm, endoderm, and mesoderm [243]. Due to their capacity for continuous self-renewal, ESC-MSCs offer an inexhaustible source of stem cells and chondrocytes for cartilage repair. However, the most significant barrier to employing ESC-MSCs for cartilage matrix regeneration is the ethical complexity and low survival rate of human ESC-MSCs following cell mass disintegration [244]. In addition, ESC-MSCs differentiation into chondrocytes and cartilage regeneration are challenging processes requiring a complex microenvironment, three-dimensional structure, and a specific mechanotransduction signal [245].  
Studies found that exosomes produced from ESC-MSCs reduced cartilage breakdown and matrix degradation in a medial meniscus instability model by enhancing Col II and aggrecan while lowering ADMTS5 expression. They also reduced the maximal and total OARSI scores, which resulted in milder OA pathology [246].  
Furthermore, it was observed that exosomes and microparticles derived from BM-MSC exhibited a dose-dependent effect on enhancing the expression of anabolic cartilage markers, precisely collagen type II (Col II) and aggrecan, in chondrocytes with osteoarthritis-like characteristics. Additionally, these exosomes and microparticles demonstrated inhibitory effects on catabolic markers, such as MMP-13 and ADAMTS5, as well as inflammatory markers. Moreover, they were found to mitigate articular cartilage damage and subchondral bone degradation, as evidenced by experimental data [247].  
When ESCs were coupled with polydimethylsiloxane (PDMS) scaffolds, initial expression of chondrogenic markers Sox9 was boosted, followed by increased production of Col II (a cartilage-specific marker) and decreased expression of octamer-binding transcription factor 4 (Oct4). It did not, however, promote hypertrophic cell differentiation [245].  
Exosomes obtained from the 3D culture of umbilical MSCs were more chondrocyte than exosomes obtained from 2D culture systems [248]. This meant that they significantly increased chondrocyte proliferation, migration, and matrix synthesis, as well as improving gross appearance and attenuating cartilage defects in an animal model.
## 2 An Optimized Handover Based on Context Information
### An Optimized Handover Scheme
\begin{array}{ll}\mathrm{P_{Macro\_total}}=&\mathrm{w_{Mm}}\\ &\times\left(\mathrm{P_{Macro}^{static}+P_{Macro\_linear}}\right.\\ \qquad\qquad\times\sum_{k=1}^{Num\_UEs}\left(d_{k}\times\mathrm{a_{Mm,k}} \times\left(1-A2_{B_{aver,BS,\ k}}\right)\times A3_{B_{target,BS,\ k}}\times \mathrm{v_{k}}\times C_{B_{target,BS,\ k}}\right.\\ \qquad\qquad\times\mathrm{D\_Cell load\_BS_{target,BS,\ k}}\times\left.\frac{ \left(\mathrm{r_{k}\times C_{req,high}}+\left(1-\mathrm{r_{k}}\right)\times C _{req,low}\right)}{C_{Macro}^{static}}\right)\right)\end{array}  \tag{20}\]  
The total energy consumption is defined as in Equation (21):  
\[\mathrm{P_{Total}}=\mathrm{P_{Macro\_total}}+\sum_{Ss=1}^{Num\_SBSs}\mathrm{P (Ss)_{Small\_total}} \tag{21}\]  
Finally the energy optimization problem of the proposed scheme is formulated as in Equation (22):  
\[ \begin{array}{ll}\min&\mathrm{P_{total}}\\ \mathrm{s.t.}&\mathrm{P(n)}_{small}\leq n\mathrm{P_{Small}^{static}}\\ &\mathrm{P_{macro}\leq P_{Macro}^{static}}\\ &\mathrm{a_{Mm,k}\leq w_{Mm}}\\ &\mathrm{a_{Ss,k}\leq w_{Ss}}\\ &\mathrm{r_{k}\leq d_{k}}\\ &\sum_{n=1}^{Num\_SBSs}\mathrm{a_{Ss,k}\leq 1}\\ &\mathrm{w_{Mm},w_{Ss}\leq 1}\\ &1\leq k\leq Num\_UEs,\quad 1\leq n\leq Num\_SBSs}\end{array}  \tag{22}\]Since the optimization problem formulated is a NP hard problem, we obtain the solution of the optimization problem using particle swarm optimization [42; 43], which is a widely used bio-inspired algorithm, like genome-based approach [44]. The particle swarm optimization is easy to implement and calculated easily within a short time. It has been applied to many network issues such as resource allocation problem [45], routing algorithms [46], and the concept of information theory and entropy were used in the development of particle swarm optimization. In this paper, the discrete particle swarm optimization (DPSO) [43] is applied to obtain the solution to the proposed optimization problem. Figure 7 shows the flowchart of the particle swarm optimization of the proposed handover scheme, which is based on [47]. To obtain an initial solution, the values of number of particles, learning factors, and dimension of particles were initialized. Also, the number of iterations is initialized as 500. Then, conditions of the proposed optimization problems were initialized as particles by random positioning and the initial value of the particle was initialized as 0. Then, initial fitness was evaluated for each particle position. After that, local best fitness pbest and position were obtained, and global fitness gbest and position were obtained. Figure 7: Flowchart of the particle swarm optimization of the proposed handover scheme. The velocity and position of particles were updated using Equations (23)-(25), based on DPSO [43]. Then, the algorithm checks allowable range for each particle and new fitness function of the objective function were evaluated. The values of pbest and gbest are obtained and the same procedure was iterated. The obtained gbest value is considered as the solution to the proposed optimization problem.
## 1 Introduction  
Fire engineering is a science discipline that requires a multidisciplinary approach to resolve fire safety parameters. Some of these parameters include the cause of the fire, the incipient phase of fire, fire detection systems, personal evacuation, fire development, and fire suppression systems. The fire processes are limited primarily to the characteristics of the fuel, the amount of fuel, and the availability of oxidants.  
A simple method that can be used to graphically represent fire is the heat release rate (HRR) versus time, known as the \"fire curve\". The HRR is so important that it has been described as the single most important variable for a fire hazard.  
The traditional prediction for specifying the development of an enclosure fire assumes there is a homogeneous temperature condition in the enclosure. The accuracy and validity of this assumption was examined in [PERSON]'s experimental fire tests. The results clearly show that homogeneous temperature conditions are not present, and variations in the enclosure averages exist [1].  
During the course of the fire, the fire can change between being fuel-controlled and ventilation-controlled. In ventilation-limited fires, the airflow (oxygen) will govern whether the enclosure will be a fuel-rich environment or lean fuel environment. This can be due to the natural stages of the fire, the geometry of the enclosure, or the factors affecting the ventilation.  
The idea of using a fire computation simulation \"field model\" to generate data has been shown to be useful in recent fire engineering studies, such as for fire detection in tunnels [2], structural fire protection designs [3], and hazard assessments. The field model would help avoid the need for conducting costly experiments and facilitate parametric studies of problems [4].  
There are different approaches employing CFD codes for turbulent flows. The visualization of a turbulent flow using \"turbulent eddies\" or Reynolds-averaged Navier-Stokes (RANS) equations is a rational approach considering that the direct numerical simulation (DNS) computing requirements for the direct solution are not practical. In this paper, turbulent fluid motions were modeled, but only a mean flow was resolved. The computations were not intended to model a specific fire scenario but rather an enclosure fire with a focus on ventilation variation and geometry to elaborate on the problems, visualize ideas, and achieve safety objectives.
## 2 Control Framework and Test Platform
### Hardware Implementation for the Test Platform  
As shown in Figure 2, the experimental platforms are built based on two electric passenger vehicles as the host vehicle and the preceding vehicle. The host vehicle, produced by the BAIC company, is front-wheel drive and equipped with a 100-kW driving motor, a single-speed gearbox, and a 41.4-kWh lithium battery pack.  
A Cohda Wireless MK5 OBU (On-Board Unit), which supports the SAE J2735 DSRC standard, is used to transmit information from the preceding vehicle to the host vehicle. The data receiving and sending program, \"BSM-Shell\", is run in the embedded Linux system of the OBUs. Both vehicles are equipped with the NAV-982 inertial navigation system (INS) combined with a Global Positioning System (GPS) to measure acceleration and record the position of the vehicle. On the preceding vehicle, a Raspberry Pi with extended RS232 and CAN interfaces serves as the bridge between NAV-982 and OBU for parsing the raw data from NAV-982. A Delphi 77-GHz millimeter-wave radar is installed on the front of the host vehicle to measure the distance and relative velocity of the preceding vehicle. A rapid control prototyping system is used to test the control strategy. The control algorithm is programmed in the MATLAB/Simulink environment and run in the dSPACE Autobox controller, equipped with a DS1007 processor board and a DS2202 I/O board. The control signals for acceleration and braking are sent to the lower-level control units through CAN bus. In addition, INS data parsing, radar signal processing, and the target tracking algorithm are fulfilled by the dSPACE. System states are monitored on the host PC through Ethernet. The sampling time of the system is 50 ms.
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 3 Separate False Alarm Clutter from Data
### Mark the Point on Grid  
After the CFAR processor and the Direction of Angle (DoA) estimation, we obtain the point data carrying with its own location information, namely range and azimuth angle. The first step of the separation is to mark those points on the grid constructed in Section 2 based on their locations. As the grid is uniform in angular dimension, to facilitate the calculation, we operate in order from azimuth angle to range. Assume \(n_{a}\), \(n_{r}\) as the grid index of the point in angular dimension and range dimension respectively. r and \(\alpha\) refer to its range and azimuth angle. The calculation method is described in Equations (11) and (12).  
\[n_{a}=\left[\frac{\alpha}{\Delta\theta}\right]+1 \tag{11}\]  
\[n_{r}=\left[indnet(ind,n_{a})\leq r<net(ind+1,n_{a})\right] \tag{12}\]  
where \([\cdot]\) is the symbol of round down. \(\Delta\theta\) is the angular spacing of the grid. \(net\) is the grid coordinate matrix in accord with the range coordinate set \(\Lambda\) which is calculated in Section 2. The number of columns in \(net\) is the same as the number of elements in the angular coordinate set \(\Theta\) where each column vector is the range division corresponding to each angular value. According to the range and angular index calculated above, mark the point on a matrix \(A\) with the same size of \(net\).
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 3 Proposed Method (LA-RPL)
### Network Graph Formation Phase
The parent nodes are aware of the amount of k in the network that cannot send to children more than the threshold \(k\) of the DAO-Ack message on admission as a child. The proposed structure for DIO message is presented in Figure 7.  
Figure 5: A perspective of RPL graph structure. Figure 6: A perspective of a limited degree RPL graph. In this approach, the number of transmitted (i.e., relayed) packets in nodes placed near the sink node is not changed. However, the collision and packet loss rate decreases, which yields in the reduction of network energy consumption. According to our proposed method, in each node other than the root node, these steps are followed:  
1. As soon as node \(v\) selects its optimal parent (\(p\)) from DODAG, node \(v\) assists \(p\) through sending a DAO message, in order to construct the downstream routes. 2. Since \(p\) may receive DAO from different children, this node investigates the number of requests at the moment of receiving a DAO. \(p\) adopts node \(v\) as its child and adds the existing route to \(v\) into its routing table, provided that the number of accepted parental requests (including the request of node \(v\)) does not exceed \(k\). By doing so, \(p\) informs \(v\) about acceptance of the request through sending a DAO-ACK to \(v\). On the other hand, if the number of existing children of \(p\) exceeds \(k\) value, \(p\) denies the request of \(v\) and informs it through sending a DAO-ACK. 3. Having received the DAO-ACK message, node \(v\) creates the upstream route to \(p\) in order to stop the procedure of DAO allocation and confirmation, provided that DAO-ACK is an accept confirmation. However, if the DAO-ACK includes a denial notification, \(v\) selects another proper parent \(p^{\prime}\) from the available parent set, and sends a DAO message to \(p^{\prime}\).
## 3 Results and Discussion
### Optimization of the Production Conditions by RSM Design
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **DF** & **Mean Square** & **\(F\) Values** & **\(p\) Values** & **Significance** \\ \hline Model & 4.18 & 9 & 0.4643 & 18.54 & 0.0025 & **\(\ast\)** \\ A–Time point of & 0.2722 & 1 & 0.2722 & 10.87 & 0.0215 & \(\ast\) \\ L-Met addition & 0.2722 & 1 & 0.2722 & 10.87 & 0.0215 & \(\ast\) \\ B–Glucose & 0.6188 & 1 & 0.6188 & 24.71 & 0.0042 & \(\ast\) \\ concentration & & & & & & \\ C–Yeast extract & 0.6757 & 1 & 0.6757 & 26.98 & 0.0035 & \(\ast\) \\ AC & 0.0051 & 1 & 0.0051 & 0.2029 & 0.6713 & \\ BC & 0.0692 & 1 & 0.0692 & 2.76 & 0.1574 & \\ A\({}^{2}\) & 0.8383 & 1 & 0.8383 & 33.47 & 0.0022 & \(\ast\) \\ B\({}^{2}\) & 0.7690 & 1 & 0.7690 & 30.70 & 0.0026 & \(\ast\) \\ C\({}^{2}\) & 1.31 & 1 & 1.31 & 52.28 & 0.0008 & \(\ast\) \\ Residual & 0.1252 & 5 & 0.0250 & & & \\ Lack of Fit & 0.1019 & 3 & 0.0340 & 2.91 & 0.2658 & Not significant \\ Pure Error & 0.0233 & 2 & 0.0117 & & & \\ Cor Total & 4.30 & 14 & & & & \\ \(R^{2}\) = 0.9709 & \(R^{2}\)\({}^{\mathrm{a}}\)\({}^{\mathrm{a}}\)\({}^{\mathrm{a}}\) & \(\mathrm{CV}=7.42\)\% & & & \\ \hline \hline \end{tabular} Note: \({}^{\ast\ast\ast\ast}\), Significant at 5% level (\(p<\)0.05); \({}^{\ast\ast\ast\ast}\), significant at 1% level (\(p<\) 0.01); \({}^{\ast\ast\ast\ast\ast}\) Significant at 0.11% level (\(p<\) 0.001).  
\end{table} 
Table 7: Regression coefficients and their significances for 3-Met production from the results of the BBD. Figure 3: Cont. The maximum predicted value of 3-Met production was 3.03 g/L derived by the Design expert 11 software with the following critical values: A (time point of L-Met addition) = 4.9 h, B (glucose concentration) = 42.7 g/L and C (yeast extract concentration) = 0.9 g/L. According to the results of the statistically designed experiments, the optimized process parameters were: 42.7 g/L glucose, pH 6, 0.9 g/L yeast extract, 6 g/L L-Met, 28 \({}^{\circ}\)C, shaking speed 210 rpm, loading volume 50 mL/250 mL, inoculum size 0.5% (v/v), time point of L-Met addition 4.9 h, culturing period 48 h and 2.5 g/L Tween-80. Validation experiments were performed at the optimal level, and the yield of 3-Met was 3.16 g/L, which was 88.1% higher than the value before optimization (1.68 g/L, Table 5). Additionally, YHM-G has obvious advantages in terms of yield compared with other reported 3-Met-producing natural yeast strains (Table 1).
## 2 Materials and Methods
### Study-Selection Process and Data Extraction  
Studies retrieved from the literature search were screened independently by two of the authors. The following inclusion and exclusion criteria were applied: duplicates were excluded. Only studies published in English were considered. Reviews and book chapters were excluded, and only research articles were considered eligible. Titles, abstracts, and full texts were screened. Studies reporting the application of phenolic compounds as MR inhibitory agents in food matrices other than bakery products were excluded, as well as articles about (i) the effect of phenolic compounds in in vitro studies, (ii) studies on enzymatic immobilization, and (iii) phenolic compound bioaccessibility studies. Research articles reporting data on MR compound content in cereal/pseudocereal-based products enriched with phenolic compounds were included. To minimize bias, only research articles published in peer-reviewed journals were considered eligible. Conflicts and inaccuracies were solved by a third author/scientist. The study search and selection process were summarized in a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram [11].
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 4 Network Models under Test  
This section describes the network models used in the study, all configured in a data center topology and utilizing the OSPF protocol for dynamic routing.  
The _emulated (GNS3)_ model (Figure 2) consisted of six nodes, emulated Cisco routers running IOSv Version 15.5, built using Cisco Modeling Labs (CML). The configuration was performed using the CLI and node support shell processing. The routers employed the OSPF protocol for dynamic routing and established neighbor relationships by utilizing the same Autonomous System (AS) number.  
The _Physical (Cisco)_ network model followed the same structure as the _emulated (GNS3)_ model, utilizing Cisco 1841 routers and adhering to the data center topology with the OSPF protocol for dynamic routing.  
The _SEED Internet Emulator_ model also consisted of six nodes. This platform provides essential Internet elements as Python classes, generating Docker containers and necessary files to set up the network. In Figure 2, circles represent routers and squares represent subnets. The SEED client is required to interact with the system. Once the network is bootstrapped, events are logged to a file for the verification of successful network instantiation. To modify the emulated network, users must adapt the Python code before re-emulating.  
The hybrid network combines the emulated (GNS3) model (Figure 3) with three physical Cisco routers (1841) using a bridged network adapter on the host server to demonstrate the integration of different network models through GNS3 functionality. The system includes a cloud node representing physical Network Interface Cards (NICs). The cloud node, labeled as _Cloud1_, provides access to the physical network to build a distributed solution once the physical equipment is configured and operational.  
Figure 2: Logical Network Topology.