esa-sceva/satcom-chunk-collection huggingface dataset:
# Groove Formation in Glass Substrate by a UV Nanosecond Laser
## 4 Conclusions  
In this study, the UV nanosecond laser grooving process for glass substrate is carried out, and the process characteristics including surface morphology, crack, and material removal mechanism are investigated. The important conclusions of this work are as follows.  
(1) The damage threshold power density of the UV laser beam for soda lime glass surface is \(\sim\)6.37 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\) (at 5 mm/s scan speed and 30 kHz pulse repetition rate).  
(2) The groove produced by the single laser scan process can be categorized as two types based on the morphology of the groove. The first type of the groove has a smooth surface owing to the melting-dominant material removal process, and the cracks of several tens to hundreds of microns in size are usually produced along the groove of this type. The second type of the groove has a rough surface morphology due to shattering and non-ejected molten glass powders; however, in this case, the crack-free groove can be obtained when a relatively high scan speed is used. The process conditions producing the crack-free groove are shown in Table 5.  
(3) The near rectangular-shaped groove, imitating the microchannel for a bio-chip application, of several hundred micrometers in size and with smooth surface is successfully fabricated by multiple laser scans without crack. This result shows the feasibility of a UV nanosecond laser as a potential tool for the fabrication of bio-chip channels.  
**Author Contributions:** J.S., conceptualization, data curation, formal analysis, funding acquisition, methodology, project administration, supervision, visualization, writing--original draft, writing--review & editing. K.N., data curation, investigation, methodology, visualization. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07040563).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Affordable Thin Lens Using Single Polarized Disparate Filter Arrays for Beyond 5G toward 6G
## 2 Design of Single Polarized Thin Lens
### Feeder Design: 4 \(\times\) 4 Patch Antenna Array  
Figure 9 shows the geometry of an antenna used as a feed source to verify the gain enhancement of the proposed lens with a given process limit. The antenna is designed as a rectangular patch, and the antenna uses the direct feed method through the via hole, as shown in Figure 9b. A \(4\times 4\) array configuration is selected for beam steering capability, which is an essential functionality for a wireless backhaul system requiring a slight beam adjustment of a few degrees in deployment scenarios. The designed patch antenna array is fabricated using a 10-layer FR4 stacked substrate, which is widely used in commercial millimeter wave antenna-in-package solutions because of the rapid development of 5G technology with a low manufacturing cost [17; 18; 19; 20]. The thickness of the antenna substrate is 0.762 mm, and the dielectric constant and loss tangent are 4.2 and 0.002, respectively. All the 10 layers are filled with copper except for the areas related to dominant fringing fields, as shown in Figure 9a. This is because dielectric losses along the substrate increase substantially as the target frequency increase up to millimeter wave frequencies such as 60 GHz. Physical dimensions in the antenna array are \(L_{x}=16\), \(L_{y}=16\), \(p_{x}=1.04\), \(p_{y}=1.04\), \(u_{x}=1.9\), \(u_{y}=1.9\), \(s_{x}=2.5\), and \(s_{y}=2.5\) in millimeters (mm). Figure 10 shows the 3D radiation patterns of the designed \(4\times 4\) patch antenna array. Each beam pattern is formed when input phase offsets of 0, 45, 90, and 135\({}^{\circ}\) are applied along the \(y\)-axis. Accordingly, the beam pattern exhibits beam steering characteristics in the \(\theta\)-direction according to the phase offset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**UC\#** & **Filter Type** & \(g1\) **(mm)** & \(g2\) **(mm)** & \(g3\) **(mm)** & \(w\) **(mm)** & **Insertion Loss (dB)** & **Phase Shift (\({}^{\circ}\))** \\ \hline LP1 & Low-pass & 0.2 & 0.125 & 0.225 & & 0.8 & –276 \\ LP2 & Low-pass & 0.225 & 0.15 & 0.25 & & 0.5 & –236 \\ LP3 & Low-pass & 0.25 & 0.15 & 0.3 & & 0.4 & –208 \\ LP4 & Low-pass & 0.3 & 0.25 & 0.3 & & 0.2 & –167 \\ LP5 & Low-pass & 0.325 & 0.3 & 0.35 & & 0.4 & –142 \\ \hline BP1 & Band-pass & 0.325 & & 0.375 & 0.25 & 0.2 & –115 \\ BP2 & Band-pass & 0.4 & & 0.4 & 0.25 & 0.3 & –98 \\ BP3 & Band-pass & 0.5 & & 0.6 & 0.3 & 0.4 & –63 \\ BP4 & Band-pass & 0.7 & & 0.7 & 0.3 & 0.2 & –27 \\ BP5 & Band-pass & 0.9 & & 0.95 & 0.25 & 0.7 & –2 \\ \hline \end{tabular}
\end{table}
Table 1: Geometric parameters, insertion loss, and phase shift of low-pass and band-pass unit cells at 60 GHz with normal incidence.  
Figure 9: (**a**) Top and (**b**) bottom views of the \(4\times 4\) patch antenna array to use as a source for lens verification.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 4 The Proposed Method
### PKs (Probability and k-Shell)  
During propagation, changes in the probability of propagation affect the extent to which a node can propagate. This is because a lower probability of propagation puts the entire network in a state of complete immunity before it is fully propagated. Therefore, changes in the propagation probability can have a significant impact on node identification. The k-shell algorithm has the disadvantage that the nodes in the same layer cannot recognize the cruciality, and the recognition granularity is coarse. The influence level of nodes in the network changes with the propagation probability, which means that the ranking of nodes by their influence level will also change accordingly. Based on this, this paper proposes the PKs (probability and k-shell) algorithm, which combines the propagation probability with the k-shell algorithm, thereby addressing the issue of coarse recognition strength.  
For a particular node, when the propagation probability is low, greater consideration should be given to the number of nodes with higher influence levels in its immediate neighbors. Conversely, when the propagation probability is high, more emphasis should be placed on the number of nodes with higher influence levels in its distant neighbors. Then, the value \(PK_{s}\) for node \(i\) is shown in Equation (5):  
\[PK_{s}(i)=(\beta_{max}-\beta)*k_{s}(i)+\beta*\sum_{j\in N(i)}k_{s}(j) \tag{5}\]  
where \(\beta_{max}\) is the maximum propagation probability, \(\beta\) is the current propagation probability, \(k_{s}(i)\) is the k-shell value of node \(i\), and \(N(i)\) is the first-order neighbor set of node \(i\).
## 2 RSOA Direct Modulation Assisted by MRR-Based Notch Filtering  
### Configuration  
The basic configuration of a directly modulated RSOA is shown in Figure 1. A CW signal of constant power, \(P_{\text{CW}}\), is inserted in the RSOA. Concurrently, a radio frequency (RF) signal, which comprises non-return-to-zero (NRZ) data pulses, is superimposed to the RSOA DC bias current, \(I_{bias}\), and induces, via the RSOA electrical impedance, a peak current offset, \(\pm I_{m}\). Since the RSOA rear edge is highly reflective, the CW signal undertakes a double pass inside the active medium of length \(L\), and when it exits from the RSOA front facet, which is highly antireflective, it has perceived the gain variations due to the modulated current. Normally, the CW signal at the RSOA output should bear the exact digital information of the applied electrical excitation in optical form. However, as the rate of direct modulation gets faster, this may not happen, as due to the RSOA limited modulation bandwidth, the performance of the directly modulated RSOA is progressively deteriorated by pattern effects and eventually becomes poor. Still, it is possible to enhance the RSOA direct modulation capability by means of optical notch filtering. For this purpose, the RSOA is connected to an MRR, which, as shown in Figure 1, is a waveguide shaped into a ring structure of radius \(R\) coupled to a bus waveguide with field transmission coefficient \(r\)By properly tailoring the MRR spectral response, the MRR can efficiently act as frequency discriminator on the encoded pulses and improve their quality. This is done by adjusting the wavelength separation between adjacent peaks or notches, or free spectral range (FSR), which is inversely proportional to the MRR radius, and wavelength offset, that is, detuning, \(\Delta\lambda\), between the encoded signal spectral position, \(\lambda_{enc}\), and the nearest transfer function (TF) transmission peak, while maximizing the MRR transfer function peak-to-notch contrast ratio (PNCR) through operating the MRR in the critical coupling regime, where the microring internal losses are equal to the coupling losses. In this manner, the MRR compensates for the inherently limited bandwidth of the RSOA, and mitigates the pattern-dependent impairments induced on the encoded pulses.
## 3 Discussion  
Benzastatins or virantmycins are isolated from _Streptomyces_ species of actinomycetes, which have unique heterocyclic structures that contain indoline or tetrahydroquinoline unit [17; 18; 21; 22; 23]. Those compounds were derived from geranyl diphosphate (GPP) and p-aminobenzoic acids. Then, the acetylated geranylated p-aminobenzoic acids (GPAA) were catalyzed to generate an azirdine ring by the key cytochrome P450 (BezE), and hydroxide ion or chloride ion attacked different position of the azirdine ring to form indoline or tetrahydroquinoline moiety through nucleophilic addition (Figure 7) [24]. Benzastatin E may be oxidized by monooxygenase to obtain compound **2** with an epoxy moiety at C-13 and C-14 [25]. Due to the structural stability of the indoline moiety, the configuration at C-9 is reversed. After the chlorine atom has left, **1** is possibly formatted by the cyclization of the electrophilic addition reaction, and the orientation of the methoxymethyl side chain changes during the annulation reaction (Figure 8).  
Virantmycin, which was first isolated from the fermentation broth of _Streptomyces nitrosporeus_, has been reported to show significant antiviral activity against various RNA and DNA viruses [19; 26]. The antiviral activities of virantmycin derivatives indicate that the configurations of the tetrahydroquinoline ring unit, the carboxylic group, and the terminal tetrasubstituted double bond are responsible for the anti-influenza virus activity [19]. Benzastatin C, an amide derivative of virantmycin, has specific activities against several viruses, and benzastatin D, a hydroxyl substitute of benzastatin C, is inactive against the tested viruses, which suggests that chloride plays an important role as the active moiety for antiviral activity [27]. The present study (Table 1) also indicates that the chlorine atom is necessary for the antiviral activity. The good leaving groups (for example, chlorine or mesyl group) in the structure are shown to be essential to the activity, and the compound may react with the target protein through the substitution reaction [18]. Compound **4** has the best activity among the isolated compounds, which leads to the conclusion that the tetrahydroquinoline skeleton and the methyl group of the terminal double bond play an important role in the activation of anti-PRV activity. Benzastatin C and benzastatin D show mostly equal free radical scavenging activity, indicating that their anti-PRV activity is not related to antioxidant ability [28]. The mode of action of virantmycin against PRV should be further investigated, and the effect of virantmycin on AD needs to be verified in vivo.  
Figure 7: Compound 4 inhibits PRV proliferation. Vero cells were infected with 100 TCID\({}_{50}\) PRV for 1.5 h, and then compound 4 (0.10, 0.05, 0.02, and 0.01 ug/mL) and acyclovir (20 ug/mL) were added. After 48 h incubation, cell suspension was collected, and DNA was extracted for qPCR.
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 4 Results and Discussion
### Effect of Varying SNR Levels  
To develop a noise robust model for detecting victim from scream, we have created a dataset with nine different level of SNR varying from \(-10\) dB to 30 dB. The analysis section for SVM and LSTM portrayed the result of combined dataset which includes all the SNR level. In this section we discuss the effect of individual SNR level on both SVM and LSTM model for this custom dataset. For this purpose, we have separated the dataset into different level of SNRs which gave us nine segments of the dataset. Both SVM and LSTM model were then trained with individual segments. The hyperparameters and data split for the models were kept same as the best models discussed in the previous section. Tables 8 and 9 provides an insight of how different SNR levels are affecting the classification process. The'misclassified sample' columns in the tables refers to the number of incorrectly predicted samples in the test set of a total 504 samples from five classes. The'misclassified scream' columns represent the number of misclassified samples on the test set out of 101 scream samples.  
From Tables 8 and 9, it is clear that both of the models depict high recall and lower misclassification for high SNR. In contrast to this, the performance of the models degrades for the lower SNR as the background noise gets prominent. At \(-10\) dB SNR, SVM can accurately predict 91% of scream with only 9 misclassified scream samples out of 101 samples. However, for LSTM the performance goes down drastically with the decrease in SNR. The model can only predict 66% of scream correctly which is quite low compared with SVM.
## 5 Conclusions  
When analyzing the tests performed using a reflectometer, the following conclusions can be drawn:  
1. The 410-Solar reflectometer allows for a quantitative assessment of the sun protection properties of sesame oil within the spectrum of solar radiation in in vivo conditions.
2. Oil A enhances the reflectance in the near-infrared and visible light ranges, including blue HEV light. For significant protection against blue light, you must wait 60 min after applying the oil to the skin. Even 120 min later, it still retains its protective properties. In the near-infrared spectrum, significant protection is achieved within a period of 120 min.  
Oil B caused an increase in reflectance in the largest number of analyzed ranges when compared with the other tested oils. In the ultraviolet range, the reflectance value decreases 60 min after applying the oil to the skin. In the near-infrared range, it continues to increase.
4. During the tests, the effects of Oil C were seen in one range of visible light and in all ranges of near-infrared light. This oil is at its highest reflectance value 120 min after application to the skin.
5. Oil D caused an increase in reflectance in the smallest number of analyzed ranges when compared with the other tested oils. Achieving the highest level of reflectance measured in this study took approximately 120 min after application to the skin. There was an impact on the reflectance in one of the visible and one of the infrared light ranges.
6. Sesame oil has photoprotective properties; however, due to its relatively low protection factor, it should not be treated as an equivalent to sunscreen preparations.  
Conceptualization, M.B. and S.W.; methodology, M.B.; software, S.W.; validation, J.T., A.L. and A.S.-W.; formal analysis, M.B.; investigation, J.T., P.B. and A.L.; resources, S.W.; data curation, M.B.; writing--original draft preparation, A.S.-W. and A.L.; writing--review and editing, J.T.; visualization, A.S.-W. and J.T.; supervision, S.W.; project administration, M.B.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Medical University of Silesia, grant number PCN-CBN-640-2-1-1005/22/23.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics Committee of the Medical University of Silesia on 29 June 2021 (PCN/CBN/0022/KB1/27/III/16/17/21).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to privacy reasons.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Plants Isotopic Values (C-N-S), Comparison Between Regions
#### 3.1.4 \(\delta^{13}\)C, \(\delta^{15}\)N, and \(\delta^{34}\)S of Marine Species  
The fish samples studied come from both marine and freshwater environments. The relatively low carbon values of the marine fish samples state that they might have been caught from waters with significant freshwater influx. In fact, the marine coastal fish samples analyzed in this work include sea bass, groupers, and breams. The \(\delta^{13}\)C values of these species range from \(-13.1\) to \(-18.9\%\); this value characterizes the species that have been found in shallower waters near shorelines, typically in coastal regions where the depth of the water is less than 200 m. They are often associated with estuaries, bays, and reefs and can tolerate the dynamic conditions of these areas (Eurylahaline zone), including changes in salinity, temperature, and depth. Inshore fish, also, such as sparidae (\(\delta^{13}\)C = \(-13.1\%\) and \(\delta^{15}\)N = \(+6.3\%\)), enter coastal Lagoons, estuaries, and salt marshes for spawning. The inshore fishes are commonly found in marine and brackish waters of temperate regions, like those in Greece.  
The coastal species found in deeper waters, such as sole and red mullets, present \(\delta^{13}\)C between \(-18.3\) and \(-19.5\%\)., \(\delta^{15}\)N between 9.6 and 12.7%., and \(\delta^{34}\)S values ranging from 17.8 to 18%. The marine pelagic fish, like mackerels, show \(\delta^{13}\)C = \(-17.8\%\), \(\delta^{15}\)N = \(10.5\%\)and \(\delta^{34}\)S values = 17%. Generally, these marine species come from the east coast of the mainland of Greece, and their \(\delta^{34}\)S values reflect the marine sulfate influence. These coastal species are the most common in archaeological fish assemblages [67; 68].  
The \(\delta^{13}\)C values of freshwater fish range from \(-\)18.7 to \(-\)20.5%, \(\delta^{15}\)N values range from 7.5 to 13%, and \(\delta^{34}\)S values range from 4.6 to 14%. These freshwater fish originate from Northern Greece, a region known for its rivers and lakes. Most of these rivers form swamps and lakes before reaching the Aegean coast. This aquatic habitat is also preferred by bream because this fish thrives in slow-moving or still waters and ponds with vegetation- and oxygen-rich conditions. So, the freshwater hydrological system, in Northern Greece, falls largely into the \"coastal zone\". The carp and various other cypririinals inhabit these warm, deep, slow-flowing, and still waters (Table 3). For comparison, the \(\delta^{13}\)C and \(\delta^{15}\)N values of recent [68] and older [52; 69; 70; 71; 72; 73] freshwater fish and fauna from the Mediterranean Sea are depicted in Table 4. These findings demonstrate significant similarities between the aforementioned fish samples (see Table 3).
## 3 Results
### Epileptic Activity  
The possible influence of disease severity (expressed by the APACHE II score or the SOFA score) and renal function (expressed by the CKD-EPI eGFR) on epileptic status was investigated. The Shapiro-Wilk test indicated normal distribution of the data and therefore one-way ANOVA was performed to detect a difference in the values of APACHE II and eGFR between the three levels of epileptic activity (positive, negative, possible). It was found that the APACHE II score was significantly different between the three groups (\(p\) = 0.034). A statistically significant difference was also found for the CKD-EPI eGFR (\(p\) = 0.031). For the APACHE II score, the absence of epileptic activity (i.e., the \"negative\" group) differed from the \"possible\" (\(p\) = 0.033) and the \"positive\" epileptic activity (\(p\) = 0.027). The patients with the lowest APACHE II score belonged to the group without epileptic seizures.  
Regarding the CKD-EPI eGFR values, the three epilepsy groups showed significant differences (\(p\) = 0.031). Similarly, the group of patients without seizures was statistically different from the \"possible\" (\(p\) = 0.034) and the \"positive\" group (\(p\) = 0.022). Statistical comparisons were made for all other characteristics, but no significant differences were found.  
It should be clarified that the abovementioned analysis was not the primary purpose of the study and for this reason no power assessment was made focusing on this. In addition, this assessment only attempted to link disease severity with renal function and not to explore their relationship with levetiracetam pharmacokinetics. In any case, it should be underlined that due to the limited sample size and therefore the number of patients within each group, these findings should be considered as exploratory and further adequately powered studies are necessary to confirm the results.
## 5 Conclusions and Suggestions
### Limitations and Future Research  
This study also has several limitations. The PageRank model used here is based solely on binary win-loss outcomes, without incorporating contextual factors such as the margin of victory, home-field advantage, or player-level variables. Furthermore, this study's empirical scope is confined to a single tournament in Taiwan, which may limit the generalizability of its conclusions to other sports contexts or educational systems. Cross-validation against alternative sports or geographical contexts would strengthen external validity and broaden the applicability of these findings.  
Additionally, this study did not conduct formal statistical robustness testing such as sensitivity analysis, bootstrap validation, or perturbation studies. While our findings demonstrate consistent performance across multiple seasons and temporal horizons, formal robustness evaluation remains an important direction for future research to establish PageRank's reliability under data uncertainty in tournament settings.  
Future studies may expand upon this work in several directions. Methodologically, incorporating weighted metrics such as score differentials or recency-weighted outcomes may enhance the sensitivity of PageRank in capturing real-time team dynamics. Time-decay variants or dynamic PageRank adaptations may also better accommodate performance evolution throughout a season. Additionally, comparative evaluations with alternative ranking models, including Elo, Colley, or Massey, would help clarify the strengths and trade-offs of different algorithms. Finally, applying these methods across different sports, competition levels, or international school systems can further test the scalability and versatility of network-based ranking approaches in sports analytics.  
Conceptualization, methodology, investigation, formal analysis, writing, and supervision, Y.-C.H.; data curation, W.-J.Z.; software, Y.-C.H. and W.-J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Taiwan University of Sport (grant number 114 DG00108).  
Not applicable.  
Data Availability StatementData were obtained from the official website of the Black Panther Cup ([[https://blackpanthercup.tw/](https://blackpanthercup.tw/)]([https://blackpanthercup.tw/](https://blackpanthercup.tw/)), accessed on 4 May 2025) and the Chinese Taipei Baseball Association ([[http://www.ctba.org.tw/](http://www.ctba.org.tw/)]([http://www.ctba.org.tw/](http://www.ctba.org.tw/)), accessed on 4 May 2025). The cleaned and processed datasets used in this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.