esa-sceva/satcom-chunk-collection huggingface dataset:
# Soft Sets Extensions: Innovating Healthcare Claims Analysis
## 5 Discussion  
While the application of soft sets and their extensions in healthcare claims data analysis offers numerous advantages, it is essential to address the limitations and challenges associated with these methods:  
* Complexity in Integration: The integration of soft sets with fuzzy logic and its extensions introduces significant complexity. Healthcare organizations, often lacking the necessary mathematical expertise, may struggle to implement and maintain these integrated systems. This complexity poses a practical challenge to the widespread adoption of such tools in the healthcare sector. Finding qualified experts who understand both soft sets and fuzzy logic is crucial but may not always be feasible.
* Risk of Overfitting: In cases such as Neuro-Adaptive Learning and ANFIS techniques, there is a risk of overfitting when using soft set extensions. Overfitting occurs when models perform well on training data but fail to generalize to new, unseen data. In healthcare, where diagnostic accuracy is paramount, overfitting can lead to misdiagnosis or inappropriate treatment recommendations, compromising patient safety.
* Handling Indeterminacy: While IndetermSoft Sets and IndetermHyperSoft Sets are designed to handle indeterminate and uncertain data, measuring and interpreting this uncertainty is complex. The subjectivity involved in quantifying indeterminacy can lead to inconsistent outcomes across different healthcare settings, making it difficult to standardize and validate results. The lack of standardized frameworks for uncertainty quantification remains a significant challenge.
* Interpretability and Transparency: The abstract nature of soft set-based models can make them difficult to interpret, particularly for healthcare practitioners who may not be familiar with these mathematical constructs. This lack of transparency can hinder trust and adoption among healthcare stakeholders, who need to understand the basis for diagnostic and treatment recommendations. Ensuring that soft set models are interpretable and transparent is crucial for their acceptance in clinical practice.  
These challenges underscore the need for further research and development in soft set methodologies to ensure their successful integration into real-world healthcare applications. Addressing issues such as implementation complexity, computational intensity, risk of overfitting, handling indeterminacy, and enhancing interpretability will be critical for the future adoption of soft set-based tools in healthcare.
###### Abstract  
Finger vein recognition has become a research hotspot in the field of biometrics due to its advantages of non-contact acquisition, unique information, and difficulty in terms of forging or pirating. However, in the real-world application process, the extraction of image features for the biometric remains a significant challenge when the captured finger vein images suffer from blur, noise, or missing feature information. To address the above challenges, we propose a novel deep reinforcement learning-based finger vein image recovery method, DRL-FVRestore, which trained an agent that adaptively selects the appropriate restoration behavior according to the state of the finger vein image, enabling continuous restoration of the image. The behaviors of image restoration are divided into three tasks: deblurring restoration, defect restoration, and denoising and enhancement restoration. Specifically, a DeblurGAN-v2 based on the Inception-Resnet-v2 backbone is proposed to achieve deblurring restoration of finger vein images. A finger vein feature-guided restoration network is proposed to achieve defect image restoration. The DRL-FVRestore is proposed to deal with multi-image problems in complex situations. In this paper, extensive experimental results are conducted based on using four publicly accessible datasets. The experimental results show that for restoration with single image problems, the EER values of the deblurring network and damage restoration network are reduced by an average of 4.31% and 1.71%, respectively, compared to other methods. For images with multiple vision problems, the EER value of the proposed DRL-FVRestore is reduced by an average of 3.98%.  
biometrics; finger vein recognition; image restoration; reinforcement learning 2023
## 5 Simulation Results
### LPD Performance  
The objective of LPD property is to guarantee the covertness of the signal waveform, which means [PERSON] can detect the signals transmitted by [PERSON], while [PERSON] can hardly detect the presence of the transmit signals. In this section, we will investigate the detection performance at [PERSON]'s and [PERSON]'s ends in both AWGN and fading channels. The detection method is as described in the last section, and the predetermined false alarm rate is \(P_{fa}=1e-3\).  
Simulation results in Figure 7 show that detection probability at [PERSON]'s end is always superior to [PERSON]'s when the channel signal-to-noise ratio is less than 10 dB over the AWGN channel. There exists a security region depicted by the SNR, in which [PERSON]'s detection probability is approaching 1, while that of [PERSON]'s is at a low level. For example, when the SNR is in the \([-3,4]\) (dB) interval, the detection probability of [PERSON] is close to 1, while the detection probability of [PERSON] is always lower than 0.1 given \(N=10\) and \(D=4\). In practical applications, [PERSON] can adjust the transmit power so that the received SNR is always in this region, thereby ensuring the covertness of the signal.  
Furthermore, the range of the security region increases with the sub-carrier number \(N\). This means a larger bandwidth may always lead to stronger security in signal covertness.  
Figure 6: Diagram of the system model in simulations.  
Figure 7: Signal detection performance of [PERSON] and [PERSON] over AWGN channel.  
Such a conclusion is completely consistent with how the larger the spread spectrum ratio is in DSSS, the better the security is in the direct sequence spread spectrum communication system.  
Simulation results in Figure 8 show that [PERSON]'s detection performance in fading channel is basically the same as that in the AWGN channel, and the precoding scheme is proved to be effective. However, for [PERSON], the weighted factor \(\alpha_{k}\) and channel coefficients \(g_{k}\) are completely independent, and the SNR at [PERSON]'s side is significantly reduced. Therefore, the security region is wider than that in the AWGN channel.
## 1 Introduction
According to [PERSON] [27], the obtained circuit is not a model but an analogy because the same experimental data can be expressed in different equivalent circuits and, therefore, need to be adjusted carefully to correspond to a physical reality. Local near-field imaging techniques like Atomic Force Microscopy (AFM) and Scanning Kelvin Probe Force Microscopy (SKPFM) allow for the observation of surface inhomogeneities at a nanometric scale. For example, it can reveal the localization of porosity areas on the film or local passivity breaks, which make the material subject to corrosion. [PERSON] et al. [28] studied passive films formed by cyclic voltammetry on AISI 316 in solutions of Ca(OH)\({}_{2}\) and cement extract by AFM. They observed by AFM a morphology difference between the films obtained: the film formed in saturated Ca(OH)\({}_{2}\) was rough, crystalline and non-homogeneous, whereas the passive layer formed in the cement extract solution was homogeneous and completely covered the metal surface. More recently, in 2017, [PERSON] et al. [29] studied the growth of passive films on carbon steel by EIS and confirmed these results with AFM measurements. Therefore, we also decided to correlate the resistance of passive films formed on Ni-based alloys (Inconel) and stainless steel with its surface topography to better understand the passivation process. In the present study, we started by investigating films formed by cyclic voltammetry (CV) on AISI 304 and AISI 316 types of stainless steel and Inconel 600, a nickel-based alloy. In the first part of this work, we use EIS to determine how to obtain the most protective films in an alkaline solution at pH = 13. The selected solution and pH simulate the environment of reinforced concrete, as demonstrated in reference [9]. In the second part of this work, we study the evolution of spontaneously formed films on two stainless steels (AISI 304 and AISI 316) and a nickel-based alloy (Inconel 600) as a function of immersion time. We use AFM microscopy to provide additional information on the growth of passive films and to understand the EIS results. We only focused on the spontaneous formation of films in order to better simulate the actual conditions of use of these materials since CV modifies the chemical composition of the outer layer and its thickness. We notice that all the materialscould be simulated by the same equivalent electric circuit, indicating that the passivation process is approximately the same whatever the substrate is as shown also by AFM.
# A Named Entity and Relationship Extraction Method from Trouble-Shooting Documents in Korean
###### Abstract
They did not consider the relationships among the named entities in a sentence. The relationship among the named entities could be useful to precisely find parts, failures, or repair actions. Therefore, this paper proposes an approach that adopts both the NER and dependency parsing approaches, joining them with simple rules. The proposed approach showed that the extracted knowledge from maintenance documents could represent not only the meaningful entities (parts, failures, repair actions) but also their relations, such as is-part-of, is-failure-of, and is-action-of. The proposed approach was implemented using Korean Language Understanding Evaluation (KLUE) [8], a collection of eight Korean natural language understanding tasks, including NER, dependency parsing, and others. This paper is organized as follows. Previous research is explored in Section 2. Section 3 presents an overview of the proposed approach and how the pre-processed data were prepared. Sections 4-6 describe each step in the proposed approach with examples: Section 4 explains how to apply the NER approach to maintenance documents; Section 5 shows how the relationships among entities were captured by dependency parsing methods; and Section 6 proposes a rule-based approach to infer maintenance knowledge from the processed data. Section 7 shows the results of the experiment, and the conclusions and future studies are presented in Section 8.
## 4 Novel \(O(\log_{2}N)\) fully-balanced Redistribution
### Algorithmic Details and Theorems
#### 4.2.3 Rotational Nearly Sort and Split Redistribution  
**Theorem 3**.: _Given an array of \(N\) particles, \(\mathbf{x}\), and their copies to be created, \(\mathbf{ncopies}\), whose elements are evenly distributed across the \(P\) cores of a DM, Algorithm 5 (performed by each core \(p\), \(\forall p=0,1,...,P-1\)) redistributes all particles in \(\mathbf{x}\) for which \(\mathbf{ncopies}^{i}>0\), and performs that in \(O(\log_{2}N)\) parallel time for \(P=N\)._  
Proof of Theorem 3.: After the cores perform Rotational Nearly Sort first, and then Rotational Split, Equation (11) holds, as proven by Theorem 2. Therefore, the cores can independently use S-R to redistribute their \(\frac{N}{P}\) particle copies in their private memory in \(O(\frac{N}{P})\) iterations. As proven by Theorems 1 and 2, Algorithms 2 and 4 complete their task in \(O(\log_{2}N)\) parallel time, which means that the achieved time complexity by Algorithm 5 is then \(O(N)\) for \(P=1\), \(O(\log_{2}N)\) for \(P=N\) cores and, for any \(1\leq P\leq N\), is:  
\[O\bigg{(}\frac{N}{P}+\frac{N}{P}\log_{2}P\bigg{)} \tag{23}\]  
the first term in (23) represents S-R, which is always performed, and all of the steps that are only ever called once for any \(P>1\) (e.g., S-NS). The second term in (23) describes the\(\log_{2}P\) stages of Algorithms 2 and 4, during which, we update, send and receive up to \(\frac{N}{P}\) particles.  
With the results of Theorem 3 in view, we conclude that the PF tasks now take either \(O(\frac{N}{P})\), \(O(\frac{N}{P}+\log_{2}P)\) or \(O(\frac{N}{P}+\frac{N}{P}\log_{2}P)\) time. This is because (3), (4) and (8) require reduction (which notoriously scales as \(O(\frac{N}{P}+\log_{2}P)\)[36]), the IS step is embarrassingly parallel and (in the resampling algorithm) Equation (6) requires an exclusive cumulative sum; in addition, Equation (7) and resetting the weights to \(1/N\) are also embarrassingly parallel tasks. Table 1 summarizes this conclusion. This means that, even if we had an embarrassingly parallel fully-balanced redistribution, the time complexity of PFs will still be no less than \(O(\frac{N}{P}+\log_{2}P)\), since reduction and the cumulative sum are required elsewhere.
## 3 Results and Discussion
### Changes in Microbial Community During Fermentation  
The 16S rRNA gene sequencing results (Figure 3a) revealed that the predominant bacterial genera in the early fermentation stage were _Pediococcus pentosaceus_, _Lactobacillus fermentutum_, _Weissella paramesenteroides_, and _Lactococcus lactis_. Among these, _Pediococcus pentosaceus_ was the most abundant, averaging 56% of comparative abundance during the early fermentation stage. Following the addition of _L. plantarum_, there was an obvious increase in the comparative abundance of _Lactobacillus_ in the SY sample on day 7. Furthermore, the comparative abundance of _Lactobacillus_ remained more elevated among the SY samples compared to the KB samples between days 7 and 11, leading to significant differences in aromatic compounds and organic acids between the SY and KB samples (Figures 2a,b and S2). During the late fermentation stage (days 9 to 15), the relative abundances of _Pediococcus pentosaceus_ and _Lactobacillus_ continued to decrease. By the end of fermentation, the microbial composition and abundance in both samples were similar, suggesting that the exogenously added _L. plantarum_ was suppressed by disadvantageous conditions such as high alcohol concentration, nutrient deficiency, and harmful metabolites during the late fermentation stage.  
Figure 3: Relative abundances of bacterial (a) and fungal (b) flora at the genus and species levels during fermentation of CFR.  
The high-throughput sequencing results of fungal ITS genes (Figure 4b) revealed that seven fungal genera were identified in both groups, including _Saccharomyces_, _Pichia_, _Kluyveromyces_, _Trichosporon_, _Cyberlinduera_, _Rhizopus_, and _Mucor_. While seven fungal genera were identified among SY samples, the _Pichia_ genus was not found in KB samples. During the early fermentation stages, the abundance of _Saccharomyces_ gradually declined, yet it remained the dominant genus, with an average abundance of 63.29%. After the addition of _P. anomalous_ on day 6, the dominant fungal genera in SY samples were _Saccharomyces_, _Pichia_, and _Kluyveromyces_, whereas KB samples only had _Saccharomyces_ and _Kluyveromyces_ as dominant genera. The comparative abundance of _Pichia_ among SY samples rose prominently from 7.38% on day 6 to a maximum of 22.46% at the end of fermentation. _P. anomalous_ demonstrated good adaptability to the late fermentation environment and exhibited a competitive survival advantage. Compared to KB samples, the contents of phenylethyl alcohol and esters in SY samples increased by 2.8- and 5-fold, respectively. Previous studies [49; 50] have shown that genera such as _Saccharomyces_, _Pichia_, _Kluyveromyces_, and _Trichosporon_ produce flavor metabolites such as aldehydes, alcohols, and esters. Therefore, it was speculated that _P. anomalous_ enhanced the production of esters and phenylethyl alcohol in CFB.
## 2 Materials and Methods
### Minimum Detectable Difference and Student's \(t\)-Test Analysis  
Most researchers analyze the resolution from SPECT of gamma camera according to the individual FWHM of one peak profile, whereas in this study, the MDD was analyzed by two overlapped peak profiles collected from a tangent slice of the V-shaped slit with two neighboring peaks. Specifically, it is described as the minimum distance between two nearby peak centers via the Student's \(t\)-test with a constant, 1.96, which indicates that the two nearby peak centers separated far enough to create a 95% confidence level of separation [22]:  
\[|X_{1}-X_{2}|\geq 1.96\times\sqrt{\left(\frac{FWHM_{1}}{2}\right)^{2}+ \left(\frac{FWHM_{2}}{2}\right)^{2}} \tag{7}\]  
where \(X_{1}\) and \(X_{2}\) are the centers of peaks 1 and 2, respectively, while \(FWHM\) is the full width at half maximum of the specific peak. To evaluate the MDD from two nearby peaks via the unique V-shaped slit, the valley edge of two peaks was changed gradually to provide the quantitative assessment of its edge width in its original designation.  
A small MDD is always preferable for the gamma camera scan image due to its superior recognizable capability in diagnosing the spine structure. As seen in Figure 2A, two peaks can be recognized as fully separated; (B) two peaks barely passed the Student's \(t\)-test evaluation; nevertheless, in (C), the two peaks are too close to be distinguished. Note that the principle of failing or passing the Student's \(t\)-test are calculated because they are completely built on the calculation results, comparing to other reports, which are mostly stood on the qualitative discrimination of radiologists and may include human factor-biased uncertainties. Therefore, the quantified MDD can provide an auxiliary tool in justifying the performing of gamma camera scanned imaging rather than radiologists' visual ranking. However, manual judgment is still essential in the preliminary sorting stage of the Taguchi analysis.
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 3 Local Mapping on Vehicle
### Semantic Element Ranging
#### 3.2.1 Near-Ground Plane Assumption  
It is assumed that the ground can be approximated as a plane within a certain range of camera observations. This allows for a fixed-projection relationship between 2D points on the camera image plane and 3D points on the ground, which can be directly back-projected to solve the problem of monocular camera ranging for ground elements [32]. By estimating the camera pose and using the ground equation calibration algorithm based on the vanishing points of lane lines, the ranging accuracy of the ground elements can be improved.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 1 Introduction
More recently, [PERSON] et al. [19] demonstrated that HDR-VQM is the best full-reference HDR quality metric. The above studies have two major limitations. First, as all of these metrics are color-blind, they only provide an answer to the increase of luminance range but they do not consider the WCG gamut. Second, the databases used to evaluate the different metrics were most of the time defined with an HDR display only capable of displaying a BT.709 [1] gamut. The WCG gamut BT.2020 [5] is currently addressed neither by current metrics nor by current databases. To overcome these limitations, in this paper, we adapt existing SDR metrics to HDR/WCG images using uniform color spaces adapted to HDR. Indeed, most SDR metrics assume that the representation of images is perceptually linear. To be able to evaluate HDR metrics that include both luminance and chromatic information, we propose two new image databases, that include chrominance artifacts within the BT.2020 wide color gamut. This paper is organized as follows. First, we describe the adaptation of SDR metrics to HDR/WCG images using perceptually uniform color spaces. Second, we present the methodology used to evaluate the performances of these metrics. In a third part, the performances of the considered metrics are presented. Results are discussed in a fourth part. A fifth section describes our recommendation to assess the quality of HDR/WCG images. The last section concludes this paper.
# Applications of the Open-Source Hardware Arduino Platform in the Mining Industry: A Review
## 6 Conclusions
Although the accuracy may be lower than that of existing expensive instruments, monitoring sensors combined with Arduino have advantages in being low cost and having high efficiency; therefore, multiple sensors can be installed in the field. In addition, as the developer can easily customize the product, various attempts can be made, and it can be easily integrated with other analysis methods. However, as there are few applications for real mines, verification at a mine site is necessary to improve accuracy and effectiveness. A wearable system for mining workers developed using an Arduino board, various sensors, and wireless networks was proven to be useful for gas detection, air quality measurement, proximity detection between miners and machines, collapse notifications, and heart rate monitoring in an underground (mostly coal) mining environment. As the wearable system is worn by miners, it has the advantage of measuring the environmental parameters that the miners face more accurately than other systems. Therefore, if risks are identified at early stages, mine workers can take immediate action to avoid accidents. In particular, real-time monitoring of the heart rates and health of mine workers using biosensors can be considered as the most effective application of an Arduino-based wearable system as doing so with field-based or UAV systems remains difficult. However, as there are various commercially available wearable devices (e.g., smart glasses) and biosensors, it is necessary to develop diverse applications for enhancing work efficiency and securing the safety of miners. Many applications of Arduino have been reported in the literature. For example, Arduino was useful for developing initial prototypes of autonomous mine systems such as underground monitoring robots, automated buckets for LHDs, automated haul trucks, and UAVs for dust sensing and cavity monitoring. In these applications, the Arduino UNO or Mega board was integrated with various sensors for measuring gas, temperature, humidity, tilt, pressure, distance, infrared, dust, acceleration, etc. The ZigBee wireless communication module (Xbee) is often used to send data from the transmitter to the receiver. Although the Arduino-based prototype systems were tested in the laboratory, few field test results have been reported in the literature. A real mining site presents more complications than the laboratory environment; therefore, further research is necessary to use these autonomous mine systems in the real world. All the authors gathered, reviewed, analyzed and wrote the published literature; [PERSON] implemented the final work for the paper submission. All authors have read and agreed to the published version of the manuscript. This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020R1G1A1010789). The authors declare no conflicts of interest.