esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Design and Implementation
### Proposed Technique  
In this work, we present an Optimized Enhanced ad hoc On-demand Distance Vector (OEAODV) protocol for the detection of Gray and Black Hole attacks. Two of the parameters in our suggested solution OEAODV are the sequence number and the hop count, which are both included in the four major parameters. These two aspects are used by the Black Hole and Gray Hole to compromise the network's integrity and availability. The remaining three metrics in the table are network performance outputs that have deteriorated as a consequence of the first two (parameters) being attacked. Therefore, we are able to figure out the assault according to Algorithm 1 more efficiently than earlier research by combining these four features and determining the criteria for future Black Hole operations in advance. The architecture of the proposed system and flow diagram of OEAODV are presented in Figures 4 and 5, respectively.  
```
0: Insecure data communication with Black Hole and Gray Hole attacks
0: Secure data communication with Black Hole and Gray Hole attacks
1: Start
2: SN floods RREQ with Fake IP
3: If (IN reply back to Fake IP) OR (Seq > Th1) (GrayListing)
4: SPN Affirmation
5: Initialization of normal AODV route discovery
6: Data packets Routing
7: If (PDR < Th2 and E2E delay > Th3 and overhead(OH) > Th4) or (Blacklisting)
8: MN Affirmation
9: Blacklisting MN
10: Addition of extra field in RREQ to encapsulate ID of MN for alarm
11: else
12: AODV ();
13: end if
14: else
15: start ();
16: end if
17: End
```  
**Algorithm 1** Optimized enhanced ad hoc on-demand distance vector protocol  
**Keywords**: SN, Source Node; IN, Intermediate Node; SPN, Suspicious Node; MN, Malicious Node; Seq, Destination Sequence Number; Th 1, Threshold for Seq; Th 2, Threshold for PDR; Th 3, Threshold for E2E; Th 4, Threshold for OH.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 2 Scenario Definition
### GNSS Satellite Visibility
The standard and the proposed models are used to define the elevation mask as function of the satellite azimuth. A symmetrical and straight street is considered, such as in Figure 2, and the street orientation is randomly distributed over 50,000 iterations at different epochs. The resulting visible satellites per epoch are used to compute the probability of observing at least one, two, three or four satellites for a maximum elevation mask, which is defined between \(0^{\circ}\) and \(90^{\circ}\). As shown in Figure 3, the GPS constellation is able to provide full position availability (i.e., four or more visible satellites) for a maximum elevation mask of \(30^{\circ}\). Thus, both models have the same impact for rural and suburban areas. However, in urban areas with a high density of buildings, the standard model is too optimistic, and it may fail to characterize the low satellite visibility. Thus, the proposed model is a generic and representative model, which can be characterized only with the maximum elevation mask. Let us now couple the cellular network density with the GNSS visibility, in order to define a generic hybrid scenario. As described in the previous section, the urban, suburban and rural areas are classified according to the density of BSs, by using the average distance metric \(d_{\mathrm{av,3 BSs}}\). The GNSS visibility is then determined with a set of maximum elevation masks for the different environments. An educated guess of these elevation masks is provided in Table 2, by considering the typical building height and street width of the corresponding scenario. Considering the representative cellular deployment of Figure 1, the resulting \(d_{\mathrm{av,3 BSs}}\) for the grid of receiver positions is used to define the GNSS visibility regions shown in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} Predefined urban canyon \\ Receiver at any location \\ of any symmetrical street \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GNSS elevation mask models for straight street scenarios. Figure 4: Elevation mask defined by the average distance to the three closest BSs of a representative cellular network deployment. Figure 3: Probability of a minimum number of visible GNSS satellites per maximum elevation mask for the ETSI TS 103.246-3 [13] and the proposed models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Average Distance to 3 Closest BSs (m)** & **Channel Model** & **Max. Elevation Mask (\({}^{\circ}\))** \\ \hline Rural & \(d_{\text{av,3 BSs}}>500\) & RMa & 5 \\ Suburban & \(200<d_{\text{av,3 BSs}}\leq 500\) & UMa & 15 \\ Urban & \(100<d_{\text{av,3 BSs}}\leq 200\) & UMi & 30 \\ Urban & \(50<d_{\text{av,3 BSs}}\leq 100\) & UMi & 50 \\ Urban & \(d_{\text{av,3 BSs}}\leq 50\) & UMi & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hybrid scenario definition based on cellular network density and GNSS elevation mask.
# Mathematical Apparatus of Optimal Decision-Making Based on Vector Optimization
## 4 Research, Analysis, and Formulation of the Problem of Decision-Making with Uncertain Data
#### 4.1.2 Investigation of Condition of Uncertainty  
Conditions of uncertainty are considered in two aspects. The first relates to a lack of sufficient information. This is the uncertainty associated with the variety of characteristics (criteria) of the object under study.  
This aspect is defined by the fact that there is not sufficient information on the functional dependence of the characteristic \(f\) and restrictions \(g\) from parameters \(X\) of the studied object. In this case, input data characterizing the object are presented as:  
(a) random data,fuzzy data, or (c) incomplete data, which are usually obtained from experimental data.  
For options (a) and (b), input data have to be transformed into option (c), and are presented in table form: 1 column--parameter size, 2 columns--characteristic size. The methodology of the transformation of random and fuzzy data into tabular form is presented in the magazine \"Fuzzy Decision Making and Soft Computing Applications\". Tabular (experimental) data, using regression analysis, will be transformed into the \(f(X)\) function, i.e., in terms of certainty.  
In the future, only variant c) is considered in this work, and its transformations by regression methods into certainty conditions, i.e., into the function \(f(X)\).  
The second aspect of decision-making uncertainty is related to the fact that an object is characterized by many characteristics: \(f_{1}(X)\),..., \(f_{K}(X)\). The set of characteristics \(\mathbf{K}\) is divided into two subsets \(K_{1}\) and \(\mathbf{K}_{2}\). The subset of characteristics \(\mathbf{K}_{1}\) in a numerical value is desired to be as high as possible (maximum), and the subset of characteristics \(\mathbf{K}_{2}\) is desired to be as low as possible (minimum). Below it will be shown that the decision-making problem with a set of characteristics is reduced to a vector problem of mathematical programming, the solution of which is presented in Section 3.
## 3 Results  
### Titanium Disks Do Not Impair BFP-Mesenchymal Stem Cell (MSC) Viability  
To evaluate the growth and viability of BFP-MSCs on Ti implants, cells were plated on Ti disks for 72h. Next, disks were moved to new culture plates and cells (termed cells \"on disks\") were collected by trypsinization and counted using 0.4% Trypan Blu dye exclusion test, used to determine the number of viable cells present in a cell suspension [23]. Cells remaining in the original culture plate (termed cells \"under the disks\") were also counted. As control (CTRL), BFP-MSCs were plated without disks, in standard growth conditions. Cells were detected on all Ti disks. BFP-MSCs on treated Ti disks showed a proliferative rate comparable to control and comparable to cells on untreated disks (D1) (Figure 1). Furthermore, when plated on Kontact S (D2) and Kontack N (D4) surfaces, the number of living cells counted on disks was significantly higher compared to that of cells under the disks (Figure 1). By contrast, on Kontact (D3) surface the number of living cells counted on the disk was similar to that of the cells under the disk.  
Moreover, Ti disks were not responsible of any cytotoxic effect on BFP-MSCs. Indeed, cells on disks displayed a 90% viability rate with no differences among disks and compared to control cells (Figure 2).  
To investigate the long-term effect of Ti disks on BFP-MSC viability, cells detached from disks were re-plated. After 72 h, BFP-MSCs were live, attached on the plate, reflective, and with a morphology comparable to control cells (Figures 3 and 4). Moreover, they showed a proliferative rate of about three-fold, with no differences among the experimental conditions (Figure 4). Therefore, also after the contact with disks, cells retain their ability to survive and proliferate.  
Figure 1: [PERSON]’s fat pad mesenchymal stem cell (BFP-MSC) growth on Ti disks. Number of BFP-MSCs plated in 12-well culture plates without disks (CTRL), on D1, D2, D3 or D4 for 72 h. Black bars represent live cells counted on disks. Gray bars represent live cells counted under the disks. \({}^{*}\) Denote statistical significance of cells on disk versus cells under disk (\({}^{*}\)_p_ < 0.05).  
Figure 3: BFP-MSC morphology. Representative image of BFP-MSCs harvested from disks and re-plated. Images were taken by the Olympus DP20 microscope digital camera associated to a phase contrast inverted microscopy (magnification 100\(\times\)).  
Figure 2: BFP-MSC viability on Ti disks. Percentage of live BFP-MSCs plated in 12-well culture plates without disks (CTRL), on disk D1, disk D2, disk D3 or D4 after 72 h.
###### Abstract  
(i) Background: The increasing prevalence of allergic diseases highlights the need for effective treatments. _Lonicera caerulea_ fruit has been recognized for its anti-inflammatory, anti-cancer, and neuroprotective effects, but the mechanisms underlying its anti-allergic properties remain unclear. (ii) Objective: This study aims to evaluate the total phenolic, total flavonoid, and cyanidin-3-glucoside (C3G) contents of _Lonicera caerulea_ extract (HR2302-30E) and to investigate its antioxidant and anti-allergic activities. (iii) Methods: Using an IgE-stimulated RBL-2H3 cell model, we assessed the effects of HR2302-30E and C3G on mast cell degranulation, \(\beta\)-hexosaminidase and histamine release. Western blot analysis was performed to evaluate the expression of high-affinity IgE receptor (Fc\(\varepsilon\)RI)/\(\gamma\) and the phosphorylation of Src family kinases (Syk, Fyn). We also examined the phosphorylation of downstream factors phospholipase Cy, protein kinase C8, and mitogen-activated protein kinase. (iv) Results: Total phenolic, flavonoid, and C3G contents of HR2302-30E were 18.73 mg GAE/g, 11.83 mg QE/g, and 7.02 mg/g, respectively. In IgE-activated mast cells, HR2302-30E and C3G inhibited \(\beta\)-hexosaminidase and histamine release. Western blot analysis revealed reduced expression of Fc\(\varepsilon\)RI/\(\gamma\) and decreased phosphorylation of key downstream signaling molecules. Conclusions: These findings suggest that HR2302-30E and C3G modulate Fc\(\varepsilon\)RI signaling, indicating their potential as natural anti-allergic agents.  
blue honeyuckle; cyanidin-3-glucoside; mast cell stabilization; IgE receptor signaling +
Footnote †: journal: Applied sciences
# Object-Level Semantic Map Construction for Dynamic Scenes
## 5 Discussion
### System Efficiency  
In object 3D tracking, the depth information and gray-scale texture information are both used so the processed data has been massively increased compared with the 2D tracking method. To achieve the real-time or quasi-real-time performance of the algorithm, we perform this task with GPU. If there are 3-4 objects tracking in the scene at the same time, the system test can execute at a rate of approximately 20-25 fps. Nevertheless, our algorithm can handle multiple moving objects in the scene by simply taking up more GPU computing resources. However, if data occlusion occurs due to move overlap on these moving objects, our system will be helpless because the depth data and grayscale texture data of the same object used for 3D tracking would be different at different moments after the objects overlap. As a result, 3D object tracking results would have large errors.
## 5 Use Case
### Threat Analysis and Risk Assessment  
The Threat Analysis and Risk Assessment (TARA) identifies, evaluates, and assigns risk treatment decisions to attack scenarios affecting the ITEM. These decisions define how risks are managed in the security concept. Assets, the cybersecurity-relevant components of the ITEM, are assigned to ITEM elements and identified using the ENISA checklist [27]. Multiple assets, such as software, status, data, and configurations, may correspond to a single element. Damage scenarios, linked to specific assets and cybersecurity properties (availability, integrity, confidentiality), describe potential harm and vehicle impact. Under scenarios are divided for precise property assignment. The case study iteratively developed damage scenarios by analyzing asset vulnerabilities across these properties. Threat scenarios outline potential threats to ITEM assets, identified via the UN ECE R 155 checklist [28]. Each scenario is associated with a cybersecurity property, and ambiguous scenarios are subdivided as necessary. An impact Rating assesses each damage scenario's effect on safety, financial, operational, and privacy (SFOP) attributes, with a rating assigned for each category. Attack paths detail attack steps for ITEM assets. Each threat scenario is linked to attack approaches, which are further divided into steps. In the case study, attack paths traced vulnerabilities to ITEM boundaries. Non-ITEM system attacks are also considered in the security concept. Attack feasibility ratings evaluate the practicality of attack approaches based on factors such as time for vulnerability identification, required resources, available knowledge, and execution windows. Risk ratings are calculated for each attack approach using impact and feasibility ratings. Based on these ratings, risk treatment decisions determine whether risks are acceptable, redistributed, or mitigated in the security concept. Risk treatment decisions are tabulated for attack approaches and threat scenarios. If any decision mandates risk reduction, the corresponding threat scenario requires mitigation. These decisions form the basis for security concept requirements. The diagrams of the model are shown in Figures A15-A23.
## 4 The Proposed Scalable Blockchain Trust Management System (SBTMS)
### Step 4: Consensus Process on Transactions  
As mentioned in step one, calculated message reliability by the vehicles are sent to the nearest RSUs. Each RSU in a specific time calculates the net reliability and then broadcasts it as a transaction on the network of RSUs. Then, according to Step 3, RSUs generate a transient identity, and after that RSUs should attempt to find out transient identities of other RSUs in their committee by sharing or exchanging information with each other, and to compose a fully-connected overlay network for each committee in the RSUs's network. Next, in each committee, RSUs run Practical Byzantine Fault Tolerance (PBFT) protocol [46] to concur on a set (or shard) of transactions. Figure 4 shows the PBFT protocol in a committee to add a transaction in a shard.In PBFT consensus process. In the pre-prepare phase, a RSU sends a transaction to the committee members for validating. In the preparation phase, each of the committee's RSU verifies the transaction validity and sends the result to the others. In commit phase, each RSU that receives more than \(\frac{2z}{3}\) of the prepare message, sends the commit message to the others. Finally, by receiving \(\frac{2z}{3}\) a commit message by the others, the transaction is validated and added to the set (or shard).  
The consensus shard in each committee, signed by at least \(\frac{z}{2}+1\) RSUs, is sent to the final committee, which is composed based on a given s-bit final committee identifier, to create a new block in the Blockchain. The final committee receives the consensus shards provided by the committees and computes a cryptographic digest. To do so, RSUs in final committee run again a PBFT and send shards to the network. Figure 5 presents the process of sharding protocol in our model.
## 5 Attacks and Countermeasures
### Attacking P2P
#### 5.1.3 Sybil Attacks on P2P Networks  
A malicious node may join a distributed network multiple times to generate multiple identities. A malicious node having multiple virtual identities can increase the impact of other attacks like the _Incorrect Lookup Routing_. Further, as P2P networks often use reputation mechanisms to avoid _free riders_, single nodes with multiple virtual identities may improve their own reputation.
## 1 Superalloy
### Creep Life Prediction Model
#### 1.5.1 Traditional Power Law Creep Models  
* Power model  
The power law model represents the steady-state creep rate, \(\varepsilon_{ss}\), as a function of temperature and stress. It can be expressed by combining the [PERSON]'s law and [PERSON]'s law. Equations (1) and (2) represent [PERSON]'s law and [PERSON]'s law, respectively [48; 49]:  
\[\dot{\varepsilon}_{ss}\propto\exp\left(\frac{-Q_{c}}{RT}\right) \tag{1}\]  
\[\dot{\varepsilon}_{ss}\propto A\sigma^{n} \tag{2}\]  
where \(\varepsilon_{ss}\) is the steady-state creep rate at temperature (\(T\)), \(Q_{c}\) is the creep activation energy, \(R\) is the ideal gas constant (8.314 J/mol-K), \(\sigma\) is the applied stress, \(A\) is a proportional constant, and \(n\) is the creep exponent. The Arrhenius Law indicates that the strain rate increases as the temperature (\(T\)) increases, and Norton's Law indicates that the strain rate increases as the stress (\(\sigma\)) increases.  
The power law model can be expressed as Equation (3) by a combination of Equations (1) and (2).  
\[\dot{\varepsilon}_{ss}\propto A\sigma^{n}\exp\left\{\frac{-Q_{c}}{RT}\right\} \tag{3}\]  
where \(\varepsilon_{ss}\) is the steady-state creep rate at temperature (\(T\)), \(Q_{c}\) is the creep activation energy, \(R\) is the ideal gas constant, \(A\) is the proportional constant, and \(n\) is the creep exponent. In the case of power law, \(Q_{c}\) and n are assumed to be constant. The power law model represents the steady-state creep rate as a function of temperature and stress. In a creep deformation, when the temperature is increasing, dependence of stress on steady state creep rate is increasing. It is represented by a change in n value at the power law model [48; 49]. It shows in Figure 9.  
Figure 9: Schematic representation of steady-state strain rate versus stress.  
* Larson-Miller parameter method  
The most widely used creep life prediction method for metals is the LMP method. The LMP consists of two relationships: power law equation (Equation (3)) and Monkman-Grant relationship (Equation (4)).  
\[\dot{\varepsilon}_{ss}t_{r}=C_{MG} \tag{4}\]  
where \(\varepsilon_{ss}\) is the steady-state creep rate at temperature (\(T\)), \(C_{MG}\) are constants, and \(t_{r}\) is the rupture time in the creep test. Assuming constant stress, the two equations can be summarized as the following LMP equation (Equation (5)).  
\[P_{LM}=f(\sigma)=T(C_{LM}+\log t_{r}) \tag{5}\]  
where \(P_{LM}\) is the Larson-Miller parameter and \(C_{LM}\) is the Larson-Miller constant. \(C_{LM}\) typically has a value between 10 and 50. The LMP model predicts creep life based on empirical fitting. Using \(P_{LM}\), LMP model can show a relationship between stress and \(P_{LM}\). However, it has limitations in explaining the change of deformation mechanism when the stress and temperature is changing.
## 4 Example Analysis Validation
### Field Measurements
#### 4.4.2 Results and Analyses of Observations  
The specific measurement process is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Borehole** & **Aperture/mm** & **Hole Length/m** & **Vertical Height/m** & **Angle of Elevation (\({}^{\circ}\))** & **Axial Relationship with Roadway** \\ \hline
1 & 90 & 75 & 60 & 55 & It is perpendicular to the axial \\
2 & 90 & 75 & 60 & 60 & direction of the roadway and faces \\
3 & 90 & 75 & 60 & 65 & the goal of 1301 working face. \\ \hline \hline \end{tabular}
\end{table}
Table 3: borehole design parameter table.  
Figure 11: Borehole layout.  
Figure 12: Control process of a specific measurement.  
The observation results of the boreholes are shown in Figure 13. The variation in leakage across the three boreholes exhibits similar trends. Therefore, this analysis will focus on the variation in leakage and the reasons in Borehole 1 as a representative case. In the borehole section with a depth of 18-38 m, the overall leakage was slightly small, generally not exceeding 10 L/min, indicating that secondary fissures were not well-developed within this zone and that the strata were less affected by mining activities, maintaining relatively high integrity. The predominant fractures in this interval were the original, inherent fractures in the rock mass. However, in the borehole interval of 40-68 m in depth, the leakage during drilling increased sharply to 13-15.8 L/min. This significant increase in leakage suggests that this zone had been heavily impacted by the mining-induced deformation. The enhanced interconnectivity of these secondary fractures led to the formation of a highly permeable fracture network within this interval of the overburden strata. Therefore, the drilling data indicates that this deeper zone had been penetrated by the borehole, entering the WFZ.  
After 68 m, the leakage was 5.2-7.4 L/min, indicating that the overall permeability of the strata in this zone was relatively low. This suggests that the fractures were reduced in this interval compared to the zone above. Therefore, it can be considered that the boundary of the WFZ was at a depth of 68 m in the drill hole, making the WFZDH 58.9 m. The leakage in Borehole 2 and Borehole 3 is similar to that of Borehole 1, and the WFZDHs can be obtained from the analysis as 55.3 m and 57.4 m.  
Figure 14 shows observations of the mining overburden fissures through Borehole 1. According to the pictures from the field observations, no fissures were present in the overburden until the hole depth was 30 m, and slight fissures started to appear at 30 m. At 36 m and 45 m, the rock stratum was seriously damaged. From 63 m onwards, the damage to the rock layer began to weaken, and the fissures decreased until 72 m when no obvious fissures appeared in the rock mass. It can be assumed that the WFZ develops to a depth of 63 m in the borehole, at which the WFZDH is 54.5 m.  
Figure 13: Changes in water leakage in different areas of boreholes: (**a**) Borehole 1, (**b**) Borehole 2, and (**c**) Borehole 3.