esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 3 Risk Analysis
### Semi-Quantitative Risk Assessment (Sqra)
#### 3.3.1 Dynamic Fault Tree  
FTA is used for different applications to study system dependability [53]. It consists of many logic gates that represent the relationship between failures and their origin. In an FTA, basic events are independent, and no consideration of events sequencing/order is possible [54]. This model is called Static Fault Tree (SFT). In literature, many attempts have been reported to overcome these constraints, with consideration of the temporal aspect and statistical dependencies in the FT model. In 1976, [PERSON] et al. [55] have introduced the concept of Priority-AND (PAND) gate. Later, many other extensions to the SFTs have been proposed (e.g., DFT [56], temporal fault trees [57], and State/event fault trees [58]). The most popular one is the DFT. It retains the PAND gate and adds many others like priority OR (POR), Functional Dependency (FDEP), Warm Spare (WSP), and Sequence enforcing (SEQ). Unlike the static fault tree, DFT uses both Boolean and dynamic gates to specify logical relationships among events.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Performance Measures  
These terms are used in SEED [11], we also use these terms to evaluate and analyze the performance of our proposed clustering protocol.  
* Network lifetime has been proved a very important paradigm in WSNs literature. It is the time span of both stable and unstable regions of the network. In this time duration, all the nodes in the network deplete their energy resources.
* From the beginning of the network operation up to the first node depletes its battery is the stable region of the network. This is the time span in which a network generates maximum throughput, as the generated data has direct proportion with the alive number of nodes.
* The timeline starts when the first node runs out of battery up to all the sensor nodes deplete their batteries is called the unstable region in the lifetime of the network. Generally, in this time duration throughput of a network gradually decreases.
* The evaluation metric for the number of CHs also plays very important role in WSNs. This selection measures means that the selection of CHs should be optimal, as, proper and careful utilization of the available resources can help in increasing the lifetime of the network.
* Transmission delay is a time period for which a complete data packet is successfully received at the BS.
* Data throughput is also an important metric for WSN. The successful delivery of the data packets at the BS is called the throughput of the network. There is always a tradeoff between the network lifetime and the throughput of the network. The greater the lifetime of the network, the greater the throughput and vice versa.  
The details of these performance metrics are described in detail in the next subsections.
# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
## 4 Numerical Simulation
### How Node-Type Ratios Affect SCNR  
In this part of our paper, we use resilience metrics \(Y\) and \(S\) to measure SCNR. In Figures 10 and 11, the horizontal coordinates are SCNs at different node-type ratios, and the vertical coordinates are the SCNR at that ratio. The difference between these two figures is obvious. As can be seen in Figure 10, as the horizontal coordinate shifts to the right, the corresponding vertical coordinate decreases significantly. When \(S/R/M/D=1\):1:1:1, \(Y=0.0262\); when \(S/R/M/D=47\):1:1:1, \(Y=0.0053\). This indicates that the more uniform the distribution of node types is in an SCN, the greater its resilience. However, in Figure 10, the SCNR always fluctuates around 0.3436 for different node types. The \(p\)-value calculated under \(Y\) is very small and negligible, but the \(p\)-value obtained under \(S\) is 0.2122, which is much larger than 0.05. This shows that the proportion of node types has a significant impact on SCNR when measured by metric \(Y\). And the effect of node type is not considered when measured by metric \(S\). However, the type of node affects the normal operation of the SCN and should be considered when measuring SCNR.  
In summary, our resilience metric \(Y\) is more applicable compared to the resilience metric \(S\). The resilience metrics we used included all types of nodes, and the type of each node is equally important. The greater the difference in the ratio of node types, the more the SCN may lack a certain type of node to generate a full-type subgraph. Then, the size of the maximum full-type subgraph becomes smaller and the SCN becomes less resilient.  
Figure 9: The resilience of SCNs for different clustering coefficients when using the resilience metric \(S\).
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \multicolumn{8}{c}{**Doors**} \\ \hline
**Element** & **Method** & s [cm] & \multicolumn{3}{c}{Layer 1} \\ \hline
**Internal door** & **Manual** & 5 & \multicolumn{3}{c}{Wood} \\
**Automatic** & 5 & \multicolumn{3}{c}{Wood} \\ \hline External door & **Manual** & 7 & \multicolumn{3}{c}{Wood} \\  & Automatic & 7 & \multicolumn{3}{c}{Wood} \\ \hline \multicolumn{8}{c}{**Internal Shading**} \\ \hline Element & **Method** & Multiplier U\({}_{\text{glass}}\) & Multiplier \(\text{$\delta$}_{\text{glass}}\) & Multiplier T & Diffusion factor \\ \hline Refurbished roller blind & **Manual** & 0.9 & **0.4** & 0.09 & **0.5** \\  & **Automatic** & 0.9 & **0.12** & 0.09 & 1 \\ \hline Original roller blind & **Manual** & **0.8** & **0.2** & 0.09 & **0.5** \\  & **Automatic** & **0.7** & **0.7** & 0.09 & 1 \\ \hline \multicolumn{8}{c}{**Slabs**} \\ \hline Element & Method & s [cm] & Layer 1 & Layer 2 & Layer 3 & Layer 4 & Layer 5 & Layer 5 & U [W/m\({}^{2}\)K] \\ \hline \multirow{2}{*}{Basement floor} & Manual & 54.5 & Gravel (40 cm) & Concrete (13 cm) & Cores (1.5 cm) & - & - & 1.615 \\  & Automatic & 54.5 & Gravel (40 cm) & Concrete (13 cm) & Cores (1.5 cm) & - & - & - & 1.615 \\ \hline \multirow{2}{*}{Internal floor} & Manual & 22.5 & Plaster (1 cm) & Jost (22 cm) & Jost (22 cm) & Jost (4 cm) & Concrete (4 cm) & Gres (1.5 cm) & - & 2.238 \\  & Automatic & 22.5 & Plaster (1 cm) & Jost (12 cm) & Jost (4 cm) & Concrete (4 cm) & Gres (1.5 cm) & - & 2.238 \\ \hline \multirow{2}{*}{Roof} & **Manual** & **43** & Plaster (1 cm) & Jost **(24 cm)** & Slab (4 cm) & Air (4 cm) & Concrete (\# cm) & Bituminous membrane (1 cm) & **1.337** \\  & **Automatic** & **30** & Plaster (1 cm) & Jost **(22 cm)** & Slab (4 cm) & Air (4 cm) & Concrete (\(\mathcal{T}\) cm) & Bituminous membrane (1 cm) & **1.325** \\ \hline \multicolumn{8}{c}{**Surfaces**} \\ \hline Element & Method & \multicolumn{3}{c}{Emissivity} & \multicolumn{3}{c}{Reflectance} \\ \hline Manual & & 0.90 & \multicolumn{3}{c}{0.50} \\  & **Automatic** & **0.95** & \multicolumn{3}{c}{**0.56**} \\ \hline External wall marble & **Marual** & 0.90 & \multicolumn{3}{c}{0.50} \\ cladding & **Automatic** & **0.96** & \multicolumn{3}{c}{**0.50**} \\ \hline External wall full cladding & \multicolumn{3}{c}{0.90} & \multicolumn{3}{c}{0.50} \\  & Automatic & 0.96 & \multicolumn{3}{c}{0.50} \\ \hline \multirow{2}{*}{Roof} & Manual & 0.90 & \multicolumn{3}{c}{0.50} \\  & Automatic & 0.96 & \multicolumn{3}{c}{0.50} \\ \hline \multirow{2}{*}{Roof} & Manual & 0.90 & \multicolumn{3}{c}{0.50} \\  & **Automatic** & **0.80** & \multicolumn{3}{c}{**0.45**} \\ \hline \end{tabular}
\end{table} 
Table 14: Construction elements for resulting models from manual and automatic calibration (bold when changed).
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### Transaction Processing  
As has been presented, BlockchainDB's transaction model and consensus mechanism relies in part on the chosen existing blockchain. Thus, the route of a transaction from a client to the database and the storage layer, up until the chosen blockchain receives the given request, will be examined here in abstract.  
**Database Layer:** Clients communicate with their trusted BlockchainDB peer through put/get requests. These requests are received by the database layer and then are processed by the transaction manager. The transaction manager processes transactions with the customisable consistency level. Before a put/get request is sent to the storage layer, it is forwarded to the ShardManager. The task of the ShardManager is to ascertain the appropriate shard for a given key.  
**Storage Layer:** The storage layer is already aware of the exact location of the necessary shard on the basis of the data received from the database layer. Thus, it can interact with the blockchain network and can perform the necessary function with the aforementioned interface, for example, read-write-async.  
**Offchain verification:** Clients can verify the appropriate execution of their sent requests through the database layer. This offchain verification can be completed on- or offline. With online verification, clients can be assured of the validity of their received results through the verify operation. Offline verification does not call verify operation for single requests; it defers verifications until multiple operations can be executed at once.
# Progressive Collapse Analysis of SRC Frame-RC Core Tube Hybrid Structure
###### Abstract  
Steel reinforced concrete (SRC) frame-reinforced concrete (RC) core tube hybrid structures are widely used in high-rise buildings. Focusing on the progressive collapse behavior of this structural system, this paper presents an experiment and analysis on a 1/5 scaled, 10-story SRC frame-RC core tube structural model. The finite element (FE) model developed for the purpose of progressive collapse analysis was validated by comparing the test results and simulation results. The alternate load path method (APM) was applied in conducting nonlinear static and dynamic analyses, in which key components including columns and shear walls were removed. The stress state of the beams adjacent to the removed component, the structural behavior including inter-story drift ratio and shear distribution between frame and tube were investigated. The demand capacity ratio (DCR) was applied to evaluate the progressive collapse resistance under loss of key components scenarios. The results indicate that the frame and the tube cooperate in a certain way to resist progressive collapse. The core tube plays a role as the first line of defense against progressive collapse, and the frame plays a role as the second line of defense against progressive collapse. It is also found that the shear distribution is related to the location of the component removed, especially the corner column and shear walls.  
steel reinforced concrete frame; reinforced concrete core tube; progressive collapse analysis; loss of key components Article
# Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis
## 3 Methodology
### Wind Energy Calculation
 \tag{4}\]  
```
1:\(P_{w}\), \(t_{charge}\), EV battery time to charge;
2:\(t_{ov}\), Overlap time of intervals;
3:\(P_{charger}\), EV charger power;
4:\(E_{nov}\), Constant supplied energy;
5:\(EV_{no}\), Number of EVs
```  
**Algorithm 1** EV wind power supply  
The total wind energy per unit of time (i.e., one day) was calculated from the intervals that show power stability and do not intersect (overlap) in time. Let \(P_{ov}\) be the instantaneous power intervals of duration \(P_{charge}\) (i.e., charging time of EV battery) distributed over the entire day. These intervals are found by using overlapped sliding windows, where the overlapped intervals are required to be omitted prior to calculating the total energy for the whole day. The non-overlap total energy is found by:  
\[E_{w}=\sum_{i=1}^{N}\overline{P_{w}^{i}}\times\frac{t_{charge}}{60} \tag{5}\]  
where \(N\) is the total non-overlap interval (lines 8-10 in Algorithm 1) within a specific period of time that satisfies the condition \((P_{ov1}\ \cap\ P_{ov2}=\phi)\) for each of the consecutive intervals \(P_{ov1}\) and \(P_{ov2}\). The sum of wind energy generated \(E_{w}^{i}\) within a \(t_{charge}\) time interval is more than the EV charger energy per the smallest time unit (i.e., one minute) for partial charging or is more than the EV charger energy per \(t_{charge}\) time for full charging (80%).
## 7 Conclusions  
In this study, we designed a DGLS for the oil-gas-water multiphase flow of low-production liquid and high-water combine with the phase isolation method. We used methods in Fluent and well experiment simulation to model gas-liquid separation when total flow rates change. In this way, we obtained the model's applicable range. We also studied DGLS's gas-liquid separation efficiency in gas-water two-phase flow and oil-gas-water multiphase flow and drew the following conclusions:  
1. When the medium is gas-water two-phase flow and the total flow rate is constant, the greater the DGLS's gas content, the stronger the gas-liquid separation effect. The gas-liquid separation efficiency peaks when the total flow rate is 20 m\({}^{3}\)/d, indicating that the DGLS is suitable for gas-liquid separation at low flow rates.
2. When the medium is oil-gas-water multiphase flow, the DGLS also shows a pronounced gas-liquid separation effect and realizes the requirement of separating the gas and liquid phases to improve the flowmeter's measurement accuracy. By analyzing the calculated results, researchers can determine the fluid flow state's influence on the DLGS's separation effect.
3. Experimental research on simulated wells has shown that when the gas phase flow is under 15 m\({}^{3}\)/d, the turbine's response increases with the increase in the low flow of liquid. Moreover, the output results show that the DGLS can isolate the gas phase from the liquid phase. However, when the gas phase flow continues to increase, the turbine response reaches an inflection point at low liquid volume, and the DGLS's gas-liquid separation effect is minimal.  
Our research demonstrates that a DGLS is feasible in phase isolation when the medium is oil-gas-water multiphase flow. The DGLS design provides a theoretical basis for the actual production and has application guiding significance as well.  
Conceptualization, Y.Y. and Z.J.; methodology, Y.Y. and G.D.; software, L.H. and W.L.; validation, X.L. and G.D.; formal analysis, Z.J.; data curation, Y.Y.; writing--original draft preparation, Y.Y., L.H. and W.L.; writing--review and editing, X.L. and G.D.; visualization, Y.Y. and Z.J.; project administration, Y.Y., X.L. and Z.J.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Northeast Petroleum University Youth National Fund (2017 QNJL-07), the National Natural Science Foundation of China (51774092), National Natural Science Foundation of Heilongjiang Province (LH2020E012) and the China Postdoctoral Science Foundation (2016M601399).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 4 Results and Discussion
### Imperceptibility Analysis  
Imperceptibility refers to the relative quality of the image (embedded with the secret data) compared to the original image, and is one of the most important parameters used to check the efficiency of a data-hiding scheme. The stego-image resulting after data embedding should be such that it should not draw attention to the adversary. The PSNR, calculated from the differences between the original, cover, and stego-images, provides an idea about the quality of the watermarked images. A high PSNR value indicates good visual quality, and effectiveness of the in embedding scheme. The objective and subjective quality results, for both techniques, under no attack, are shown in Table 1.  
Figure 4: Various test images.  
Figure 5: Watermark.  
Table 1 presents various quality metrics for various test images, for both of the proposed schemes. The minimum PSNR of the former is 38.947 dB, with an average value of 40.33 dB. The use of LDS significantly improves the quality of stego-images, as can be seen from the results. On average, it improves the PSNR by a significant margin of about 3 dB. The average PSNR for the two schemes, at an embedding capacity of 1.5 bpp, was 40.338 dB and 43.75 dB, respectively. A comparison of results of the two proposed schemes and several state-of-the-art techniques is depicted below, in Table 2. Results reveal that the proposed scheme offers better quality stego-images, while maintaining reversibility and a higher embedding capacity. It is pertinent to mention that [PERSON] et al.'s scheme offers more embedding capacity than the proposed scheme, but the PSNR is significantly lower.
## 4 Nanofluid Application in Machining
Tool wear is mostly related to cutting conditions [90], and various types of tool wear occur during the machining process, such as crater wear, notch wear, chipping wear, comb crack, and, most commonly, flake wear [91; 92]. The crater wear is mainly due to diffusion and abrasion, and the diffusion is activated mainly by high temperatures, so if we decrease the temperature in this region, we consequently decrease crater wear. The notch wear is caused by mechanical abrasion from harder workpieces which, in the same way as crater wear, occurs due to hard particle diffusion, influenced again by temperature. The flank wear is the most important wear that appears on the flank surface parallel to the cutting edge. Generally, it generates high temperatures, which affect tool and work material properties. Another indirect influence of temperature in the tool wear is due to the thermal expansion of materials (tool and workpiece) that occurs at high temperatures. This phenomenon causes high contact pressures between the workpiece and the tool and, as a result, promotes an increase in tool wear. To minimize the aforesaid drawback, several researchers have applied nanofluids to improve the thermal properties. Thus, it is possible to reduce tool wear meaningfully using nanofluids, as proved by [PERSON] et al. [93], who used Fe\({}_{3}\)O\({}_{4}\) with conventional coolant to improve the wear resistance up to 63% for crack wear on the rake face. Following the same goal line, [PERSON] et al. [94] developed a formulation of Al\({}_{2}\)O\({}_{3}\) with vegetable oil to reduce the flank wear up to 26.5%. In this sense, [PERSON] et al. [95] tested CuO nanoparticles with mineral oil and achieved an increased service life of the cutting tool up to 604%. In other research work, [PERSON] et al. [96] used Al\({}_{2}\)O\({}_{3}\) with soybean oil and verified that tool life increases almost 177% over the base fluid.
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 4 Nutritional Value of Seaweed
### Proteins and Amino Acids  
Among seaweeds, red seaweed contains the highest protein content, which ranges from 0.67% to 45.0% dw, followed by green seaweed (3.42-29.80% dw) and brown seaweed (5.02-19.66% dw) [49]. Specifically, the protein content of red seaweed varies, with values of 9.32% dw for _Gelidium latifolium_, 15.58% dw for _Gracilaria verrucosa_[50], and 26.69% dw for _Plocanum telfairiae_[51]. Green seaweeds such as _Caulera lentitillifera_, _Ulva rigida_, and _Ulva pertusa_ contain protein contents of 7%, 9.3%, and 21.5% dw, respectively [4,46]. The protein contents of brown seaweeds _Hizikia fusiforme_ and _Fucus vesicosus_ were reported to be 12.2% [52] and 15.1% dw [4], respectively. The protein content of seaweed also depends on the season. For example, _Palmaria palmata_ (Dulse) collected on the French Atlantic coast during winter and spring, exhibited a higher protein content (21.9% dw) than during the summer and autumn months (11.9% dw), with essential amino acids constituting 26% to 50% dw of the protein [53]. Several studies have examined the amino acid composition of seaweeds, and aspartic acid, glutamic acid, taurine, threonine, arginine, and alanine have been reported to be high in concentration. To compare, the concentrations of aspartic acid and threonine in _Ulva armoricana_, _Ulva Pertusa_, _Palmaria palmata_, and _Porphyna tenra_ were found to be much higher than in leguminous plants and ovalbumin [54]. Finally, glutamic and aspartic acids seem to be the most abundant amino acids in most seaweeds [55]. The reference nutrient intake (RNI) for protein is a 5-g portion equal to a maximum of 1.97%, 4.5%, and 2.98% dw from brown, red, and green seaweed, respectively. The digestibilityof protein in edible seaweed species ranged from 14.7% to 86.2% dw, with _Porphyra tenera_ showing the highest value [49].
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 3 Recently Announced Submarine Cable Systems  
### A Detailed Overview of SDM-Based Technology Cable Systems  
In this subsection, we refer to all known submarine cable systems [1; 2; 3; 4; 5]. Table 3 summarizes their main characteristics: capacity, fiber pairs (FPs), length, and year of release. As SDM seems to be the most promising multiplexing technology for the high-speed bandwidth submarine era, we focus on SDM-based cable systems. As expected, a common characteristic of these systems is the use of an increased number of parallel FPs inside the submarine cable. Figure 10: A typical cable ship profile. Figure 9: Types of submarine cables (e.g., SDM, testoon, uncompensated). The Dunant submarine cable system connects Virginia Beach (US) to France (French Atlantic coast), via a 6600-km cable. It is the first submarine system over Atlantic Ocean that employs a 12 FPs SDM-based design, featuring a capacity of 25 Tb/s/FP and thus a total of 300 Tb/s. Compared to previous subsea cable technologies, there are based on a dedicated set of pump lasers which are used to amplify each fiber pair, the Dunant cable system implements a common sharing of pump lasers and associated optical components, among multiple fiber pairs. This system is built by Google. Google and Orange are the managers of the landing sites in US and France respectively. Dunant has been in use since 19 January 2021. Equiano is submerged along the west coast of Africa, connecting Portugal and South Africa, and is expected to serve several countries along its route. It is the first subsea cable system performing optical switching at the fiber-pair level (instead of wavelength-level switching). Alcatel Submarine Networks is building the cable, which will travel for more than 12,000 km and deliver a capacity of 200 Tb/s to its users with approximately 20 times more network capacity than its predecessor. Equiano is expected to be ready for service in late 2022. Peace is a 25,000-km-long (15,000 km at the begging and 25,000-km with the extensions), privately owned cable system which uses the shortest direct route from China to Africa and Europe with main landing points in France, Egypt, Kenya, Pakistan, Singapore, Maldives and (recent update March 2022) indicates that the system will extend to Seychelles. The submarine system will deploy 200-G WDM technology interfacing with HMN's SDM repeater, which provides capabilities to transmit up to 25 Tb/s/FP, highly optimized algorithms, and latest-generation Nyquist subcarriers. PEACE Mediterranean segment, with landing points in Marseille, Cyprus, Malta and Egypt, has already been put into commercial use since early 2022. The Peace extension to Singaporeis expected to be ready for service in late 2023. The Hainan to Hong Kong Express (H2 HE) is a state-of-the-art SDM technology cable system, consisting of a 675-km-long cable, with landing points at Hong Kong SAR and Hainan Province, Mainland China, with a branch to Guangdong Province. The H2 HE cable system is the first 16 fiber-pair repeated submarine cable system in the world, achieving