esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### IOTA Functionality  
As noticed in Figure 4, IOTA uses a seed in order to generate multiple private and public keys, as the signing scheme of IOTA is similar to the Winternitz type signature scheme [7]. Thus, each private key should be used only once to sign a transaction, as key re-use leads to forge-ability.  
Figure 3: An indicative example of a transaction structure.  
Figure 2: IOTA’s ledger, “The Tangle”. The graph’s edges are the transactions while the vertices indicate the transactions that are referenced [5].  
To issue a transaction, the FN needs to perform 3 distinct actions: (i) sign the transaction using a unique private key and store the signature in the transaction; (ii) use the reference algorithm to choose 2 transactions; (iii) perform Proof of Work (PoW); and finally (iv) broadcast the transaction in the network. It is worth noting that in IOTA, PoW is not used to achieve consensus but rather as a spam counter-measure. When a FN receives a transaction from the network, it verifies the validity of the above steps and moreover verifies the validity of all the transactions directly or indirectly referenced. This verification includes both the validity of the transaction structure as well as the absence of conflicts. This process is conducted each time by a different FN that has different view of the Tangle, since at a particular time no FN knows all the transactions that are currently being issued in the network due to lag. Consensus is achieved by collaboratively assessing the state of the Tangle as each FN verifies a specific subset of the whole Tangle. As these subsets overlap, different FN agree on their view of the Tangle and progressively the whole system achieves equilibrium. Thus, a transaction can have different levels of acceptance by the network, depending on how many different nodes have accepted it. The system will let the users to decide what acceptance level is adequate to consider a transaction legitimate and thus proceed with the exchange of goods or services. Currently, the aforementioned mechanism remains a concept in the IOTA white paper and network consensus is achieved using a centralized architecture. The IOTA foundation runs a private special Node called \"The Coordinator\", which issues regularly special transactions that are called \"milestones\". Every transaction that is directly or indirectly referenced by a milestone is considered as accepted. This particular irregularity is also discussed in Section 6.
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 5 Conclusions  
In this paper, a frame super-resolution-based end-to-end rate control is proposed in DCVC. Different from the traditional hybrid coding framework, the key coding parameters of the multi-scale-based end-to-end rate control are predicted using various convolutional neural networks. Firstly, the original video is processed by employing the multi-scale bicubic downsampling operation to greatly reduce video data. For end-to-end rate control, the suitable bit rate ratio for every frame is predicted using the two-branch residual-based network according to the temporally encoded parameters. Moreover, the optimal \(\lambda\) can be obtained using the two-branch regression-based network according to the temporally encoded feature in order to balance distortion and the bit rate. Finally, for restoring the high-resolution frame, HEDS-Net is designed, which contains a multi-branch distillation network, the lightweight attention LCA block, and the upsampling network, to generate the detailed features of the upsampling frame. The experimental results show that the proposed algorithm can achieve a PSNR BD-rate of \(-1.24\%\) and SSIM BD-rate of \(-0.50\%\), with \(1.82\%\) rate control accuracy.  
Since video content changes generate fluctuating bit rates that affect rate control performances, dynamic data analysis techniques for video streams comprise the future subjects for rate control research. Applying analyses and reasoning in dynamic data analyses will assist the encoder in understanding and analyzing video content, video features, and video structure. Therefore, dynamic data analysis techniques will be useful for improving coding performance and the control accuracy of rate control for the future work.  
The authors declare no conflicts of interest.
## 6 Lipid-Based Nanocarriers
### Micelles  
Micelles are structures formed by amphiphilic molecules, which, in aqueous solutions, self-assemble when they reach critical micelle concentration. In micelles, the amphiphilic molecules arrange their hydrophobic regions to form an inner core, which can be used to load lipophilic drugs [114]. Micelles can effectively deliver antimicrobial agents inside the biofilm matrix and, consequently, the bacteria within it. The amphiphilic nature of micelles allows them to penetrate the biofilm's EPS, a significant barrier to drug delivery. Once within the biofilm, micelles can release their encapsulated drugs in a controlled manner, ensuring sustained therapeutic levels at the site of infection. This targeted delivery system enhances the efficacy of the antimicrobial agents and reduces the likelihood of systemic side effects [115].  
Table 4 summarizes the information concerning using lipid-based formulations to eradicate biofilms. Lipid-based nanocarriers, such as liposomes, quatosomes, and solid lipid nanoparticles, are widely used in drug delivery due to their biocompatibility and ability to encapsulate both hydrophilic and hydrophobic drugs. These nanocarriers mimic biological membranes, which generally leads to lower toxicity compared to other nanomaterials. However, toxicity can still arise from the degradation products of lipid components, the presence of synthetic surfactants, or the accumulation of nanocarriers in tissues. Ensuring biocompatibility involves careful selection of lipid types, optimizing particle size, and avoiding immunogenic responses. Continuous research is needed to balance efficacy with safety, particularly for long-term or repeated use [161].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of** & **Composition** & **Encapsulated** & **Bacteria** & **Mechanism of Action** & **References** \\
**Nanocarrier** & **Composition** & **Conceptual** & **Staphylococcus aureus** & Controlled drug release, enhanced liposomal & [162] \\ \hline \multirow{4}{*}{Liposomes} & DOPA coated with CaP & \multirow{2}{*}{Gentamicin} & _Pseudomonas aeruginosa_ & Controlled drug release, enhanced liposomal & [163] \\  & & & and _S. aureus_ & & stability \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of lipid-based nanocarriers developed to eradicate biofilms.
## 3 Results and Discussion
Figure 4 shows the fracture surface morphologies. As shown, the macroscopic fracture surface of the Al\({}_{83}\)Cu\({}_{17}\) alloy (inset in Figure 4a) shows cleavage-like features, and only a few main cracks penetrate the whole fracture surface, indicating a brittle fracture. The detailed microscopic observation evidently shows that the deformation of \(\alpha\)-Al layers is accompanied with pull-out and softening, as indicated by white arrows in Figure 4a. While the \(\theta\)-Al\({}_{2}\)Cu layers fail by a predominantly faceted cleavage fracture. As shown in Figure 4b, ultrafine lamellar eutectic composite Al\({}_{90}\)Cu\({}_{10}\) shows a complex fracture surface with many isolated dimple-like patterns and some cleavage-like features, which are fractures characteristic of the ductile \(\alpha\)-Al and brittle ultrafine lamellar eutectic matrix, respectively. For the Al\({}_{94}\)Cu\({}_{6}\) alloy, the main fracture feature is similar to the Al\({}_{90}\)Cu\({}_{10}\) alloy, but much more dimple-like patterns connect with each other and form a continuous distribution. Moreover, an apparent necking can be observed, indicating more plastic deformation. Figure 4: The microscopic fracture morphologies for alloys (**a**) Al\({}_{83}\)Cu\({}_{17}\), (**b**) Al\({}_{90}\)Cu\({}_{10}\) and (**c**) Al\({}_{94}\)Cu\({}_{6}\) are shown. The insets in (**a–c**) show the macroscopic fracture surfaces of the corresponding alloys.
# The Model of the Virtual Air Carrier as a Concept for the
## 2 Literature Review
### Model of Virtual Airline  
At the beginning of the entire scheme is a passenger who needs a ticket. For a virtual airline to be able to offer tickets for sale, it must have partnerships with other airlines (virtual code-sharing) and be a part of a global distribution system (GDS). The GDS can be thought of as one big database in which all the flight information of the contracting companies is represented. Individual airlines are then granted access to this system based on contracts with GDS. In this way, companies can offer the services of individual carriers and other entities cooperating with a given GDS. GDS systems are financed by travel service providers (mainly airlines) who pay for the display of their services offered and for the individual transactions in which their services were sold [17]. GDS is directly connected to the Internet interface of the virtual airline. The passenger can make a reservation directly on the website or via a mobile application. After entering all the necessary data and completing the order, the passenger already has a valid ticket to board the partner airline. The simplicity is original and virtual airline does not have to use traditional distribution systems, but the only model it relies on is the mobile one, which brings it profit and tries to make the most of it. Online sales bring an advantage in the form of saved costs. Mobile applications represent another platform, thanks to which it is possible to become closer to customers. The applications are set up in such a way that the user can be notified at any time about various promotions and cheaper tickets. This results in an increase of interest in booking a ticket, the user is motivated to view offers anytime and anywhere. The sale of flight tickets thus increases and brings profit. Another advantage of online bookings is that the virtual airline can obtain passenger data, which it will process and then use to segment and improve the services it offers. Online sales and sales via a mobile application also have the advantage that the customer's attention can be redirected to various advertisements on the airline's website, or to their own advertisement regarding additional services. Ancillary services may not be linked to a ticket but may represent additional services for passengers that are as important to the airline as the purchase of the ticket [18].  
The management of this entire system and the sale itself is provided by the staff of the virtual airline company, which consists of a few people, depending on the size of the business. The advantage is that such a company does not hold an AOC, it is not burdened with assets, that means it does not have to buy aircraft, thanks to which it can save costs that would otherwise go to, for example, servicing and maintaining aircraft. Its business does not have to be tied to any base airport, but it necessarily needs to have good business relations with its partners, with whom it has concluded virtual code-share contracts and, of course, IT equipment, to be able to ensure the sale of products and services for passengers.
## 3 Proposed Methodology
The incorporation of fuzzy logic allows for the handling of information recorded in natural language and built upon the expertise of professionals [34,42]. It also facilitates the treatment of multiple states of components and systems and the combination of qualitative assessments in a natural and coherent manner. The next step is to select the critical equipment in the system under analysis. This stage is mainly based on FMEA results. Critical equipment should be considered as that which causes the plant to stop in the event of a failure, as well as that which forms an essential part of the plant's operation. In conjunction with this selection criterion, alternatively, for each piece of equipment, the failure mode or modes that produce the longest recovery times to its nominal operating state (critical failure mode) are prioritized. Bearing in mind the previous steps, a series of Monte Carlo simulation experiments are executed with the objective of obtaining probabilistic distributions to represent the behavior corresponding to reliability, maintainability, and availability of each one of the assets. For this purpose, the data previously extracted from the FMECA report and the incident log are considered, and the most representative probability distributions for each system element are analyzed. The analysis of reliability and risk prioritization in industrial systems is essential for enhancing efficiency and minimizing failures. In their article, [PERSON] and [PERSON] (2023) present an integrated approach combining fuzzy FMECA and AHP for risk prioritization in vertical mills, highlighting the accuracy in assessing critical failures [42]. Similarly, [PERSON] et al. (2024) employ a comparable method to evaluate failures in mining trucks, demonstrating that the fuzzy approach effectively manages uncertainty and ambiguity better than traditional methods [43]. Collectively, these studies underscore the necessity of adopting innovative, data-driven approaches for risk management and reliability optimization across various industrial applications [4,41,42,43,44].
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
###### Abstract  
In multi-access edge computing (MEC) networks, parallelized service function chains (P-SFCs) can provide low-delay network services for mobile users by deploying virtualized network functions (VNFs) to process user requests in parallel. These VNFs are unreliable due to software faults and server failures. A practical way to address this is to deploy idle backup VNFs (BVNFs) near these active VNFs and activate them when active VNFs fail. However, deploying BVNFs preempts server resources and decreases the number of accepted user requests. Thus, this paper proposes a reliability enhancement approach that uses BVNFs satisfying the delay requirement as active VNFs to form P-SFCs, which contributes to the delay reduction and reliability enhancement. Since the resource capacities of edge servers can only deploy a certain number of P-SFCs and BVNFs, establishing how to deploy the minimum number of P-SFCs and BVNFs to satisfy the delay and reliability requirements of mobile users and maximize the number of accepted user requests is a challenging problem. In this paper, we first model the dynamics of delay and reliability caused by VNF parallelization and BVNFs deployment, then formulate the joint deployment problem of P-SFCs and BVNFs. Next, we design an approximation algorithm to deploy critical VNFs and BVNFs on a target edge server and schedule the data traffic of user requests processed by P-SFCs. Experimental results based on real-world datasets show that our proposed algorithm outperforms two benchmark algorithms in terms of throughput, delay, reliability, and resource utilization.  
network function virtualization; parallelized SFCs; BVNFs; delay and reliability
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 3 Results and Discussion
### Microscopic Test  
#### 3.5.1 SEM Analysis  
Figure 5a,b show the SEM images of 28 d slag/fly-ash-based geopolymer-stabilized macadam (GFA-4). It can be observed that the slag/fly-ash-based geopolymer produces many gelatinous substances with a dense and continuous irregularly shape, which significantly contributes the strength of slag/fly-ash-based geopolymer. Larger unbroken or incompletely broken spherical fly ash particles can be observed, which can decrease the reaction rate. Furthermore, the geopolymer consisted of a C-S-H, C(N)-A-S-H gel-generated tetrahedral network structure due to the subsequent hydration and polymerization reaction in alkaline environment. The residual quartz may act as an inert filler to fill micro-voids, thus improving material stability [27; 28; 29]. More silicon dioxide participates in polymerization and produces more calcium silicate hydrate due to the addition of fly ash [30].  
The interfacial transition zone (ITZ) of slag/fly-ash-based geopolymer stabilized macadam (GFA-4) after 28 d of curing is shown in Figure 6a,b. The result shows that no obvious porous around the ITZ was detected, while there is intrusion or overlay of reaction products with the surface of aggregates, indicating that the slag/fly-ash-based geopolymer and aggregates were well bonded. At the same time, a large amount of fly ash residues was found near the ITZ. This is probably because the adsorption of cementitious materials on the surface of aggregates causes loose and porous microstructure near the interface, which results in an uneven strength and affects the integrity of the structure [15].  
Figure 4: Dry shrinkage test change with time. (**a**) Cumulative water loss rate. (**b**) Cumulative strain. (**c**) Cumulative shrinkage factor.
## 3 Accuracy Validation for 3-D Forward Modeling Algorithm
Figure 4 shows a comparison of the forward modeling responses for the three cases. As can be seen, due to the anomaly being at a shallow depth near the surface, the maximum difference compared with the uniform half-space model response occurs at the early times, rapidly reaching its peak value, which was 34%, and then decreasing. Simply the presence of a magnetic object (\(\mu_{r}=5\)) caused more than 5% differences in the response. The above examples demonstrate the influence of the magnetic permeability on the TEM responses. Therefore, when there is a significant magnetic permeability anomaly in the subsurface, the 3-D TEM forward modeling algorithm must be capable of simulating the models with heterogeneous magnetic permeability to accurately compute such anomalous responses.
## 3 Discussion of Results
### Effect of Glass on the Density of Mortar  
The inclusion of glass in cement mortar causes a decrease in density with age, as shown in Figure 6. The density of mortar increases with the duration of hydration reaction. This implies that as calcium-silicate-hydrate (CSH) is being formed, it occupies some spaces within the microstructure and therefore causes a reduction in capillary pores [36]. A reduction in the capillary pore volume can lead to an increase in the internal density with time. It is also noted that the density decreased with the percentage of cement replacement by glass powder. There was 3.4% loss of density in 28 days in C80G20 (20% glass content). This value reduced to 0.95%, 1.82% and 2.6% when the glass content was 5, 10 and 15 wt.%, respectively. This is due to the higher value of specific gravity of cement (3.15) compared to that glass powder (2.48). Equation (1) was developed to express the density of the mortar in terms of cement content (C), glass content (G) and age. Figure 7 shows the relationship between the experimental and predicted densities with a correlation coefficient of 0.82 at a constant water binder ratio of 0.40.  
\[\rho=-217.9+630\left(\frac{\text{G}}{\text{C}}\right)^{28.1}-1.274\text{G}^{0.9 }+2435.3\text{Age}^{0.01} \tag{1}\]
## 3 Results and Discussion
### 316L Stainless Steel Electrodes for Oxyhydrogen Gas Production
#### 3.1.2 HHO Gas Production Efficiency  
Increasing an electrolyzer's gas generation efficiency has a number of important technical and financial effects. Greater efficiency translates into more HHO gas being produced for the same electrical energy, or, to put it another way, less energy being used by the electrolyzer. As a result, the process becomes more cost-effective and sustainable by lowering the energy cost per unit of HHO gas produced.  
To evaluate the efficiency of HHO gas production, the ideal volume of HHO gas (V\({}_{\text{gas}}\)ideal) in milliliters is calculated using Equation (1) [12; 29]:  
\[\text{V}_{\text{gas}}\text{ }^{\text{ideal}}=\frac{\text{I}\times\text{V}_{ \text{m}}\times\text{t}}{2\times\text{F}} \tag{1}\]  
Figure 7: Output voltage measurements at varying currents using different electrode materials.  
where I is the current (in amps) passing through the cell during time t (in seconds), Vm is the molar volume of an ideal gas under standard conditions (298.15 K and 1 atm), approximately 22,414 mL/mol, and F is [PERSON]'s constant, 96,485 C/mol.  
The real volume of HHO gas (V\({}_{\text{gas}}\)real) is calculated using Equation (2) as follows:  
\[\text{V}_{\text{gas}}\text{real}=\text{V}_{\text{gas}}\text{ measured}\times\frac{\text{T}_{\text{standard}}}{\text{T}_{\text{measured}}} \tag{2}\]  
where V\({}_{\text{gas}}\)measured is the volume of HHO gas experimentally measured via water displacement (in mL). T\({}_{\text{standard}}\) and T\({}_{\text{measure}}\) are the standard and measured temperatures (in Kelvin), respectively.  
Equations (1) and (2) are used to determine the HHO gas production efficiency, which is calculated using Equation (3) [12; 29].  
\[\text{Efficiency}=\frac{\text{Vgas}_{\text{real}}}{\text{Vgas}_{\text{ideal}}} \times 100 \tag{3}\]  
The gas production efficiency depends on the value of the passing current and the resultant gas flow rate. The bar graph in Figure 8 illustrates the HHO gas production efficiency for the different electrode materials at a passing current of 8 amps. The as-rolled electrode exhibits the lowest efficiency at 41.81%, while the as-built electrode shows a moderate improvement, reaching approximately 51.52%.  
Electrodes subjected to post-processing heat treatments (PPHTs) demonstrate significantly enhanced efficiencies. The HT1 electrode achieves an efficiency of 63.63%, whereas the HT2 electrode attains the highest efficiency at 62.11%. These results indicate that post-processing heat treatments play a crucial role in improving HHO gas production efficiency.  
Figure 8: HHO gas production efficiency using different electrode materials at a current of 8 A.