esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Consequences and Discussion
### Experimental Results on ZDT Problems
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Test Function**} & \multicolumn{1}{c}{**MOEA/D-DE**} & \multicolumn{1}{c}{**CMODE**} & \multicolumn{1}{c}{**MOCLPSO**} & \multicolumn{1}{c}{**CMPSO**} & \multicolumn{1}{c}{**CMGBO**} \\ \hline \multirow{3}{*}{ZDT1} & Mean & \(1.60\times 10^{-1}\) & \(4.21\times 10^{-3}\) & \(4.80\times 10^{-3}\) & \(4.13\times 10^{-3}\) & \(4.07\times 10^{-3}\) \\  & Std & \(1.93\times 10^{-2}\) & \(2.54\times 10^{-4}\) & \(1.76\times 10^{-4}\) & \(8.30\times 10^{-5}\) & \(2.91\times 10^{-4}\) \\  & Rank & \(5-\) & \(3-\) & \(4-\) & \(2\approx\) & \(1\) \\ \hline \multirow{3}{*}{ZDT2} & Mean & \(2.30\times 10^{-1}\) & \(7.15\times 10^{-3}\) & \(3.80\times 10^{-2}\) & \(4.32\times 10^{-3}\) & \(3.92\times 10^{-3}\) \\  & Std & \(3.07\times 10^{-2}\) & \(2.51\times 10^{-4}\) & \(3.04\times 10^{-3}\) & \(1.03\times 10^{-4}\) & \(3.25\times 10^{-4}\) \\  & Rank & \(5-\) & \(3-\) & \(4-\) & \(2\approx\) & \(1\) \\ \hline \multirow{3}{*}{ZDT3} & Mean & \(2.30\times 10^{-1}\) & \(9.12\times 10^{-2}\) & \(5.49\times 10^{-3}\) & \(1.39\times 10^{-2}\) & \(2.25\times 10^{-2}\) \\  & Std & \(2.17\times 10^{-2}\) & \(5.36\times 10^{-2}\) & \(2.49\times 10^{-4}\) & \(3.49\times 10^{-3}\) & \(4.47\times 10^{-3}\) \\  & Rank & \(5-\) & \(4-\) & \(1+\) & \(2+\) & \(3\) \\ \hline \multirow{3}{*}{ZDT4} & Mean & \(3.10\times 10^{-1}\) & \(4.90\times 10^{-1}\) & \(3.26\) & \(7.90\times 10^{-1}\) & \(5.70\times 10^{-1}\) \\  & Std & \(2.30\times 10^{-1}\) & \(3.20\times 10^{-1}\) & \(1.35\) & \(2.60\times 10^{-1}\) & \(2.20\times 10^{-1}\) \\  & Rank & \(1+\) & \(2+\) & \(5-\) & \(4-\) & \(3\) \\ \hline \multirow{3}{*}{ZDT6} & Mean & \(1.54\) & \(4.06\times 10^{-3}\) & \(3.69\times 10^{-3}\) & \(3.72\times 10^{-3}\) & \(3.64\times 10^{-3}\) \\  & Std & \(1.30\times 10^{-1}\) & \(5.19\times 10^{-4}\) & \(1.31\times 10^{-4}\) & \(1.47\times 10^{-4}\) & \(1.73\times 10^{-4}\) \\  & Rank & \(5-\) & \(4-\) & \(2\approx\) & \(3\approx\) & \(1\) \\ \hline \multirow{3}{*}{Final Rank} & Total & \(21\) & \(16\) & \(16\) & \(13\) & \(9\) \\ \cline{2-6}  & Final & \(5\) & \(3\) & \(3\) & \(2\) & \(1\) \\ \hline \multicolumn{2}{c}{Better-Worse} & \(-3\) & \(-3\) & \(-2\) & \(0\) & \\ \hline \multicolumn{2}{c}{**Algorithm**} & \multicolumn{1}{c}{MOEA/D-DE} & \multicolumn{1}{c}{CMODE} & \multicolumn{1}{c}{MOCLPSO} & \multicolumn{1}{c}{CMPSO} & \multicolumn{1}{c}{CMGBO} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Consequences on the ZDT problems.
## 1 Introduction
### Motivation, Technical Characterization and Contribution  
In summary, existing maritime path planning algorithms suffer from low accuracy and low operational efficiency, which makes the algorithms unable to support more constraints; parallel computing has been successfully used to improve algorithmic performance in the shortest path problem, but it is seldom seen in maritime minimum-risk path planning. Aiming at the above problems, this paper proposes a path planning method based on parallel computing, namely segment parallel A* (SPA*). Its technical process and characteristics are shown in Figure 1. SPA* needs to carry out a relevant risk assessment of the sea area where the route is located based on prior information, such as terrain risk, wind wave risk, and comprehensive risk. Firstly, a standard path with minimum terrain risk is generated based on A*. Secondly, the standard path is divided equally. The segments obtained by dividing the standard path are assigned to the core of the CPU through a control algorithm to run A*, respectively. In this step, we propose an adaptive heuristic function for A*. It is used to reduce A* expansion to useless grids, thereby improving algorithm performance and running speed. Finally, the planned track of each segment is spliced into a minimum-risk track from the starting point to the end point.  
The main contributions of this paper are: (1) For long-distance planning tasks, multi-constraint conditions, and high-resolution data requirements, SPA* greatly reduces computational time while maintaining performance through parallel allocation. Compared with some current parallel path planning algorithms, it is more compatible. more compatible. Whether it is the problem of the minimum-risk route or the problem of the shortest path, SPA* is easy to adjust and use directly. (2) The algorithm considers more complex meteorological conditions. We consider the stalling of ships due to wind and wave fields and propose vector risk and two constraints on IMO. This makes the sea route planning more in line with the norms of authoritative organizations and closer to reality.
## 3 Results
### Intercomparison between UTOPIA and NOAH Simulations Initialized with GLDAS
#### 3.3.2 Sensible Heat Flux (SHF)  
SHF mean and median values (Tables 15-17 and Supplementary Figures S12-S14) differ by 0-25 W m\({}^{-2}\) between NOAH and UTOPIA, NOAH values being larger (except for the CO station): these differences are compatible with SHF measurement errors using fast response instruments [53; 54]. However, individual values (daily means) differ more, as it may be seen looking at ranges and extremes. UTOPIA ranges are larger than those of NOAH. One reason is that, compared to NOAH, minima of UTOPIA are lower (10-80 W m\({}^{-2}\)), while maxima are higher (20-90 W m\({}^{-2}\)). Also considering the results of EXP1,2,3a (Tables 5-7), perhaps both models capture the mean value of daily means and can be considered able to get the climatology of the sites, but individual NOAH daily values may be considered more accurate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP2b & 71.2 & 76.1 & 61.0 & \(-\)50.2 & 215.4 & 265.6 \\ EXP4 & 113.5 & 88.5 & 121.4 & \(-\)25.0 & 252.6 & 277.6 \\ EXP3b & 66.8 & 57.6 & 68.1 & \(-\)37.6 & 169.5 & 207.1 \\ EXP5 & 118.8 & 55.5 & 132.8 & 9.3 & 211.3 & 202.0 \\ \hline \end{tabular}
\end{table}
Table 13: RNET (W m\({}^{-2}\)) simulated by UTOPIA and NOAH, initialized with GLDAS, at the CO station.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP2b & 80.9 & 75.0 & 71.8 & \(-\)46.2 & 222.7 & 268.9 \\ EXP4 & 129.9 & 82.3 & 135.0 & \(-\)21.2 & 266.1 & 287.3 \\ EXP3b & 69.3 & 55.4 & 71.1 & \(-\)33.5 & 170.5 & 204.0 \\ EXP5 & 117.4 & 56.8 & 130.5 & 8.5 & 213.1 & 204.6 \\ \hline \end{tabular}
\end{table}
Table 12: RNET (W m\({}^{-2}\)) simulated by UTOPIA and NOAH, initialized with GLDAS, at the CF station.  
Regarding the two versions of GLDAS, results are puzzling: often, but not always, GLDAS-2.1-based mean values exceed those based on GLDAS-2.0, while ranges of GLDAS-2.1 simulations are larger than those of GLDAS-2.0 ones. Ranges of GLDAS2.1 are larger than those of GLDAS2.0 in almost every site, and maxima of EXP5 (NOAH) appear truncated above 50-70 W m\({}^{-2}\) (Supplementary Figures S12-S14). For this reason, it is not clear to conclude which version would provide more reasonable values.
## 3 Methodology
### Image feature extraction
#### Size Features  
We use the following seven features to quantify the size of the nodules: area, ConvexArea, perimeter, ConvexPerimeter, EquivDiameter, MajorAxisLength, and MinorAxisLength. The _area_ and _perimeter_ image features measure the actual number of pixels in the region and on the boundary, respectively. The _ConvexArea_ and _ConvexPerimeter_ measure the number of pixels in the convex hull and on the boundary of the convex hull corresponding to the nodule region. _EquivDiameter_ is the diameter of a circle with the same area as the region. Lastly, the _MajorAxisLength_ and _MinorAxisLength_ give the length (in pixels) of the major and minor axes of the ellipse that has the same normalized second central moments as the region.
# From NFT 1.0 to NFT 2.0: A Review of the Evolution of Non-Fungible Tokens
## 2 Background and Preliminaries
### Blockchain Classification  
The most well-known blockchain classification divides them into two categories: permissionless and permissioned [19]. Permissionless blockchains allow any user to join the blockchain network, while in permissioned blockchains, nodes must be authenticated. Both categories have some benefits and drawbacks. Indeed, permissionless blockchains are considered more secure than permissioned blockchains because they have many nodes that participate in the validation of transactions. However, they usually have long transaction processing times due to a large number of nodes and the large size of the transactions [19].  
In the literature, four types of blockchain structures have been identified [20] (see Figure 2). We provide a comparison of the blockchain structures in Table 1. Briefly, the four blockchain structures are the following:  
* **Public blockchains**. Public blockchains are permissionless and completely decentralized, and are primarily used for exchanging and mining cryptocurrencies, such as bitcoin and Ethereum.
* **Private blockchains**. Private blockchains are permissioned blockchains fully controlled by a single organization. A famous private blockchain is Hyperledger [21].
* **Consortium blockchains**. Consortium (or federated) blockchains [22] are permissioned blockchains governed by a group of organizations instead of only a single organization; therefore, they are more decentralized than private blockchains.
* **Hybrid blockchains**. A hybrid blockchain is defined as a blockchain that attempts to use the best part of both private and public blockchain solutions. Indeed, hybrid blockchains are controlled by a single organization, but with a level of oversight performed by the public blockchain, which is required to perform certain transaction validations.  
Figure 1: Blockchain structure.  
Both private and public blockchains have drawbacks: public blockchains tend to have longer validation times for new data than private blockchains, and private blockchains are more vulnerable to fraud and bad actors. To address these drawbacks, consortium and hybrid blockchains have been developed. Current NFT projects are proposed on public blockchains, which are preferred, considering that all the projects are public and they try to include a huge amount of users. However, there are many benefits to running an NFT platform on a private chain. First of all, it allows the NFT team to have control over the entire blockchain with fewer risks concerning decentralisation. Furthermore, it allows them to use the chain and token as payment methods for their services. Additionally, private blockchains benefit from a lower running cost, faster transaction confirmation, and increased data privacy.
# Continuous-Service M/M/1 Queuing Systems
## 1 Introduction and Motivation
Real customers have service priority over imaginary customers. Hence, upon service completion of a customer, the server will always first attend to a real customer over an imaginary one if there is at least one real customer waiting in queue; an imaginary customer is only and always (since infinitely many imaginary customers waiting in queue and hence rendering continuous service) attended to if there are no real customers waiting in queue. Note that service times for real customers and that for imaginary customers follow the same exponential distribution. As follows, we provide two motivating examples that may be modeled using our systems. * A receptionist answers phone calls when there is no customer physically present to attend to. The switchboard is always busy and there are an infinite number of calls waiting (imaginary customers). The receptionist will have to finish answering a call before attending to a customer who physically shows up during the call service (real customers). The receptionist only gets to answer calls when no customer physically waits in queue;
* An industrial robotic arm sorts a number of types of parts, determining which belongs to what group by using an overhead camera for assessment before each action. One may in this case consider that one particular type of part (real customers) has priority over the other type of part (imaginary customers). In this paper, we conduct the steady-state analysis of, and determine common performance measures of the continuous-service M/M/1 queueing system. In addition, we carry out simulation experiments to verify our results. We compare our results to that of the standard M/M/1 system, and draw interesting conclusions. The remainder of the paper is organized as follows. Section 2 derives the steady-state distribution of the continuous-service M/M/1 queueing system. Section 3 determines the average number of customers in the system and the average number of customers in the queue; Section 4, on the other hand, studies the system and queue time distributions. Finally, we offer concluding remarks in Section 5.
## 7 Conclusions  
This paper presents experimental results obtained for HEVC compressed HFR video frame size traces for the first time in a multifractal domain. In the analysis, it is presented that HFR trace sequences manifest long-range dependence and multifractal behavior. In comparison between temporally recovered UHD 120 fps HFR and corresponding nonrecovered or original HFR data, lower Hurst indices are obtained, as well as often wider multifractal spectra. By analyzing spectra for different crf compressed sequences, it is assumed that it is possible to differentiate TRR signals from the original ones. The proposed WkNN approach was able to detect recovered video data, where the Mahalanobis measure was applied. Also, the feature vector is of low length, and features are extracted as nonreference. Input can be a TRR sample, which can be detected without prior assumption related to constant rate factors and without direct comparison between modified and original sequence. The proposed detection approach gave above 98% in accuracy during the cross-validation.  
Overall, the differences between TRR and original HFR video are not easy to notice, even though Hurst indices like the ones calculated using R/S statistics show these differences. Multifractal spectra and their characteristics are also indicative in differentiating between the two groups consisting of various content and motion. By examination of the reference UVG dataset included in the video trace analysis, this research shows that multifractal descriptors and the trained model may be adequate for detection. The model enabled high-accuracy results regardless of compression rate or content. However, further development of the proposed model should be oriented towards other distortion possibilities in HFR domain.  
Integrity and authentification issues may arise, and the approach may be useful in TRR or frame rate upconversion detection. Namely, there are a lot of challenges associated with HFR, and HFR needs to be properly addressed in order to truly realize its potential. The obtained results in this work can be considered valuable for future research. It can be concluded that HFR represents a significant advancement in the field of video technologies, and tracing analysis is important for dealing with specific behavior that HFR brings.  
This work was partially supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, no.: 451-03-47/2023-01/200103.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data analyzed are available in a publicly accessible repository at [[https://ultravideo.fi/](https://ultravideo.fi/)]([https://ultravideo.fi/](https://ultravideo.fi/)) (accessed on 1 May 2023) [23]. Additional data are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
Whenever a new slice request arrives, it is  
Figure 1: Types of slices in 5G network. also given to a machine learning module to decide whether the request is admitted or not by checking the status of the RAN and core network resources. If enough resources are available then the slice is admitted successfully and otherwise rejected. Hence, our system can automate the slice creation process completely. To summarize, the following are the major contributions of our work:  
* An intent-driven approach for the automated E2E slice configuration and the resource orchestration is the main contribution of our system. It induces a generic mechanism that can orchestrate resources at multiple domains. Additionally, it abstracts the underlying platforms and enables the configuration of different vendor-specific platforms. A few of the example platforms are CORD, OSM, and FlexRAN. Additionally, the best thing about this mechanism is that it eliminates the manual effort of work for creating, updating, and deleting network slices. Hence, it completely automates the lifecycle management of network services while keeping the IETF standard-based intent lifecycle mechanism. * For the lifecycle management of intents, continuous monitoring is essential. Hence, while having been limited to open-source platforms this system introduced the monitoring and management of both the access and core network resources. * The standards for network automation insist on introducing intelligence into different domains of the networks. Hence, AI-driven slice resource assurance is achieved by using the GAN deep learning model. The organization of this article is as follows. Section 1 presents the brief introduction of the proposed system. In Section 2, background literature related to core and RAN network slicing is explained. The design and architecture of the E2E network slicing system are presented in Section 3. The components of the slicing system are well explained in Section 4. Section 5 explains the implementation of the proposed system and its working principle. Section 6 presents the slice instantiation process with the implemented test-bed. The performance results generated by the proposed system are discussed in Section 7. Section 8 presents the discussion of the proposed system with existing works. The final section contains the conclusion and future work of the presented work.
## 6 Conclusion  
In this paper, we have presented a medium access protocol, called TDMA/CA, providing bandwidth spatial reuse and ensuring the absence of conflict when only links of good quality are used, links are stable and symmetric. This protocol schedules medium access according to node colors provided by SERENA. We have pointed out that the use of bad quality links leads to collisions even in case of a stable topology with symmetric links. Such conflicts would also occur in any slot assignment algorithm providing spatial reuse based on node neighborhood. With data gathering applications, the tree-based coloring enables optimization of the number of colors and the end-to-end delays. Concerning energy efficiency, TDMA/CA and SERENA reduces the duration of the active period. Depending on the application requirements, the duration of the inactive period is increased or the reactivity of the application is improved by a more frequent active period. In case of mobility, late node arrival, topology changes, or asymmetric links, color conflicts can occur. TDMA/CA is able to detect the conflicts causing data losses. Such conflicts are tolerated as long as application requirements are met. In the negative, conflicts detected by TDMA/CA are notified to SERENA which is in charge of solving the color conflicts. Currently, we are implementing SERENA and TDMA/CA on a real wireless sensor network.
## 3 Biosurfactants
### Application of Selected Biosurfactant Cosmetics
#### 3.3.1 Glycolipids  
Glycolipids are the most common and desirable group of biosurfactants in the cosmetic and pharmaceutical industry, with great stability throughout a wide range of pH levels,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Class** & **Biosurfactant** & **Microorganism** & **Ref.** \\ \hline \multirow{8}{*}{glycolipids} & \multirow{8}{*}{rhammolipids} & _Pseudomonas_ sp., _ Bacillus_ sp., _Renibacterium_ sp., _Enterobacter_ sp., _Acinetobacter_ sp., _Plasococcus_ sp., _Lysinobacillus_ sp., _Microbacterium_ sp., _Stentropleomonas_ sp. \\ \cline{2-3}  & & \multirow{2}{*}{trehalipids} & _Rhodococcus erythropolis_, _Artirobacter_ sp., _Mycobacterium_ sp., _Coryobacterium_ sp., _Gorpheuclearium_ sp., _Bordetella hinizi-DAF1_ \\ \cline{2-3}  & & & _Candida_ sp., _Trichosporon asahi_, _Starmerella bombicola_, _Wickerhamiella domericiae_ \\ \cline{2-3}  & & \multirow{2}{*}{cellobiozolipids} & _Ustilago mavdys_ & [94] \\ \cline{2-3}  & & & _Senudozyma_ sp., _Ustilago scitaminea_, _Candida_ sp. \\ \cline{2-3}  & & & _Candida_ sp. _Pseudozyme_ sp. & [97] \\ \hline \multirow{8}{*}{Lipoproteins} & \multirow{8}{*}{Lipoproteins} & \multirow{8}{*}{surfactin} & _Bacillus subtilis_ & [98, 99] \\ \cline{2-3}  & & & _Sucillis_ subtilis & [87] \\ \cline{2-3}  & & & _Serratia_ & [100] \\ \cline{2-3}  & & & _marcscens_ & [100] \\ \cline{2-3}  & & & _iichensin_ & _Bacillus licheniformis_ & [100] \\ \cline{2-3}  & \multirow{2}{*}{Fatty acids and phospholipids} & _Corynobacterium_ & _Icervidia erythropolis_ & [100] \\ \cline{2-3}  & & & _Athetabader_ sp., _Pentilliarum_ spiculsiporum_ & [100] \\ \cline{2-3}  & & & _Acinetobacter_, _calcuceticus_ _BD_ 4\(3\) & [101] \\ \cline{2-3}  & & & _Alastan_ & _Ahatetobacter radioresistens KA 53_ & [89] \\ \cline{2-3}  & Polymeric biosurfactants & & _Ahatetobacter calcoaceticus A2_ & [87] \\ \cline{2-3}  & & & _manoprotein_ & _Saccharomyces cerevisiae_ & [102] \\ \cline{2-3}  & & & _liposan_ & _Candida lipolitica_ & [89] \\ \hline \multirow{8}{*}{Molecular biosurfactants} & \multirow{8}{*}{outer membranevesicles} & \multirow{8}{*}{_Acinetobacter calcoaceticus_ (OMV)} & \multirow{8}{*}{[100]} \\ \cline{2-3}  & & & _PM-factor_ & _Pseudomonas marginalis_ \\ \cline{1-1} \cline{2-3}  & & & _whole cells_ & _Cyanobacteria_ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Division of microbial biosurfactants according to their chemical composition and particle size.  
temperatures, and salinities [71]. They are composed of carbohydrate moieties bonded to fatty acid chains of varying lengths. Among the glycolipids, the most commonly used are rhamnols, trehalolipids, mannosyl erythroid lipids (MELs), mannosyl mannitol lipids (MMLs), cellobiozolipids, and sophorolipids [106].