esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Q-Gram Filter
### Optimal Count Q-Gram Filter for Edit Similarity
While empirical studies across diverse datasets have demonstrated that padding Q-grams consistently improves F-measure statistics [55; 56], prior work has lacked a rigorous theoretical framework explaining this phenomenon. We propose that the singularity behavior of Q-gram filters provides the theoretical foundation for these empirical observations. Specifically, we show that padding characters serve two complementary functions: First, they ensure non-zero Q-gram similarity at singularity points by introducing guaranteed common sub-strings at token boundaries. Second, they expand the feature space dimensionality through additional Q-grams generated from the padding-token boundary regions. This dual effect creates what we term a \"smooth Q-gram set\"--one where similarity measures transition continuously across token boundaries rather than exhibiting sharp discontinuities. The additional discriminative power provided by the extended feature space enables more robust classification, particularly in boundary cases where the base Q-gram filter approaches singularity. Another solution could be to combine feature extraction with multiple sizes of the Q-grams, e.g., unigrams, bigrams, and trigrams. The singularities of those Q-gram filters would be mutually resolved. A generalization of such a combination could be expressed as a sum of shared Q-grams over different Q-gram sizes, \(t_{1}\), \(t_{2}\), and \(t_{3}\), and their constants, \(q_{1}=1\), \(q_{2}=2\), and \(q_{3}=3\). \[ \begin{split}& t_{1,2,3}=t_{1}+t_{2}+t_{3}=\\ & 3\max\{|X|,|Y|\}-q_{1}-q_{2}-q_{3}+3-d(\alpha,|X|,|Y|)(q_{1}+q_{2}+ q_{3})\end{split}  \tag{43}\]  
and evaluating  
\[t_{1}+t_{2}+t_{3}=3\max\{|X|,|Y|\}-3-6d(\alpha,|X|,|Y|). \tag{44}\]
# Techniques for Robust Imputation in Incomplete Two-Way Tables
## 1 Introduction  
Complete data matrices are required for some statistical analysis techniques, making the imputation of missing values necessary in certain circumstances. For example, the biplot analysis used widely in multivariate exploratory analysis [1] and the additive main effects and multiplicative interaction models-AMMI [2]-used to describe the interaction between genotypes and environments, have as main tool the singular value decomposition-SVD [3]. However, this SVD is not directly calculable if there are missing values [4], and it is necessary to pre-process the information by first replacing the missing data with plausible values.  
Modern literature on incomplete information analysis recommends completing matrices using methods that employ either maximum likelihood or multiple imputation [5]. However, these procedures can depend heavily not only on probability distributions (for example, multivariate normal), but also on the missing data mechanisms [6].  
There are currently four missing data mechanisms [7]: Missing Completely at Random (MCAR), where the missing data does not depend on the feature variable being considered or any of the other feature variables in the dataset; Missing at Random (MAR), where the missing data in the feature variable is conditional on any other feature variable in the dataset but not on the one being considered; Missing not at Random (MNAR); when the possibility of a feature variable having a missing data entry depends on the value of the feature variable itself (irrespective of any alteration or modification to the values of other feature variables in the dataset); and Missing by Natural Design (MBND), where the missing data occurs because it cannot be measured physically but is relevant in the data analysis procedure [7].  
To take into account both dependence on probability distributions and missing data mechanisms, a very useful option is non-parametric imputation [8, 9]. A generalmethod free of structural and distributional assumptions was originally proposed by [PERSON] [10] and recently generalized by [PERSON] et al. [11] to complete matrices from multi-environmental experiments. The method depends on the SVD, which is a least squares technique, but a disadvantage of these types of techniques is that they are highly influenced by untypical values or outliers [12].  
To the best of our knowledge, this method has not yet been subjected to a robustness study, so our purpose here is to evaluate its performance in the face of outlier observations and to propose some strategies for robust imputations. We therefore first present an updated version of the SVD method, then consider robust alternatives for imputation, and finally describe numerical studies for the corresponding performance analysis.
# Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography
## 1 Introduction
This model determines the mathematical relationship between dot expansion and solid ink density at the optimal K value, allowing for the calculation of the optimal solid ink density based on minimum variance. Despite the above research, the accuracy of calculating the optimal solid ink density still needs improvement. Additionally, to achieve prints that align more closely with international color standards, the colorimetric value should be considered with the density value when determining the optimal solid ink density. Furthermore, the introduction of machine-learning algorithms and the establishment of a chromaticity-density correlation model can enhance the accuracy of determining the optimal solid ink density, thereby improving the color quality of printed materials. Machine learning is an effective analytical method that simulates human thinking, and when supported by appropriate datasets and algorithms, can solve complex regression or classification problems [6; 7; 8; 9; 10]. With the continuous advancement of machine-learning theory and its applications, many scholars have applied machine-learning tools to the printing industry. However, to date, related research remains limited, and performance differences between models are evident. For example, the BP neural network addresses such problems, and although it can handle complex multidimensional nonlinear relationships [11], it also has limitations, including the need for large datasets and slow training speed. To address these issues, a stable and accurate machine-learning model is required to determine the optimal solid ink density in printing production. Among various theoretical models, the Random Forest algorithm exhibits these characteristics. Random Forest is an ensemble learning method that enhances model randomness and robustness through decision tree construction [12]. In this study, the relationship between the three stimulus values L*, a*, and b*, and the optimal solid ink density of printed material color blocks is modeled using the Random Forest algorithm. Figure 1 illustrates the basic structure of the model. By inputting the standard colorimetric values, the optimal solid ink density corresponding to the current state of the printing machine is output. This serves as a reference to control ink usage in the printing industry, reduce color discrepancies in printed materials, improve subsequent color management accuracy, and enhance production efficiency.
## 3 Materials and Methods
### Low-Latency Protocol Testing--GOOSE  
The initial latency tests run with our testbed are outlined in [26]. These tests take initial GOOSE network latency measurements against two separate IEDs, a protection system and a general-purpose relay, and compare these latency measurements against those obtained using encapsulated R-GOOSE packets. In our current 5G communications tests, these R-GOOSE packets were additionally prepared and transported over 5G. Since these tests were performed against IEDs that only support GOOSE and not R-GOOSE, our evaluation additionally considered and measured the latency impact of encapsulation and extraction. Since a number of such factors contribute to the overall end-to-end latency, a total of 1000 cycles were completed for each test to ensure statistical reliability for our results.  
Latency measurements create a log-normal distribution, causing the mean value to be skewed by including samples from the upper sample range. For this reason, we collected a sufficiently large number of samples to be statistically valid and applied the Central Limit Theorem. This theorem states that the mean will be normally distributed for high sample counts. In this study, our target error margin was \(\pm 3\%\) with a 95% confidence interval. To calculate the required number of samples, the equation for normal distributions shown in Equation (1) was utilized. Here, \(p\) is the population proportion, \(z\) is the z-score of a normal distribution to achieve a 95% confidence interval (CI), and \(E\) is the targeted error margin. Since we operate without a known population standard deviation, we utilized \(p(1-p)\) instead of our population's variance \(\sigma_{P}^{2}\).  
\[n=\sigma_{P}^{2}(\frac{z}{E})^{2}\approx p(1-p)(\frac{z}{E})^{2} \tag{1}\]  
This equation is derived from the standard error equation for a normal distribution, as shown in Equation (2). Since we have an undefined population, we implement 0.5 to give the largest variance for the population. Given a desired confidence interval of 95%, we use the standard normal 1.96 for the z-score. Since 1068 samples would be required for a 3% error margin, we calculated that 1000 samples, as shown in Equation (2), would be sufficient for a 3.1% error margin, which is sufficient for our experiments.  
\[E=z\cdot\sqrt{\frac{\sigma_{P}^{2}}{n}}\approx z\cdot\sqrt{\frac{p(1-p)}{n}} \tag{2}\]  
Figure 3: Physical 5G testbed.
## 4 Results and Discussion
### LULC Classification and Accuracy Assessment  
The LULC maps for the six years of the Landsat images, 1988, 1993, 1998, 2003, 2008, and 2011, were analyzed through the maximum likelihood algorithm and improved the map qualities using auxiliary datasets (Figure 4). The classification results from the six LULC maps indicated that the agricultural class was the predominate land in the BMR throughout the study period. However, its share decreased dramatically. In contrast, urban land seems to be increased continuously due to the land demand for residential and industrial purposes. There was a slight fluctuation in the amount of miscellaneous class. Water and forest areas were relatively stable.  
This study employed the advantages of total accuracy value and the kappa value for evaluating the classified LULC maps, as listed in Table 3. The overall accuracy values of the LULC maps were observed to be 85.19, 86.25, 90.12, 86.95, 87.65, and 89.1 percent for the six time periods, respectively. Furthermore, the kappa values of the maps were 0.80, 0.82, 0.87, 0.83, 0.83, and 0.86, respectively. All values of the overall accuracy and the kappa coefficient were greater than 85 percent and 0.80, which indicated a strong perfect agreement between the classified maps and the ground truth data. Therefore, the accuracy of the LULC maps was satisfactorily accurate for investigating the future urban LULC simulations.  
Figure 4: _Cont_.
## 3 The Dataset
### Data Preprocessing and Forming the Training and Test Set  
To carry out further work with the evaluation of machine learning models, the dataset was pre-processed in two steps. First, recorded samples were filtered and rows with NaN values were removed. Next, since the sampling frequency was much higher than the signal changes, the recorded values were averaged to obtain a sample rate of 1 sample per second (the records were grouped and averaged within a second interval). After these processing steps the dataset was decreased, the summary is presented in Table 4.  
To form the final dataset used for machine learning evaluation, the obtained records were divided into a training set and a testing set. The amount of data describing Failure 1 is significantly larger than the data describing Failure 2 and Failure 3. Therefore, the data describing Failure 1 was used as the positive class in the training dataset, while the data representing Failure 2 and Failure 3 were used to create the testing dataset. For the negative class, no-fail data were used. In this case, all records except the last 15,000 were used to create the negative class in the training dataset, while the last 15,000 records were used to create the negative class in both test datasets. Therefore, the negative class contained the same set of records in both test datasets. Finally, according to the experts' knowledge, an additional attribute was generated which represents the difference between the temperature on the leak line and the temperature on the suction line, \(T_{diff}=T_{look}-T_{suct.}\). The attribute description and symbols are presented in Table 3, and dataset statistics are presented in Table 5. As can be observed, the number of samples in each class is comparable and the unbalanced rate is almost equal to 50%.
# Systematic Review on Human Skin-Compatible Wearable Photoplethysmography Sensors
###### Abstract  
The rapid advances in human-friendly and wearable photoplethysmography (PPG) sensors have facilitated the continuous and real-time monitoring of physiological conditions, enabling self-health care without being restricted by location. In this paper, we focus on state-of-the-art skin-compatible PPG sensors and strategies to obtain accurate and stable sensing of biological signals adhered to human skin along with light-absorbing semiconducting materials that are classified as silicone, inorganic, and organic absorbers. The challenges of skin-compatible PPG-based monitoring technologies and their further improvements are also discussed. We expect that such technological developments will accelerate accurate diagnostic evaluation with the aid of the biomedical electronic devices.  
skin electronic; optoelectronic device; photodiodes; phototransistors; PPG sensors; oximeters; health-care platform +
Footnote †: journal:  
_Applied_  
of devices; (iii) low operational durability under repetitive mechanical deformation; and (iv) high power consumption for long-term and portable applications, as most photoresponsive semiconducting materials comprise rigid inorganic materials in hard and bulky device architecture. Therefore, crucial considerations are the selection of sensing materials and the skin-integration strategies for PPG sensors to achieve their mechanical and functional properties.  
In this paper, we provide a systematic review of the latest developments of PPG technology for clinical applications. A brief overview of the operational mechanism and its applications of PPG sensors is first presented, followed by recent progress and technological developments of skin-compatible materials to obtain accurate and stable light-sensing performance and mechanical durability. Finally, we highlight current challenges and possible future research directions for multifunctional PPG sensors.
## 3 Results and Discussion
### In Vivo Skin Whitening Activity of Fermented CF Leaf Metabolic Extract  
The tyrosinase structure differs among mushrooms, mice, and humans; thus, the results of antityrosinase activity obtained from the nonhuman sources of tyrosinase may not reflect the real effects. Figure 6 displays the effects of the fermented extract of CF leaves on antityrosinase activity and melanin production in HEMn cells. The IC\({}_{50}\) value of in vivo antityrosinase activity calculated from the regression equation between antityrosinase activity and the extract concentration was 33.2 mg/L, which is different from the IC\({}_{50}\) value (14.1 mg/L) examined for mushroom tyrosinase (Figure 2B). The antityrosinase activity of the fermented extract was 33.6% \(\pm\) 1.5% at 5 mg/L and 58.3% \(\pm\) 2.4% at 50 mg/L (1.74 times higher). Similarly, the melanin production in HEMn cells was 63.5% \(\pm\) 2.1% and 32.8% \(\pm\) 0.6% (1.93 times lower), respectively. The curve of actual melanin production was inconsistent with that of theoretical melanin production (Figure 6). The higher inhibitory effect of melanin production indicated the involvement of additional mechanisms other than antityrosinase activity in the skin-whitening activity. [PERSON] et al. (2021) reported that CF water extracts at 0.5% reduced melanin by approximately 30% compared with the control group [12]. Together, the findings exhibited excellent skin-whitening activity for the methanolic leaf extract of CF fermented by _A. oryzae_.  
Polyphenols, such as phenolic acids and flavonoids, have high antioxidative activities due to the presence of several hydroxyl groups in their ring structures [30,41]. The fermented CF leaf extract was abundant in phenolic compounds and flavonoids (Table 1), which likely led to its high antityrosinase activity. [PERSON] et al. (2016) indicated that rutin (also present in high concentrations in the fermented CF leaf extract) can effectively inhibit tyrosinase production and block melanin precipitation [47]. Together, these findings indicate a relationship between skin whitening activity and fermented CF leaf extract, likely due to its rutin content.
## 1 Introduction  
Human Lyme borneliosis (LB) represents a multisystem disorder that can progress in stages. It is transmitted by ticks of the _Ixodes (1) ricimus_ complex that have been infected with the spirochete _Borrelia burgdorferi_ sensu lato [1]. The causative agent of LB remained a mystery until the discovery of spirochetes in the midgut of ticks collected on Long Island, New York in 1982 by the Swiss-borne entomologist [PERSON] [2]. Subsequent epidemiological and laboratory investigations led to one of the most important biomedical discoveries of the 20 th century: the establishment of LB as a new infectious disease entity [3]. Today, LB is regarded as the most important human tick-borne illness in the northern hemisphere [4]. Annual incidence rates in Europe range from 0.001/100,000 in Italy (2001-2005) to 111/100,000 in Germany and 188.7/100,000 in Slovenia (2014) [5, 6, 7, 8, 9]. According to the Centres for Disease Control and Prevention (CDC), the incidence in the United Statesof America (U.S.A.) in 2014 was 7.9/100,000, with the majority of cases reported in the northeastern and upper midwestern states [10]. Recent modelling studies based on claims data, however, suggest significant under-reporting and predict much higher annual incidence numbers for LB of \(>\)300,000 (93/100,000) in the U.S.A. and \(>\)200,000 (260/100,000) in Germany [10; 11]. Antibiotic treatment was shown to be successful in empirical therapeutic trials soon after the causative agent was correctly identified and successfully isolated in 1982 [12; 13; 14]. Subsequently, a variety of in vitro and in vivo studies have been conducted to further characterize the activity of antimicrobial agents against _B. burgdorferi_ s.l., and to determine the clinically most efficacious stage-dependent antibiotic therapy for LB [15; 16; 17]. Culture-proven treatment failures are rare but have been reported in LB patients for almost every suitable antimicrobial agent [7; 16; 18; 19; 20; 21; 22]. However, the current understanding of the persistence or possible mechanisms of resistance in _B. burgdorferi_ s.l. remains limited and is largely based on in vitro experiments performed on a relatively few isolates. Moreover, deeper insights into the interactions between antimicrobial agents and the pathogen, and into the possible phenotypic or genotypic mechanisms of resistance, as gained from animal models and in vitro experiments, are sparse. The exact mechanisms the spirochetes use to survive in LB patients with culture-proven treatment failure are not known [7]. This review summarizes what is and what is not known about in vitro susceptibility testing methods and about the in vitro interactions of _B. burgdorferi_ s.l. with antimicrobial agents and antibiotic medium preparations. In addition, open questions and current limits of knowledge surrounding possible mechanisms of persistence and antimicrobial resistance of borreliae will be addressed which continue to fuel the ongoing debate about the possible persistence of these spirochetes following exposure to antimicrobial agents.
# Fair Benchmark for Unsupervised Node Representation Learning
## 3 Unsupervised Node Representation Learning Algorithms and Datasets
### Real-World Datasets Used in FURL  
We included eight publicly available real-world attributed networks in the experiments. Their statistical information is summarized in Table 2. We will include more datasets in the future.  
**Cora, Citeseer,** and **Pubmed**[72]. These are citation networks. Nodes correspond to papers and edges correspond to citations. Each node has a bag-of-words feature vector according to the paper abstract. Labels are defined as the academic topics.  
**Flickr**[73]. It is a social-network dataset collected from Flickr. Node attributes denote the tags that reflect the users' interests. Labels represent the groups that users have joined.  
**BlogCatalog**[73]. It is a social network collected from a blog community. Nodes are web users and edges indicate the user interactions. Node attributes are generated from keywords of their blogs. Each user can register his/her blogs into six different predefined classes, which are considered as class labels for node classification.  
**Chameleon** and **Squirrel**[74]. They are page-page networks collected from Wikipedia (December 2018), based on a given topic (either chameleons or squirrels). Nodes represent articles from the English Wikipedia. Edges reflect mutual links between articles. Node  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **\# Nodes** & **\# Edges** &
\begin{tabular}{c} **Dimension of** \\ **Node Attributes** \\ \end{tabular} & **\# Labels** \\ \hline Cora & 2708 & 5278 & 1433 & 7 \\ Flickr & 7564 & 246,929 & 12,047 & 9 \\ BlogCatalog & 5196 & 171,743 & 8189 & 6 \\ Citeseer & 3327 & 4676 & 3703 & 6 \\ Pubmed & 19,717 & 44,327 & 500 & 3 \\ Chameleon & 2277 & 31,421 & 2325 & 5 \\ Film & 7600 & 26,752 & 932 & 5 \\ Squirrel & 5201 & 198,493 & 2089 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of all real-world datasets used in the experiments.  
features indicate the presence of particular nouns in the articles and the average monthly traffic from October 2017 to November 2018.  
**Film**[58]. This dataset is the actor-only induced subgraph of the film-director-actor-writer network [75]. Each node corresponds to an actor, and the edge between two nodes denotes co-occurrence on the same Wikipedia page. Node features correspond to some keywords in the Wikipedia pages. The nodes are classified into five categories in terms of the words of an actor's Wikipedia.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 2 Related Works  
Deep learning (e.g., CNN) [10; 11; 12; 13; 14] has been widely used in image SR reconstruction, and the SR performance has been significantly improved due to the powerful function of deep neural networks. Therefore, we focus on exploring deep neural network methods to solve SR problems.