esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Extraction Methods
### Non-Conventional Extraction
#### 2.2.1 Microwave-Assisted Solvent Extraction  
Microwave-assisted solvent extraction (MASE) is a technique that combines microwave and traditional solvent extraction. Microwaves heat up the molecules using ionic conduction and dipole rotation. The heating up of moisture inside plant cells due to the microwave effect results in evaporation and generates enormous pressure on the cell wall, which then ruptures. This process increases the yield of phytoconstituents [39].  
When compared with conventional solvent extraction, MASE increased the anthocyanin extraction yield from blackcurrant marc by more than 20% [32; 40]. A group of factors, including microwave power, marc/solvent ratio, pH, and time, showed a significant effect on the yields of anthocyanin extraction Microwave power and time were the most dominant [32; 40].
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
### Organization  
The rest of the paper is organized as follows. In Section 2, the system model, optimization problem formulation, and corresponding optimization-driven solutions are described. In Section 3, we introduce the proposed machine learning-based antenna allocation method from overall framework structure to implementation details. In Section 4, the proposed method is evaluated to determine the parameters of the designed learning system, and its performance is compared to that of the conventional optimization-driven method. Finally, we conclude this paper in Section 5.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 5 Conclusions  
This paper compared and analyzed the prediction performance of four ANFIS models using different input space partitioning methods. As the input partitioning methods, the SC method, FCM clustering method, and CFCM clustering method were used from among the grid partitioning and scatter partitioning methods. The experimental results showed that the ANFIS4 model using the proposed CFCM clustering method exhibited a better predictive performance than the existing ANFIS models. Through this experiment, we noticed that a large number of rules was not a good thing, but an effective necessity. As a future research plan, we will study a method to improve the prediction performance by optimizing the internal parameters of the CFCM clustering method, and study a new input space division method using another clustering method.  
[PERSON] suggested the idea of the work and performed the experiments; [PERSON] designed the experimental method; both authors wrote and critically revised the paper.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1A6A1A03015496) (No. 2018R1D1 A1B07044907). This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), which was granted financial resources from the Ministry of Trade, Industry, and Energy (No. 20174030201620).  
The authors declare no conflict of interest.
## 5 Conclusions  
In creating a formalized association between LOINC and UCUM, we established an important milestone for dealing with problems related to the standardization of units of measure used in laboratory testing. Of the 176 quantitative LOINC properties, we were able to match 159 of them to a UCUM base unit representation. Building upon this, we applied this knowledge in the context of a software framework represented by the graphical user interface LUMA. We intended for it to lower the barriers of entry for standardization efforts concerning UCUM in laboratory settings. LUMA was therefore specifically meant to aid users inexperienced in either standard to map their proprietary units to UCUM via the use of LOINC codes. The drawbacks of currently available approaches with similar intentions informed the design of our project, and we were able to fully integrate the example units of the LOINC data set and various existing aggregations of UCUM units into our solution. Additionally, we outlined how the formalization of the connection between LOINC and UCUM could be leveraged to increase the data quality of the LOINC data set by verifying existing and future example unit entries. The main takeaways of the user evaluation were that future developments of LUMA should be focused on a web interface and that the ability to perform controlled unit conversions is not a peripheral feature but actually very much a point of interest. In this way, the logical next step comes down to presenting our findings to the Regenstrief Institute. Not only do we need to report our discoveries concerning the data set but we would also be very interested in feedback regarding our formalization efforts. Ideally, our results could become part of the official LOINC canon.  
The following are available online at [[https://www.mdpi.com/article/10.390/app12125848/sl](https://www.mdpi.com/article/10.390/app12125848/sl)]([https://www.mdpi.com/article/10.390/app12125848/sl](https://www.mdpi.com/article/10.390/app12125848/sl)).  
Conceptualization, C.D., K.V. and J.I.; methodology, K.V. and C.D.; software, K.V.; formal analysis, K.V.; investigation, K.V.; resources, C.D., J.I., J.K. and C.C.; data curation, K.V. and C.D.; writing--original draft preparation, K.V.; writing--review and editing, C.D.; visualization, K.V.; supervision, J.I. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the German Federal Ministry of Education and Research (BMBF) as part of the Medical Informatics Initiative Germany, Grand ID 01 ZZ1802Z.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FHIR & HL7 Fast Healthcare Interoperability Resources; \\ HL7 & Health Level 7 International; \\ LOINC & Logical Observation Identifiers Names and Codes; \\ LUMA & LOINC to UCUM Mapping Assistant; \\ RELMA & Regenstrief LOINC Mapping Assistant; \\ REST & Representational state transfer; \\ UCUM & Unified Code for Units of Measure. \\ \end{tabular}
# A Review of Super-Resolution Imaging through Optical High-Order Interference [Invited]
The super-resolved interference has been shown to be important for applications such as super-resolved phase measurement and high-resolution optical lithography. Recently, much effort has been devoted to realizing super-resolution in direct imaging schemes, and several practically achievable schemes have been developed in conventional lens-assisted imaging schemes, as well as in newly designed imaging schemes [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. These far-field super-resolution imaging methods could be quite useful in many applications such as biological imaging [33; 37], laser radar [32] and nano imaging [38], and therefore attract a lot of interest. In this paper, we give a review of the progress of the super-resolution imaging through optical high-order interference. The paper is organized as follows: we will first give a brief introduction to the Rayleigh resolution limit with a lens-assisted imaging scheme, then review the recently developed super-resolution imaging schemes based on optical high-order interference, and finally discuss future perspectives.
## 3 Data and Method
### Spatial Autocorrelations  
Additional tests conducted to answer RQ2 include the use of Moran's I, an index that depicts the spatial autocorrelation characteristics of average household income among provinces from 2005 to 2021. In this paper, the global Moran's I is employed, which typically ranges between \(-1\) and \(1\). A value greater than zero signifies positive autocorrelation, whereas a value less than zero indicates negative autocorrelation. This analysis was performed using the \"spdep\" package in R ([PERSON] et al., 2015).  
The global Moran's I measure, commonly used to assess spatial autocorrelation, offers a general indication of how similar or dissimilar values are across a geographic space. However, it is limited in identifying specific local patterns, as it summarises the entire spatial distribution into a single statistic. While it can detect overall clustering of high-value and low-value areas or the juxtaposition of high and low values, it cannot discern if both types of clustering coexist. To overcome this, Local Indicators of Spatial Association (LISA) decomposes the global Moran's I, allowing for the assessment of local spatial patterns ([PERSON], 1995). LISA can identify local clusters of high values (hot spots) or low values, as well as outliers and regions that deviate from the expected spatial pattern. This method enhances the interpretation of spatial data by highlighting local clusters where similar values are geographically concentrated (HH or LL) or where contrasting values are adjacent (HL or LH), thus providing an intricate understanding of the spatial distribution of economic activities ([PERSON] et al., 2007).
## 2 Point Cloud Data Registration Method
### Verification of the Methodology  
In order to verify the point cloud data registration method, this study took the Stanford Bunny as the research object to perform the coarse registration based on SIFT feature points, SURF feature points, and Harris feature points, and compared the quantity of extracted feature points, accuracyrates, and elapsed times [19]. Furthermore, the elapsed times and mean square errors (RMSEs) of the K-D tree improved ICP algorithm and Levenberg-Marquardt (LM) improved ICP algorithm were verified respectively in the fine registration [30].
# MIMO Relaying UAVs Operating in Public Safety Scenarios
## 4 Conclusions  
This work mainly shows the simulation of communication performance supported by UAVs relay using circular movement as a mobility model in NS3 simulator to analyze the impact on UAV relay serving users in unfortunate circumstances using sidelink protocol.  
Figure 6: Effects of increasing the number of drones; users = 4, speed = 10 m/s MIMO technique: Alamouti.  
Figure 7: MIMO techniques: 1—SISO; 2—Alamout; 3—2 \(\times\) 2 MIMO ZF.  
We conclude that some relevant parameters to be considered when developing drones for communication that use sidelink protocols are one speed and the number of users that need the connection simultaneously.  
With fewer user connections (i.e., three) it is possible to maintain approximately 80% of the packet reception rate at 60 m/s (equivalent to 216 km/h). As the speed and the number of users is increased, the network quality decreased proportionately down to 55% packet reception at 30 m/s with 6 connected users.  
Furthermore, the number of relaying UAVs in the network reduced the overall transmission rate. The packet reception dropped to nearly 75% after adding seven relaying drones to the network in our scenario. However, the results presented in both figures show that it is better to add more relaying UAVs than to increase the number of connections in one UAV to achieve the desired quality of services (QoS) requirements.  
Regarding MIMO techniques tested, a clear indication that using MIMO will improve the overall transport block transmission was not observed.
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 3 The Proposed DSME MAC  
In order to avoid collision due to simultaneous transmissions, a new channel access scheme is designed for the IEEE 802.15.4e star topology WSNs. The network model and the proposed schemes are illustrated as follows.
## 4 Results and Discussion
### Research Results
#### 4.1.2 Fuzzy Analytical Hierarchical Process to Calculate the Weights of the Indicators
By comparing the weights, it can be seen that the importance of functionality is greater than the other indicators in the first level of indicators, because if the functions of a software cannot meet the needs of users, it will be useless to users; in the second level of indicators, the importance of image restoration function is greater than the rest of the three indicators in the second level of indicators, because the function of image restoration is not only applied to artistic creation, but also widely used in photography, medical imaging, and other fields. This is because image restoration is not only applied to artistic creation, but is also widely used in photography, medical imaging, and other fields, and providing powerful image restoration functions can attract a wider user group; this study ranked the importance of the indicators using a fuzzy hierarchical analysis and factor analysis, and the results show that the conclusions from the two methods are consistent, which reflects the stability and high credibility of the data in this study.
# Decentralized Identity Authentication with Auditability and Privacy
## 3 Overview
### Overview  
With centralized identity providers such as Google or Facebook, users rely on the provider to manage their identity and log all associated authentication events. The provider can ensure that one user cannot see another user's authentication log. There are two problems associated with this setting. First, the provider has to be trusted to log all authentication events, including malicious ones. Second, the provider can see all of the logs; thus, there is no user privacy against the provider.  
Current DID-based authentication systems [17] do not solve these two problems. Although they do not rely on trusted, centralized parties, they do not enforce the logging of all authentication events. As a result, users cannot detect misuse of their credentials. Furthermore, simply moving the logs from a centralized system to the blockchain violates user privacy, as data in the blockchain are publicly visible.  
We address these two problems in this paper. We define two properties: _auditability_, which allows the user to see the complete log of all authentication events associated with their credentials, and _privacy_, which prevents linking an authentication event to the corresponding user and service provider. We propose two DID authentication protocols that achieve auditability and varying privacy guarantees. The first protocol, i.e., the baseline, stores authentication tokens to the blockchain for each authentication event. It is simple and efficient, but reveals links between authentication events and identities. The second protocol addresses the challenge in hiding user information in authentication events, by using a combination of VRF and zk-SNARK primitives. We highlight why both are needed by showing an intermediate protocol that only uses VRF and demonstrating that it fails to achieve auditability within our model. The final protocol ensures privacy, but at the cost of a high performance overhead.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 2 Related Work
### Blockchain-Based Routing Schemes  
Recently, some people combined the tamper-proof and traceable characteristics of the blockchain technology with routing algorithms to improve the trustworthiness between the routing nodes. [PERSON] et al. presented a trusted public key management framework named secure blockchain trust management (SBTM) [24]. The approach replaces the traditional public key infrastructure (PKI) with a blockchain protocol, thereby removing the central authentication and providing a decentralized inter-domain routing system. In [25], [PERSON] et al. established a multi-link concurrent communication scheme based on the blockchain technology. According to the specific integrated factor communication tree (IFT) algorithm and the behavioral characteristics of the routing nodes in the blockchain-based communication, the nodes can be classified as malicious or non-malicious. [PERSON] et al. proposed a blockchain-based contractual routing (BCR) protocol for routing networks with untrusted nodes [26]. It utilizes smart contracts to help routing nodes find a trusted route to the destination nodes. The main principle is that the source node confirms the routing arrival of each hop on the smart contract and records the malicious routing nodes with malicious behaviors. The subsequent packets will then no longer pass through a known malicious node. However, the scheme has security risks that a malicious node with the BCR tokens can falsely claim to have received the packets.
## 4 Results and Discussion
### Application of the NACA Airfoil  
An airfoil refers to the cross-sectional shape of an airplane's wings. In 1929, the National Advisory Committee for Aeronautics (NACA) studied a series of airfoils, and systematically standardized them to be suitable for specific conditions [49]. The NACA airfoils play a role in improving the aerodynamic performance of the airplane, and if applied to the hydro turbine blade, may be expected to stabilize the flow passing through the runner, and improve the performance of the hydro turbine. In particular, the maximum thickness of the NACA 4-digit series airfoil models is located at 30% of the chord from the leading edge [50]. This configuration has a similarity to the results previously derived from the optimization of the blade leading and trailing edge elliptic aspect ratios. Taking these into consideration, a NACA 4-digit series airfoil model was applied to the blade profile of the micro tubular propeller turbine, and numerical analysis was conducted to analyze and compare the performance with the reference model. The blade angle was set to be identical to the reference model. Based on the camber line, the NACA 0004 to the hub face and NACA 0006 to the blade tip were applied to keep the maximum thickness of 1.7 mm, which is the same as the reference model. Figure 16 compares the blade cross-sectional shape on the hub face of the reference, modified, and NACA airfoil model, while Table 5 describes the results of the numerical analysis.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \(\mathbf{A_{LE}}\) & \(\mathbf{A_{TE}}\) & **Power [W]** & **Head [m]** & **Efficiency [\%]** \\ \hline Prediction & & & 8.702 & 0.313 & 64.29 \\ Verification & 5.5 & 16 & 8.662 & 0.311 & 64.27 \\ Error [\%] & - & - & 0.46 & 0.55 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative results of prediction and verification.  
Figure 16: Comparison of the cross-sectional configurations of the blade on the hub (**a–c**).  
Figure 17 shows the pressure variation along the blade surface. The pressure fluctuation at the trailing edge is resolved for the modified model and the NACA airfoil model, compared to the reference model. In addition, the pressure variation line on the blade surface is the smoothest for the NACA airfoil model, so we can assume that the flow becomes stable. Furthermore, the flow becoming stable can also be determined through Figure 18, which shows the pressure distribution in the flow at the runner. In addition, as can be seen in the velocity distribution shown in Figure 19, the wake generated at the trailing edge is the weakest for the NACA airfoil, which results in the least influence from the wake.  
The cases of applying a NACA airfoil to the hydro turbine can support the results of this study. For instance, a NACA airfoil is applied to the guide vane in the Francis turbine [51; 52], and also to the runner blade [53]. In addition, there are examples of applications to the axis tidal turbine [54; 55].  
Figure 18: Pressure distribution contours at 50% span of the different models (**a–c**).
# Improving Lossless Image Compression with Contextual Memory
## 5 Experimental Results
### Evaluation on the Benchmarks
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Set** & **JPEG2000** & **JPEG-LS** & **MRP** & **ZPAQ** & **GraLIC** & **VanilcWLS D** & **Paq8 px167** & \begin{tabular}{c} **Paq8 px167+CM** \\ **(proposed)** \\ \end{tabular}  \\ \hline
**artificial1** & 1,1970 & 0,7980 & 0,5170 & 0,6730 & 0,4464 & 0,6820 & 0,3188 & **0,3186** \\
**bigbuilding** & 3,6550 & 3,5920 & \(2\) & 4,3350 & 3,1777 & 3,2430 & 3,1250 & **3,1216** \\
**bigtree** & 3,8050 & 3,7320 & \(2\) & 4,4130 & 3,4080 & 3,4680 & 3,3823 & **3,3803** \\
**Bridge** & 4,1930 & 4,1480 & \(2\) & 4,7250 & 3,8700 & 3,8420 & 3,7958 & **3,7953** \\
**cathedral** & 3,7100 & 3,5700 & 3,2600 & 4,2390 & 3,1900 & 3,3020 & 3,1539 & **3,1519** \\
**Deer** & 4,5820 & 4,6590 & \(2\) & 4,7280 & 4,3116 & 4,3760 & 4,1788 & **4,1750** \\
**fireworks** & 1,6540 & 1,4650 & 1,3010 & 1,5500 & 1,2500 & 1,3640 & **1,2324** & 1,2325 \\
**flower\_foveon** & 2,1980 & 2,0380 & \(2\) & 2,4640 & 1,7761 & 1,7470 & 1,6944 & **1,6943** \\
**hdr** & 2,3440 & 2,1750 & 1,8540 & 2,5890 & 1,9197 & 1,8730 & 1,8330 & **1,8327** \\
**leaves\_iso\_200** & 4,0830 & 3,8200 & 3,4000 & 4,7430 & 3,2630 & 3,5370 & 4,0509 & **4,0473** \\
**leaves\_iso\_1600** & 4,6810 & 4,4860 & 1,8560 & 2,604,0720 & 4,2430 & 3,2168 & **3,2130** \\
**nightshot\_iso\_100** & 2,3000 & 2,1300 & 1,8390 & 2,5760 & 1,8240 & 1,8750 & 1,7811 & **1,7805** \\
**nightshot\_iso\_1600** & 4,0380 & 3,9770 & 3,7430 & 4,2680 & 3,6610 & 3,7820 & 3,6295 & **3,6272** \\
**spider\_web** & 1,9080 & 1,7660 & **1,3490** & 2,3640 & 1,4441 & 1,4220 & 1,3498 & 1,3502 \\
**zone\_plate1** & 5,7550 & _7,4290 & 2,8340 & 5,943 We used as learning constants global learning rate \(\beta g=0.9\) and local learning rate \(\beta_{l}=0.1\) and the constants \(k\) and \(k_{v}\) were set to 0.4. The results in Tables 1-4 are expressed in _bits per pixel_ which is an image size independent absolute measure of the compression ratio. It represents the average number of bits needed to encode the pixel information from an image. It is computed as the compressed size of the image divided by the number of pixels. This is not to be confused with _bits per byte_, which measures the compressed ratio of a general file, though in our case the two values coincide since the size of a pixel in an 8-bit color depth grayscale image is one byte. The header of the compressed file should be excluded when computing the bits per pixel, but it is not always the case since the header is usually a minor payload compared to the content. However, it should be specified if the header is included or not in computing the bits per pixel so that the results can be verified.