esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing
## 3 System Design and Implementation
### System Architecture
#### 3.1.3 Off-Chain Data Management  
Hospitals and genomic data providers store the actual DNA sequence files in their local databases (off-chain). Each DNA sequence file is associated with an access ticket--a secure reference (e.g., an encrypted URL or index) that is uploaded to the blockchain. By storing only these encrypted pointers on-chain instead of the raw sequences, data owners retain the ability to remove or move the actual data off-chain without cluttering the blockchain. This design satisfies data sovereignty requirements and regulatory \"right to deletion\" demands, since sensitive sequence data are not permanently written to an immutable ledger [22]. The blockchain records ensure that any access to the off-chain data is permissioned and auditable via the on-chain tickets.
## 5 Environmental Applications
### Aqueous Samples  
Aqueous sampling by SPME can be performed by direct immersion, the HS method, or the IT SPME method. Most of the papers reported HS-SPME applications to aqueous samples and subsequent GC separation combined with different detectors (see Table 4). In recent years DI-SPME followed by LC has gained popularity [21]. Derivatization techniques have been used for example for analysis of PAH and related compounds [357], phenols [337,358], organometallic compounds [360], endocrine disruptors and steroid hormones [91,365], acetone [362], and acrolein [364] in aqueous samples. In addition to commercially available SPME fibers, fibers with new coating materials have been used. They include poly(3,4-ethylene dioxythiophene) [146] and a sol-gel based amino fiber [351] for analysis of pesticides. Polyaniline [88,147], oxidized multiwalled carbon nanotubes [138], and 1-butyl-3-methylimidazolium hexafluorophosphate [124] have been applied to the analysis of phenols. Furthermore, a nanofiber coated by electrospun PU [370] was utilized for analysis of chlorobenzene.  
SPME has been used for the analysis of a wide variety of water pollutants including pesticides, PAHs, and phenols. Several publications on SPME analysis of many classes of pesticides in groundwater, lake, river and seawater, and waste water are available [146,343,344,345,346,347]. Results showed good performance of the methods with high recovery rates >70% and very low LOD, often in the range of ng/L. For example, [PERSON] _et al_. [347] applied a GC/ECD method using DI-SPME for the analysis of organochlorine pesticides in groundwater with recovery rates between 92 and 105% and LOD between 0.0013 and 0.45 ng/L [347]. Furthermore, SPME has been used to determine PAH, a large group of molecules widely distributed in the environment, and their metabolites in tap, rain, and surface waters as well as scrubber water with high recoveries (>71%) [353,354,355]. SPME has also been successfully applied to other classes of pollutants in environmental water samples, e.g., phenols [88,124,138,147,358,359], pharmaceuticals [90], endocrine disrupting chemicals and hormones [97,98,365] and organometallic compounds of mercury, lead and tin [360].  
Other fields of SPME application are the analysis of volatile and semivolatile organic compounds in waste water and landfill leachate [340] and the analysis of odorous compounds like earthy-musty odorants [334,345,346] and volatile sulfur compounds [338] in tap, river, or lake water.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 7 Conclusions  
In this paper, we presented a fuzzy-based quality adaptation algorithm that exploits video content characteristics and client-side settings to jointly optimize the user experience of HAS clients in a cellular environment. The objective of the proposed algorithm is to simultaneously meet conflicting video-quality objectives in order to optimize QoE. Simulation results revealed that the proposed fuzzy-based quality adaptation algorithm outperformed other state-of-the-art algorithms. The results demonstrate that the proposed algorithm guaranteed an improved user experience, irrespective of client playback buffer size, segment duration, and video sequences. The proposed algorithm, on average, improved video quality by over 10%, increased bandwidth efficiency by over 35%, and improved QoE by over 18%.  
In the future, we plan to extend this research to an edge cloud-based fuzzy-based quality adaptation algorithm. Furthermore, we also plan to evaluate the proposed algorithm by varying the number of competing clients, the client movement speed, and the client arrival times.  
This paper represents the results of collaborative teamwork. Methodology, W.u.R.; project administration, E.-N.H.; software, W.u.R. and M.D.H.; supervision, E.-N.H.; writing--original draft, W.u.R.; writing--review and editing, W.u.R., M.D.H. and E.-N.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The dataset used to carry out the experiments can be accessed at:  
//ftp.itec.aau.at/datasets/DASHDataset2014 (accessed on 1 June 2021).  
This work was supported by Institute for Information & communications Technology Planning&Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00818, Machine Learning Based Low Bandwidth Image Communication Edge Computing System for Proactive Anomaly Detection on Smart Plant Environment).  
The authors declare no conflict of interest.
## 3 An Outline of Metabolomics
### Metabolomic Analyses in Meat Safety Control  
In order to reduce the incidence of foodborne diseases caused by microorganisms, the WHO encourages the use of the irradiation process only if the irradiation dose is up to 10 kGy. Even though irradiation is reported as a safe and effective method to extend the shelf-life of meat and protect its hygiic quality, in the European Union only a few products are permitted to be irradiated in some Member States in substantial amounts. Especially for meat products, only chicken meat, poultry, poultry preparations, mechanically recovered chicken meat and chicken offal are proposed for irradiation in the Netherlands and France [63].  
Metabolomic analyses are expected to be a tool to monitor the impact of irradiation and could be a useful food inspection tool. Qualitative untargeted metabolomics using HPLC-high-resolution magic angle spinning (HRMS)-Q-Orbitrap analysis was used by [PERSON] et al. [46] to evaluate the changes in metabolome profiling of irradiated meat, such as chicken, turkey and mixed ground meat for sausages, regarding food safety issues relating to metabolome alteration. Four hundred and two metabolites were detected, and all three matrices exhibited a specific metabolome profile that was not affected by the application of irradiation intensities. The three meat groups exhibited the following similarities: (i) the free amino acid pool was unaffected by irradiation, (ii) taurine was the most important differentiator for all three meat groups, (iii) reduction of the glutathione level and (iv) an increase in adenosine nucleotide degradation. More specifically, changes in amino acids, monosaccharides, nucleotides and free fatty acid profile as well as the potential presence of any oxidative products could have been due to irradiation. According to this study, the irradiation did not cause changes in the main food ingredients such as the free amino acid pool. Metabolomic analysis did not determine any relevant negative impact of irradiation on meat (only alterations in a few metabolic pathways) and as a result the original quality of the meat was maintained.  
Additionally, the effect of electron beam irradiation treatment on spicy yak jerky, a typical snack meat product and local specialty in China, was investigated. According to this study, a low irradiation dose did not result in obvious changes in the protein biological value of the spicy yak jerky, but high-dose irradiation (9 kGy) decreased the protein nutrition value of spicy yak jerky. More specifically, when the spicy yak jerky was irradiated with 0, 2, 5 and 7 kGy, no significant change was observed in amino acid values, while when it was irradiated with 9 kGy a significant decrease in total amino acids was observed [64].
# A Simplified Pointing Model for Alt-Az Telescopes
## 1 Introduction
The field of view of the planet sensor is limited to 2 arcminutes in order to ensure the high frequency acquisition of images. The displacement information of the target in the planet sensor is fed to the focusing and stabilizing mirror, the corresponding high frequency swing of which in two dimensions will ensure that the target is stabilized at a fix pixel. In order to achieve swift and accurate pointing towards scientific targets, the accuracy of the pointing model should be better than 1 arcminute for both the azimuth and elevation axes. Here, we implemented a novel type of telescope pointing model. This streamlined version of the pointing model was put into practice on the PAST telescope, and the results have exhibited a significant reduction in pointing errors along both the azimuth and elevation axes. This led to rapid target acquisition and an enhancement in the overall observation efficiency of the telescope. Based on these favorable outcomes, we intend to extend the application of this model to the calibration of other alt-az telescopes. The structure of this paper is as follows: In Section 2, we describe the principles of the pointing model, while the methodology for creating the telescope pointing model is presented in Section 3. Section 4 discusses the results of calibrating the PAST telescope using the pointing model. The final section provides a discussion and conclusion.
## 3 Experimental Results and Discussion
### Security Analysis
#### 3.2.2 Key-Sensitivity Test  
Every initial condition in this process is derived from a proper pseudorandom generator; then, these data should be the same in the encryption of the plain image as well as the decryption of the cipher image; if the initial conditions are even in a decimal fraction, then the decryption process gives a different image than the original plain image. The following initial conditions organized in Table 2 were generated and used in this document:  
The result of the cipher image using the previous values is shown in Figure 7e, and its histogram in Figure 7f. In this case, very small change was given in the initial conditions mentioned above, which was applied to decrypt the cipher image for further investigation, as shown in Table 3.  
The experimental result is shown in Figure 9a,b and Figure 10a,b. As Figures 9 and 10 reveal, the process for decrypting the cipher image with different initial conditions gave a different image from the expected result. This fact occurs even if there is only a tiny change; consequently, the key-sensitivity test demonstrated that the proposed novel image encryption algorithm has a high level of sensitivity to the key.  
\begin{table}
\begin{tabular}{l l} \hline \hline \(x_{1}=0.00001661846822\) & \(x_{2}=0.00001810482068\) \\ \(x_{3}=0.00015043192106\) & \(x_{4}=0.00024400694444\) \\ \(x_{5}=0.00002680017881\) & \(x_{6}=0.00001304826999\) \\ \(l_{0}=0.00000415544568\) & \(y_{0}=0.00000174644423\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial conditions derived from user interactivity for encryption.  
\begin{table}
\begin{tabular}{l l} \hline \hline \(x_{1}=0.00001661846822\) & \(x_{1}=0.00001810482068\) \\ \(x_{3}=0.00015043192106\) & \(x_{4}=0.0002440069444\) \\ \(x_{5}=0.00002680017881\) & \(x_{6}=0.0000130482699\) \\ \(l_{0}=0.00000415544538\) & \(y_{0}=0.00000174644423\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initial conditions with small change derived from user interactivity for decryption.  
Figure 9: [PERSON] image decryption. (**a**) Decrypted cipher image with different initial conditions; (**b**) histogram of (**a**).
## 7 Acoustic Simulations  
Before simulating the acoustic parameters with the proposed projects of the new shells, the model calibration was carried out based on the existing condition [18]. The acoustic parameter taken for the model calibration was the reverberation time since the others are more sensitive to the position taken inside the seating area.  
The calibration consisted of a loop process that was repeated until the difference between measured and simulated values was within 5% across all the spectrum bandwidths [19]. The negligible difference was due to physical factors that affected the measurements during the survey, like the wind direction and the temperature variance [10]. This was considered acceptable and common for the practice.  
The software chosen for the acoustic simulations was Ramsete 3.13, which is based on pyramidal ray-tracing diffusion and supports very well the high level of reflections. Other commercial software available on the market are based on image-source principle. The difference between Ramsete and other software is the time employed for computation that, in general, is dependent on the number of the virtual microphones inserted in the model [20]; however, Ramsete can manage up to 4000 microphones with constant calculation time and can increase the resolution of the acoustic maps based on high level of microphones.  
Acoustic simulations were carried out on the digital models with three specific scenarios in order to highlight the contribution and the importance, under an acoustic perspective, of a new shell proposed by the authors (option B) over the panels currently in use (option A) that are temporarily installed until a design of a definitive shell will be in place. All the three scenarios are summarized as follows:  
* **Project A**: existing conditions.
* **Project B**: existing conditions with the option A shell. The addition of acrylic panels were floor-mounted at the perimeter of the stage while eight timber panels were suspended above the stage. These timber panels were flat, inclined towards the audience, and installed perpendicularly to the main axis of the space.
* **Project C**: existing conditions with the option B shell. The addition of the new acoustic shell proposed by the authors was composed by an outer layer of ETFE skin, that was covering two sets of diffusing panels: the vertical as side walls, and the suspended ones above the stage and stalls.  
All three projects, as shown in Figure 5, were simulated at different percentage of occupancy: 0% and 100% capacity [21]. The characteristics of the three digital models are summarized in Table 1.  
In total, 192 virtual receivers were distributed over the space, while the sound source was placed in the center of the stage. Table 2 shows the absorption and scattering coefficients of the materials considered in the acoustic simulations.
###### Abstract  
Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased.  
Keywords:information-centric networking (ICN); edge computing (EC); Internet of Things (IoT); clustering and caching +
Footnote †: journal: Applied
## 2 Challenges and Routing in 6G Networks
### Challenges of Routing
#### 2.1.1 The Complex Structure of 6G
**Blockchain**  
It is estimated that there will be an incredible explosion in the number of users and devices linked to 6G networks. There will also be an explosion in the data that will be obtained from these connected users and devices. However, the increasing digitalization of life and the increase in the number of data collected every day lead to the fact that theissue of data security is on the agenda more and more. It is also thought that Blockchain technology will be useful in managing and securing these huge data sets [49]. The decentralized nature of Blockchain technology enhances its resilience against attacks in various digital environments. In this technology, the data is encrypted, anonymized, and saved as public. Keeping the recorded data in a distributed and encrypted manner constitutes a commitment to the system's security. Blockchain technology can be employed to handle and coordinate vast quantities of data and extensive networking within the 6G network. Additionally, it can be utilized for spectrum sharing, allowing users to access the same spectrum, thereby resolving the challenge of substantial spectrum demands in 6G and guaranteeing dependable, affordable, intelligent, and efficient spectrum utilization [50; 51; 52; 53]. **Machine learning**  
The approaches of artificial intelligence, machine learning (ML), and deep reinforcement learning have managed to find a place in the center of 6G networks, as well as in the center of many industry verticals. The keystone of AI ML technology is data. They are technologies that learn from data, make inferences, and take action in this direction. It is thought that the data boom in 6G networks will also be an enormous resource for AI. Increasing coverage, integrating space-air-underwater areas into terrestrial networks, and increasing the number of users and devices connected to the network are elements that increase the complexity of the network. In combating these elements, AI is not a magic wand, but it provides a driving force. In the management of the network regarding the physical layer and higher layers [54] and in the management of resources, the need for solutions that are smarter is growing more and more. There are studies in which machine learning algorithms are used to facilitate resource management, traffic management, and routing management in networks [55]. How to compress, store, and transmit this increasing volume of data poses a new challenge. In addition, processing and storage costs must be minimized. Parameters such as the reliability of machine learning algorithms, the legal regulations for artificial intelligence, the reliability of personal data, and flexibility are important [56]. Studies continue to focus on various solutions to these parameters. In order to ensure data security, the federating learning approach is proposed. With federating learning, data is trained on end devices rather than in a public environment. By using this learning technique, the objective is to protect data security [57].
## 3 Test Results and Discussion
Furthermore, the representativeness and quality of the input data are critical to the accuracy of the artificial neural network (ANN). Unreliable data, missing variables, or biases in the dataset may jeopardize the model's reliability. Furthermore, real-world deployment may face challenges due to limited data availability and unexpected external factors like weather and consumer behavior. Tables 2-4 display the weights \(\mathrm{W}\) of the connections between the input and hidden layers, the weights from the hidden layer to the output layer, and the corresponding bias weights for both the hidden and output layers, specifically for the network configuration with 15 neurons in the hidden layer. Since the input layer has 7 neurons and the hidden layer contains 15 neurons, the resulting weight matrix for their connections has dimensions of 7 \(\times\) 15, totaling 105 weights, as detailed in Table 2. Table 3 provides the weights \
## 1 Introduction
When observations of multivariate processes are available, as is often the case in remote sensing, the additional information can be leveraged for prediction of a primary process of interest through cokriging [31; 32]. For instance, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite observes both SIF and column-averaged atmospheric carbon dioxide concentrations (XCO\({}_{2}\)). Since CO\({}_{2}\) is a reactant in the process of photosynthesis, for which SIF is a proxy, dependence between SIF and XCO\({}_{2}\) is expected. In this article, we propose a cokriging-based method that leverages any dependence between SIF and XCO\({}_{2}\) to produce de-noised and gap-filled predictions for SIF with quantified uncertainties. This reduces to a kriging-based method when SIF has no dependence on XCO\({}_{2}\), which can happen during non-productive seasons. Our cokriging method is highly parallelizable, directly accounts for non-stationary measurement errors, and offers flexible spatial resolution through aggregation of the fine-resolution Level 3 data product. Here, we apply the cokriging method to multivariate OCO-2 data over a large region in North America to illustrate coSIF, which is our 0.05-degree, monthly resolution Level 3 SIF data product that includes coherent uncertainty quantification of each SIF estimate. Compared to kriging with SIF observations only, we find that our cokriging method can reduce prediction uncertainty and improve prediction accuracy when there is dependence between SIF and XCO\({}_{2}\). The remainder of this article is organized as follows. In Section 2, we describe how OCO-2 observations of SIF and XCO\({}_{2}\) are used in the construction of a bivariate spatial-statistical model, and how this model is used to produce cokriging predictions with their uncertainties quantified. Validation strategies are described in Section 2.5, and in Section 3 the strategies are applied to the coSIF data product. We give conclusions and discuss future research directions in Section 4.