esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Lipid metabolism is entwined with the circadian machinery, and its alterations are closely associated with circadian disruption [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. One factor that causes deterioration in circadian rhythms is glaucomatous damage to retinal ganglion cells (RGCs), which compromises light perception and signal transduction to the master clock (suprachiasmatic nuclei in the brain). RGC damage was found to be associated with disrupted local [16; 17] and systemic [18; 19; 20; 21] circadian rhythms, as well as sleep [22; 23]. Circadian rhythms of lipid metabolism are modulated by light and food [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Furthermore, light and the extent of the day-night intensity differences modulate chronotype, sleep parameters, and circadian alignment [24; 25; 26; 27; 28]. This also occurs in glaucoma [29]. Seasonal differences in serum lipids [30; 31] can in part be accounted for by differences in ambient light conditions [32; 33]. There are substantial differences in circadian and seasonal phases in the rhythms of different serum lipids [31]. Recently, phase response curves (PRCs) of circadian lipid rhythms to light and food that appeared to differ between lipid fractions (e.g., LDL-C failed to demonstrate a PRC) were characterized [15].  
On the other hand, dyslipidemia is associated with an increased risk of glaucoma and elevated intraocular pressure (IOP) [34]. Specific associations of glaucomatous damage and RGC loss with changes in total cholesterol, its low-density (LDL-C) and high-density (HDL-C) fractions, and triglycerides, (TG) were previously reported [34; 35; 36; 37]. Taken together, changes in lipid metabolism can be both a factor predisposing to RGC damage in glaucoma and a consequence of RGC loss, which reduces the amplitude of perceived daylight photic information and compromises circadian rhythms.  
To the best of our knowledge, data on lipid metabolism collected at different day times in relation to RGCs loss in glaucoma are currently lacking. Therefore, this study investigated how changes in serum lipids, assessed at different times of the day, depend on RGC loss that occurs with glaucoma. The role of clock genes, melatonin receptor gene polymorphisms, and salivary melatonin profiles was assessed in a subgroup of patients.
## 3 Results  
### General Characteristics of the Studies  
A total of 849 studies were identified from the databases and no items were included from other sources. After removing duplicated articles from the different databases, 605 titles and abstracts were screened, 593 were excluded, and 12 were screened as full texts. Finally, statistical analysis was performed on 8 studies [9; 10; 11; 22; 26; 27; 28; 12] (Figure 1). Table 1 provides an overview of the participants' characteristics of the studies included in the quantitative analysis. The total participants was 190 (males and females), mostly trained or active subjects. The mean age ranged from 20.5 \(\pm\) 1.3 to 39.2 \(\pm\) 5.3 years (men: 21.8 \(\pm\) 0.3 to 39.2 \(\pm\) 5.3; women: from 20.5 \(\pm\) 1.3 to 35.0 \(\pm\) 7.0). Training experience was reported in some articles and it ranged from 11.3 \(\pm\) 3 to 15.0 \(\pm\) 8 years. In addition, VO2 max values were between 35.0 \(\pm\) 5.0 and 66.7 \(\pm\) 5.9 mL/kg/min. Figure 1: PRISMA flow diagram for studies included. The intervention programs were eight weeks long [10; 11; 26; 27; 28; 12], except for [PERSON] et al. [9] (four weeks) and [PERSON] et al. [22] (15 days); the frequency of training was between 3 to 6.3 sessions per week. Regarding the distribution of time by intensity zones, the studies that reported these variables ranged from 49 to 84% in zone 1, from 12 to 39% in zone 2, and from 3 to 13% in zone 3 [10; 11; 12; 26]. The predominant method of analysis for HRV monitoring was time domain, followed by frequency domain. Remarkably, only one study used nonlinear measures (Table 2). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
M = men; \(W\) = women; SH = sleeping in hypoxia; BP: block periodization; TP: training periodization. * There were 9 dropouts, gender is not specified.  
\end{table} 
Table 1: Characteristics of the studies included in the meta-analysis.
# A Comparison of Faulty Antenna Detection Methodologies in Planar Array
###### Abstract  
Broadcasting, radar, sonar and space telecommunication systems use phased arrays to produce directed signals to be transmitted at the desired angle. This system requires a large number of antenna elements. The presence of faulty element(s) in an array causes asymmetry, which results in a deformed radiation pattern with higher sidelobe levels. Higher sidelobe levels indicate waste of energy by transmitting and receiving signals in unwanted directions. Hence, it is important to develop a method that detects faulty elements and corrects the radiation pattern. To correct the failed radiation pattern, failed elements in an array must be identified first. There have been various studies conducted on linear array failed radiation pattern correction and the finding of faulty elements, but investigation on the planar array is limited. Further, the optimization suggested for linear arrays does not necessarily work for the planar array. In this study, Planar array faulty antenna detection was developed with pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO) methods by reducing the Signal to Noise Ratio (SNR) use the objective function. The analysis was varied for 8 \(\times\) 8 and 6 \(\times\) 6 planar arrays with different types of failures. The results were compared to find the best method to identify the faulty element's location in a planar array. The pattern search method produced outstanding results in finding the faulty element's locations by providing 100% accuracy for all types of failure, while other methods failed to do the same.  
Keywords:failed elements; planar array; optimization; radiation pattern; simulated annealing; particle swarm optimization; pattern search method +
Footnote †: journal:
# On the Use of Image Analysis for Hematocrit Evaluation in Dried Blood Spots
###### Abstract  
Dried blood spots (DBSs) are formed by collecting a small sample of blood on specialized filter paper and allowing it to dry naturally. Various domains of life sciences and drug research extensively use DBSs as a sampling technique. The \"Hematocrit (Ht) effect\" affects assay bias, and several strategies have been put forth to deal with it, including the correction of quantified concentrations using an appropriate correction factor. The approach was previously applied, following the utilization of an image processing algorithm developed in Matlab[9] to derive a reliable equation correlating DBS areas to Ht% values. The present work looks more closely at the application of image analysis to the evaluation of Ht in DBS samples. Utilizing image analysis software, DBS samples with known Ht values were processed. Preparation of cards has followed a previously developed protocol for the appropriate formation of uniform area DBSs, irrespective of Ht. The resulting areas showed close resemblance to the respective theoretical areas calculated by applying the correlation equation. Following that, the equation was utilized to determine the Ht values for each sample, and a comprehensive comparison of measured versus calculated Ht was carried out using various statistical approaches for method comparison. The results demonstrated a strong correlation, suggesting the method's viability in estimating Ht for unknown DBS samples.
## 3 Materials and Methods
### Problem Definition
: _Snapshot Trajectory Point \(k\)-NN (\(STPkNN\)) query: Given a query center point \(q\) at timestamp \(t_{i}\), an integer \(k\) and a dataset of trajectory (spatio-temporal) points denoted as \(\mathbb{S}^{t_{i}}=\{tra_{1}^{t_{i}},tra_{2}^{t_{i}},tra_{3}^{t_{i}},tra_{N}^{t _{i}}\}\)a k-NN query, denoted as \(STPKNN(q,S^{t_{i}},k)\), asks for the \(k\) spatial points of \(S^{t_{i}}\) whose \(l_{2}\) distance from the query point \(q\) is less than that of the rest points of \(S^{t_{i}}\). Definition 6: Continuous Trajectory Point \(k\)-NN (\(CtpkNN\)) query: Given a query center point \(q\) at timestamp \(t_{i}\), an integer \(k\) and a dataset of trajectory (spatio-temporal) points denoted as \(S^{t_{i}}=\{traj_{1}^{t_{i}},traj_{2}^{t_{i}},traj_{3}^{t_{i}},\dots traj_{N}^{t_{ i}}\}\) at a time-interval \(t=\{t_{i}\}_{i=1}^{L}\) for \(L\rightarrow\infty\) (practically large enough), a continuous \(k\)-NN query, denoted as \(CTPkNN(q,S^{t},k)\), asks for the \(k\) spatial points of \(S^{t_{i}}\), whose distance from the query point \(q\) is less than that of the rest points of \(S^{t_{i}}\), for all consecutive \(t_{i}\in t\). Note that, \(traj_{1}^{t_{i}}\equiv pos_{1}^{t_{i}}\). Considering the previous definitions, the problem formulation for the application of robust \(CTPkNN\) queries (see Figure 1) will be considered in the following subsection. In the context of the proposed approach, all points \(q\in S^{t_{i}}\) are selected as query objects in order to acquire the \(k\) nearest neighbors of all mobile objects in each timestamp \(t_{i}\). If the process is repeated for a high number of consecutive timestamps in a selected time period, then, the collection of \(STPKNN\) queries constitutes a \(CTPkNN\) query, thus similar to our case.
## 2 Basic Theory
### Attribute Reduction for Neighborhood Rough Sets  
Feature importance is a crucial basis for feature selection. In forward-type algorithms, the importance of a candidate feature relative to a subset of features depends on the change in approximate classification quality caused by adding that feature. Let \(B\subset C\), for any \(a\in C-B\), the importance of \(a\) relative to \(B\) in the neighborhood approximation space (U, N) be expressed as  
\[\gamma_{B\cup\{a\}}^{\delta}(d)=\operatorname{Card}(POS_{B\cup\{a\}}^{\delta}( d))/\operatorname{Card}\left(U\right)\geq\operatorname{Card}(POS_{B}^{\delta}(d))/ \operatorname{Card}\left(U\right)=\gamma_{B}^{\delta}(d) \tag{12}\]  
Figure 1: Schematic diagram of support vector machine classification. (**a**) two-dimensional space; (**b**) higher dimensional space.  
The objective of neighborhood rough set feature selection is to find an attribute subset that has the same classification capability as the original data while excluding redundant attributes. Although there are usually multiple reductions for a given decision table, finding one of them is sufficient in most applications.  
To ensure that the importance of smaller value features is not overshadowed by larger value features during the classification process, we normalize each feature. Based on attribute importance indicators, we construct a greedy algorithm for feature selection. The basic idea of this algorithm is shown in Figure 2.  
The reduction process is roughly described: the reduction set starts with the empty set, each time adding an attribute to the reduction set and calculating the attribute importance of the temporary reduction set after adding the attribute \(a_{i}\). From these temporary reduction sets, the set with the greatest attribute importance is selected. Judge whether the maximum attribute importance \(sig(red\cup a_{k})\) is greater than the importance threshold \(\varepsilon\) set by us. If it is, add the attribute \(a_{k}\) to the collection \(red\) and update it; if it is less than or equal to \(\varepsilon\), exit the program.
## 2 Background Information
### Application Scenarios and Data Characteristics
On one hand, if we assume that a specific amount of data is compressed by \(x\%\), the respective data volume reduction can be correlated to analogous reduction of radio utilization resulting in significant energy conservation. On the other hand, the \(x\%\) compression percentage can also be envisioned as analogous reduction of bandwidth requirement and thus effectively increasing the limited wireless channel utilization while reducing the resulting data transfer delay.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 5 Evaluation Result
### Effect of Topology
#### 5.3.3 Large-Scale Network  
In this subsection, we analyze the performance of FTSP, GTSP, and E-FTSP in term of network scalability, which is an important criterion for network protocols. Network scalability can be defined as the overall network performance may not degrade regardless of the network size and the number of nodes. Since the effect of the network density is discussed in Section 5.3.2, this subsection focuses on the effect of the network size, especially the number of nodes, by fixing network density for all experiments. A random topology was used as same as previous evaluations, but the number of nodes is increased from 75 to 1200 nodes as a logarithmic scale. Please note that the network size is also enlarged to maintain the same network density for each experiment. As explained in Section 5.3.1, the synchronization errors of FTSP are accumulated through multi-hops from the reference node. Therefore, we marked the number of nodes together with the maximum hop distance between the reference node (located at the center of the network) and the farthest node in x-axis of Figure 13.  
Figure 13a shows that network synchronization error of FTSP grows exponentially with network size (hop distance), whereas that of GTSP only slightly increases. This indicates that GTSP is not affected by hop distance due to its inherent design of the distributed algorithm. Remarkably, the synchronization error of E-FTSP tends to be constant with increasing network size. This proves that E-FTSP minimizes the effect of error accumulation through multi-hops.  
Figure 13b describes the average number of rounds required to complete synchronization of each network. It indicates that to achieve less than \(100\,\mathrm{\SIUnitSymbolMicro s}\) accuracy the convergence time of GTSP increases proportionally to the network size, while those of FTSP and E-FTSP only slightly increase. It proves that the convergence time of GTSP highly depends on the maximum hop distance of a network. In contrast, the convergence times of FTSP and E-FTSP only depend on the hop distance  
Figure 11: (**a**) Network error and maximum neighbor error of GTSP with different topologies. (**b**) Number of rounds required to synchronize entire network in GTSP with different topologies.  
Figure 12: Number of rounds required to synchronize entire network in GTSP with different transmission ranges.  
between the reference node and the farthest node. Because the reference node is located at the center of the network, both FTSP and E-FTSP have tendencies to increase in convergence time as network size increases, but the impact is relatively small compared to that of GTSP.  
In summary, FTSP has tendency that time synchronization error increases for the large-scale network while GTSP requires long convergence time proportionally to the network size. Therefore, neither are suitable for operating in large-scale networks. In contrast, the simulation results prove that E-FTSP provides high accuracy and fast synchronization regardless of network scale.
## 1 Introduction
Furthermore, wireless and monitoring devices are increasingly used in healthcare and public health practice for communication, data collection, patient monitoring, and education to enhance adherence to chronic disease management. Sensors and context-awareness features allow real-time information collection [44]. Digital platforms using motivating messages, prompts/push notifications, coaching, and/or skills training for people have demonstrated increased engagement in treatment and self-management. Interventions delivered with these technologies can also accommodate easy and simple access to information and encourage users' participation and literacy levels [47; 48]. According to previous reviews, digitally delivered interventions may enhance the self-management and clinical outcomes of chronic diseases [36; 49; 50; 51; 52]. Currently, digital approaches are used by subgroups with historically impaired outcomes, but are limited by access to certain types of devices or the Internet [36; 49; 52]. Most digital health solutions are focused on a specific disease, such as diabetes or obesity, and fewer provide comprehensive multiple conditions services and tools [36; 52]. Furthermore, limited data support the health benefits of digital tools among varied racial/ethnic groups, and even fewer data support the value for high-risk T2 DM patients in such marginalized populations [53; 54]. This study leverages a retrospective analysis of a home-use diabetes digital platform with full data capture in a supporting mobile app among T2 DM patients with out-of-target blood glucose levels across ethnic/racial groups. The current study aimed to assess whether there were differences in blood glucose levels among high-risk T2 DM users using the same digital tool from varied racial/ethnic groups (WP, Latino, Black, or Asian) over a year. Moreover, the study's objectives were to compare clinical outcomes between racial/ethnic groups and to investigate the association between the engagement level (blood glucose measurements) with platform and clinical outcomes in both groups. The retrospective study was conducted based on real-world data. We hypothesized that the use of digital platforms over a year would be associated with reduced blood glucose levels through increased self-management among racial/ethnic groups. By analyzing average blood glucose differences over a year among the varied races/ethnicities and between BMI groups, we expected to show that different racial/ethnic groups equal in their baseline using the digital health platform will experience comparable improvement. Moreover, we speculated that user engagement with the platform would be linked with improvement in monthly average blood glucose levels across ethnic/racial groups.
# Improving Abstractive Dialogue Summarization Using Keyword Extraction
## 5 Conclusions  
We proposed a dialogue summarization system, KADS, that efficiently utilized keyword information to improve the performance of dialogue summarization systems. We showed that the keyword extractor performance could significantly affect the results of dialogue summarization. Experimental results on widely used dialogue summarization datasets indicated that our proposed keyword-aware dialogue summarization showed improvement over baseline systems. We believe that the performance of KADS can be additionally improved if a superior keyword extractor is proposed in the future. Also, we showed that our proposed system is especially efficient in low-resource conditions.  
Conceptualization, C.Y. and H.L.; methodology, C.Y. and H.L.; software, C.Y.; validation, C.Y. and H.L.; formal analysis, C.Y. and H.L.; investigation, H.L.; resources, C.Y.; data curation, C.Y.; writing--original draft preparation, C.Y. and H.L.; writing--review and editing, H.L.; visualization, C.Y. and H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Chung-Ang University Research Grants in 2023. This research was supported in part by Institute for Information & Communications Technology Planning & Evaluation (IITP) through the Korea government (MSIT) under Grant No. 2021-0-01341 (Artificial Intelligence Graduate School Program (Chung-Ang University)).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** \\ \hline Baseline & & & \\
100\% & 46.1996 & 21.0814 & 38.8086 \\
75\% & 44.8136 & 20.6273 & 38.0324 \\
50\% & 44.0717 & 20.1916 & 36.6705 \\
25\% & 43.8293 & 20.0013 & 36.0213 \\
10\% & 41.2341 & 18.7535 & 34.3425 \\ \hline KADS & & & \\
100\% & 47.2237 (+1.0241) & 22.1353 (+1.0539) & 39.8665 (+1.0579) \\
75\% & 46.2476 (+1.4340) & 21.2876 (+0.6603) & 39.2494 (+1.2170) \\
50\% & 45.2720 (+1.2003) & 20.5406 (+0.3490) & 38.1776 (+1.5071) \\
25\% & 45.9331 (+2.1038) & 20.9613 (+0.9600) & 37.7504 (+1.7291) \\
10\% & 43.2545 (+2.0204) & 19.6724 (+0.9189) & 36.0252 (+1.6827) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance comparison with the systems trained with full dataset and trained with half of the datasets randomly sampled from DialogSum.
## 3 Results
### Pearson's Correlation  
Pearson's correlation coefficients are used to evaluate the relationship between the different BCs classes of _R. angustiplus_ extracts (obtained by semi-quantitative data analysis) and the various biological activity assays. A positive correlation could reflect a direct or indirect molecular interaction between BCs and molecules involved in biological activity. Furthermore, total polyphenols (including their main classes), triterpenenes, sesquiterpene lactones, and alkaloids were considered in correlation analysis with antioxidant capacity (DPPH, ATBS, CUPRAC, FRAP, metal-chelating activity, phosphomolybdenum), and enzyme-inhibition activity (AChE, BChE, tyrosinase, \(\alpha\)-amylase, and \(\alpha\)-glucosidase). The significant correlation coefficients are shown in Table S7. A positive and significant correlation was observed between flavonoids and DPPH, ATBS, CUPRAC, and FRAP assays, delivering on average a correlation coefficient of 0.73 (_p_ < 0.01). Phosphomolybdenum and metal-chelating activities showed an overall negative correlation, caused by the main group of polyphenols (anthocyanins, flavones, LMWPs, and phenolic acids) and terpenoids (sesquietrepene lactones and triterpenes). Regarding tyrosinase-inhibition activity, anthocyanins, flavones, and phenolic acids exhibited the highest correlation coefficients, i.e., 0.849, 0.664, and 0.746 (_p_ < 0.01), respectively. Moreover, \(\alpha\)-amylase did not show significant correlations with phytochemical classes, whereas \(\alpha\)-amylase inhibition correlated with polyphenols (0.701) and triterpenes (0.761). Finally, AChE and BChE inhibition activity was highly correlated to flavonoids (0.729) and triterpenes (0.620), respectively (Table S7).