esa-sceva/satcom-chunk-collection huggingface dataset:
# On a Framework for Federated Cluster Analysis
## 3 Results  
Our framework is evaluated on three different groups of data. Firstly, we handcraft a non-i.i.d. clustering scenario and apply the complete clustering framework to it. The emphasis in this experiment is on demonstrating and motivating the use of the federated clustering framework to obtain a global cluster structure without sharing the raw data. Secondly, we create federated scenarios from 100 well-known cluster benchmark datasets from the literature [41] by uniformly distributing the data to clients. This set of experiments allows us to study how the framework performs with data of different dimensionality and different spread, and how it behaves with increasing number of clients, but fixed number of total data points. We will see that high sparsity is harmful and big cluster overlap is harmful in the federated and nonfederated setting. Thirdly, we apply the complete framework to more real-world-inspired data and demonstrate how to use it in practice. We will see that the federated and nonfederated results are mostly consistent, and the federated clustering is even slightly better in terms of FedFuzzDB.  
Moreover, we compare our federated fuzzy \(c\)-means with \(k\)-means averaging to federated fuzzy \(c\)-means, as introduced in [20,21]. We note that scenarios where the data are nonuniformly distributed among clients are not investigated by these works. In fact, we observe that the method does not converge reliably in such scenarios (see Section 3.2.1 and our preliminary work [37]). Further, note that we do not compare the method to hard clustering methods such as [19] or [22], because we only introduced the fuzzy version of the federated Davies-Bouldin index.  
Before describing each dataset in more detail and reporting the results, we introduce the evaluation methods for our experiments.
## 3 Datasets
Using the Normalized Hotspot Indices (NHI) [46], volcanic hotspots can be mapped in detail based on the well-resolved IR bands of Sentinel-2 (20 m) and Landsat 8 (30 m). Based on the particularly strong emission of hot objects at the Earth's surface in the SWIR wavelength range, as well as the shift of the maximum emission towards shorter SWIR wavelengths as the temperature of the objects increases, volcanic hotspots can be identified and classified according to their intensity. The detailed methodology of the NHI is described in [PERSON] et al. [46]. The NHI hotspots for all active Holocene volcanoes, listed by the Global Volcanism Program (GVP), can be accessed online using the GEE tool ([[https://nicogenzano.users.eartheengine.app/view/nhi-tool](https://nicogenzano.users.eartheengine.app/view/nhi-tool)]([https://nicogenzano.users.eartheengine.app/view/nhi-tool](https://nicogenzano.users.eartheengine.app/view/nhi-tool))) [50]. For the analyzed events (Karangetang I, Karangetang II, Krakatau I), NHI hotspot pixels were derived and used as input data for the object-oriented change detection methodology for lava flow classification (Section 4.3). Digital elevation models (DEM) were used to generate runoff models which allowed us to constrain potential lava runoff areas in the object-oriented change detection methodology (Section 4.3). Depending on the event studied, a TanDEM-X DEM (12 m) [51], Copernicus GLO-30 DEM (30 m) [52], Shuttle Radar Topography Mission (SRTM) DEM (30 m) [53], and an ALOS PALSAR RTC Product DEM (12.5 m) [54] were used.
# Fungal Pigments and Their Roles Associated with Human Health
## 2 Types of Fungal Pigments and Their Relevance to Human Health
### Other Fungal Pigments  
The pigment sclerootin has been isolated from cultures of _Penicillum sclerotium_ 2 AV2, which was recovered from Amazonian soil [6]. Sclerotiorin (Figure 5) has been found to have a variety of biological activities. Some of the biological activities most pertinent to human health include inhibitory effects on aldose reductase [79], endothelin receptor binding activity [80], antimicrobial effects including antifungal activity [17; 81], apoptosis-triggering actions towards HCT-116 cancer cells [82], and the inhibition of integrase and protease of HIV-1 [83].  
Studies on _Penicillum chrysogenum_ have identified a polyketide synthase (PKS) gene required for the biosynthesis of sorbicillinoid, a yellow pigment [84]. The authors demonstrated that a highly reducing PKS enzyme, encoded by Pc21g05080 (_pks13_), is indispensable for the production of polyketide precursors such as sorbicillinol and dihydroxybicillinol as well as their derivatives such as bisorbicillinoids. PKSs are also involved in the biosynthesis of sorbicillinoid in _Acemonium chrysogenum_, the fungal species used in the industrial production of cephalospororin C, an antibiotic in current clinical use [85].  
Figure 4: Chemical structure of monacolin K, monascin and anakfalavin. (**A**) Monacolin K; (**B**) Monascin and anakfalavin.
## 4 Experiments
### Comparison with State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **MT \(\uparrow\)** & **ML \(\downarrow\)** & **IDs \(\downarrow\)** & **FP \(\downarrow\)** & **HOTA \(\uparrow\)** \\ \hline \hline \multicolumn{7}{l}{* Center Track [5]} & 67.8 & 64.7 & 34.6 & 24.6 & 3039 & 18,498 & 52.2 \\ \multicolumn{7}{l}{* Ctracker [19]} & 66.6 & 57.4 & 32.2 & 24.2 & 5529 & 22,284 & 49.0 \\ \multicolumn{7}{l}{* ByteTrack [7]} & 80.3 & 77.3 & 53.2 & 14.5 & 2196 & 25,491 & 63.1 \\ \multicolumn{7}{l}{\# FairMOT [25]} & 73.7 & 72.3 & 43.2 & 17.3 & 3303 & 27,507 & 59.3 \\ \multicolumn{7}{l}{\# CSTrack [34]} & 74.9 & 72.6 & 41.5 & 17.5 & 3567 & 23,847 & 59.3 \\ \multicolumn{7}{l}{**Ours**} & **80.5** & **80.1** & **52.1** & **14.3** & **2031** & **19126** & **62.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MOT 17 Results. # indicates one-shot method. * indicates other joint detection and tracking methods which adopt non-ReID methods for data association. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **IDS \(\downarrow\)** & **FP \(\downarrow\)** & **FN \(\downarrow\)** \\ \hline Motion (k) + Re-ID & 69.1 & 72.8 & 299 & 1976 & 14,443 \\ Motion (k) + EFGCN Re-ID & **74.2** & **75.3** & **274** & **1857** & **13,658** \\ Motion (k) + BYTE & 70.3 & 73.2 & 236 & 2189 & 13,625 \\ Motion (k) + ACM & **73.4** & **74.8** & **221** & **2075** & **13,264** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with ByteTrack based on FairMOT in MOT17. The best results are shown in bold.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.4 Weighted k-NN  
Weighted k-Nearest Neighbors (Weighted k-NN) [51] is a weighted model of the k-Nearest Neighbors (k-NN) algorithm. In this model, each neighbor's influence on the prediction is weighted based on its distance. Closer neighbors have more weight in the prediction. Weights are usually calculated using inverse distance or a similar formula. Weighted k-NN provides more accurate predictions by calculating the contributions of different neighbors based on distance, rather than equally. This model is particularly useful when the differences between the effects of data points are significant.
# Profiling Croatian Whisky Using GC/MS-MS and UHPLC-QTOF
###### Abstract  
This study investigates the chemical profiling of Croatian whisky using advanced analytical techniques: Solid-phase microextraction followed by gas chromatography coupled to mass spectrometry (SPME-GC-MS) and ultra-high-performance liquid chromatography quadrupole time-of-flight (UHPLC-QTOF). To our knowledge, there are no studies on profiling Croatian whiskies to date. Therefore, the primary objective is to identify and quantify the volatile and non-volatile compounds contributing to the flavor and aroma profile of Croatian whisky. The methods applied included sample preparation, extraction, and subsequent analysis using these advanced analytical techniques. The results revealed a diverse range of compounds, including esters, alcohols, phenols, and acids, which are integral to the whisky's sensory characteristics. Furthermore, key volatile and non-volatile compounds were identified in Croatian whiskies, revealing a complex aromatic profile influenced by compounds like D-limonene, linalool, and \(\beta\)-damascenone, which contribute citrus, floral, and fruity notes. The findings also highlighted compounds associated with aging, such as esculetin and ellagic acid, which enhance flavor complexity and emphasized the role of maturation, grain type, and production methods in shaping the unique sensory characteristics of Croatian whiskies. This study concludes that SPME-GC-MS and UHPLC-QTOF are effective tools for the detailed chemical profiling of whisky, providing valuable insights for quality control and product differentiation in the whisky industry.  
C 2020 15 2738 +
Footnote †: journal: Applied sciences  
Article  
Article  
Procial whisky; SPME-GC-MS; UHPLC-QTOF; chemical profiling; volatile compounds; non-volatile compounds; flavor profile; aroma profile
## 6 Implementation of the SMC and the MPPT Algorithm
### Control Law  
The implementation of the practical sliding surface (39), which includes the hysteresis band \(-H<\psi_{x}<+H\), requires the following control conditions:  
* Condition 1: \(u=1\) (MOSFET on, Diode off) imposes a negative switching function derivative \(\frac{d\psi_{x}}{dt}<0\), as given in (14); hence, the switching function \(\psi_{x}\) is decreasing. Moreover, from Figure 1, it is observed that \(u=1\) imposes \(i_{m}=i_{MOS}\). When \(\psi_{x}\) reaches the lower limit \(-H\), \(\psi_{x}\) must increase to remain inside the hysteresis band. Therefore, the switching function derivative must be changed to a positive value \(\left(\frac{d\psi_{x}}{dt}>0\right)\), which requires changing \(u=0\) according to (23).
* Condition 0: \(u=0\) (MOSFET off, Diode on) imposes a positive \(\frac{d\psi_{x}}{dt}>0\), as given in (14); hence, \(\psi_{x}\) is increasing. Moreover, from Figure 1, it is observed that \(u=0\) imposes \(i_{m}=n\cdot i_{k}\). When \(\psi_{x}\) reaches the upper limit \(+H\), \(\psi_{x}\) must be reduced to remain inside the hysteresis band; thus, the switching function derivative must be changed to a negative value \(\left(\frac{d\psi_{x}}{dt}<0\right)\), which requires changing \(u=1\) according to (25).  
The previous control behavior is formalized in the control law described below:  
\[u=\left\{\begin{array}{ccc}0&\text{if}&\psi_{x}>+H&\text{with}&\psi_{x}=k_ {1}\cdot\left(v_{pv}-v_{mppt}\right)+i_{pv}-d\cdot i_{MOS}\\ 1&\text{if}&\psi_{x}>-H&\text{with}&\psi_{x}=k_{1}\cdot\left(v_{pv}-v_{mppt} \right)+i_{pv}-d\cdot n\cdot i_{k}\end{array}\right. \tag{44}\]  
Such a control law is implemented using a hysteresis comparator, two adders, three subtractors, two multipliers, two gains, and a divider for the calculation of \(k_{2}=d\) using (9). In addition, the MPPT filter can be implemented using an active filter based on the operational amplifiers. Figure 5 shows the block diagram for implementing the complete control system, including the MPPT block, the calculation of \(k_{2}\), the calculation of the switching function \(\psi_{x}\), and the hysteresis comparator that produces the MOSFET control signal \(u\).
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 3 Insulator Damage Detection Based on Zernike Moment
### Image Preprocessing
By calculating the characteristic moments of the insulator pieces' contours shown in Figure 6d and comparing the similarity value between different pieces on the same insulator, it is possible to distinguish whether the insulators are damaged. Figure 4: The process of the RAISR algorithm.
# Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay
###### Abstract  
Currently, more attention has been given to the study of the microstructure of contaminated clay, while few studies have focused on the analysis of SEM images of clay from a threshold perspective. The purpose of this paper was to determine the threshold range of image pixel values for municipal waste-contaminated clay by observing and processing SEM images of municipal waste-contaminated clay under different seepage depths and concentrations. In this study, municipal waste-contaminated clay samples were obtained at different depths in a column test exhibiting different CaCO\({}_{3}\) and CH\({}_{3}\)COOH concentrations, imaged through SEM and subjected to binarization. Three steps for determining the pixel threshold value of the SEM images were described, including preprocessing and binarizing the SEM images in Image Pro Plus 6.0 (IPP) and morphological processing by the Pores (Particles) and Cracks Analysis System V2.3 (PCAS). The apparent void ratio of contaminated clay and the fractal dimension of clay particles were applied as evaluation parameters. The determined effective threshold range of the image pixel values of municipal waste-contaminated clay was 110-140. The accuracy of this method was verified in three aspects: the relationship between the threshold value and the apparent void ratio, the variation in pore blockage with seepage depth, and the characteristics of the compaction factor with seepage depth and pollutant concentration.  
m +
Footnote †: journal: Applied Sciences  
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## 3 Results
### Changes in the Composition of Bacterial and Fungal Communities
These account for less than 5% of the total bacteria in the raw sludge and less than 0.5% of the bacteria from earthworm casts and vermicompost (Table 2). Most of the HPBs completely disappeared after transit through the earthworm gut. However, two of them appeared in vermicompost (Table 2), showing significantly reduced abundances (_Aroobacter cryaevophilus_) or not (_Collinsella aerofaciens_, Supplementary Table S3). Other three HBPs were present in earthworm cast and not in the sewage sludge, i.e., _Cellulosinicribum cellulans_, _Turicibacter sanguinis_ (ASV390) and _T. sanguinis_ (ASV 130, Table 2). Vermicomposting completely removed two of them, and no significant changes were detected in the abundance of _T. sanguinis_ (ASV130, Table 2). Fungal community in the sewage sludge consisted of four phyla, dominated by Basidiomycota (87%), with minor contributions from Mortierellomycota (6%) and Ascomycota (5%). Among these fungal phyla, GAP significantly increased the abundance of Mortierellomycota (see Supplementary Table S1) and also removed phylum Mucoromycota (Table 1). The fungal community of the vermicompost comprised the same phyla as the fresh casts. In this case, there were no significant differences in the abundance of fungal phyla due to CAP (Supplementary Tables S1 and S2). At the genus level, the passage of the sludge through the earthworm gut resulted in a significant decrease in the abundance of the fungal genera _Cutaneotrichospor_, _Trichospor_ and _Candidida_ and a significant increase in the abundance of the fungal genera _Pycrupovirus_, _Chellymernia_ and _Fusarium_. The cast ageing processes of vermicomposting did not produce significant changes in the abundance of fungal genera (Figure 1D, Supplementary Table S2). At the ASV level, passage of the sludge through the earthworm gut significantly reduced the abundance of 57 fungal ASVs, mainly included in the fungal genera _Apidrichum_ and _Cutaneotrichospor_, and increased the relative abundance of 49 fungal ASVs, mainly included in the fungal genera _Apidrichum_ and _Mortierella_ (Supplementary Table S3). The cast ageing processes significantly reduced the abundance of 43 fungal ASVs and increased the abundance of 45 fungal ASVs, all mainly included in the fungal genera _Apidrichum_ and _Mortierella_ (Supplementary Table S3). Figure 1: Relative abundance of the main bacterial (**A**) and fungal (**C**) genera found in sewage sludge, earthworm casts and vermicompost produced by the earthworm _Eisenia anfrei_. Less abundant genera (less than 3% and 1% for bacterial and fungal genera, respectively) are grouped under “others”. Significant differences in abundances of bacterial (**B**) and fungal (**D**) genera between sewage sludge and casts, and between casts and vermicompost (DESeq2 \(p\)-values < 0.05). Only taxa with log\({}_{2}\) fold changes > 7 or < \(-\)7 are displayed; see Supplementary Table S2 for all genera.
## 3 Results
### Method Application
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Sample Type** & **A450 (nm) 1** & **A495 (nm) 2** & **A532 (nm) 3** & **MDA (\(\mu\)g/L) 4**  \\ \hline \multirow{4}{*}{t\({}_{0}\)5  } & Cow & 0.288 2  & 0.146 2  & 0.119 2  & 97.37 2  \\  & Sheep & 0.356  & 0.198  & 0.167  & 161.25 3  \\  & SEM & 0.015 & 0.012 & 0.011 & 14.34 \\  & \(p\) value & 0.0001 & 0.0002 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \multirow{4}{*}{t\({}_{1}\)6  } & Cow & 0.308  & 0.151 2  & 105.2  \\  & Sheep & 0.392  & 0.217  & 0.184  & 182.9  \\  & SEM & 0.019 & 0.016 & 0.013 & 17.58 \\  & \(p\) value & 0.0003 & 0.0005 & 0.0002 & 0.0002 \\ \hline \hline \end{tabular} \({}^{1,2,3}\) Absorbances read at 450, 495, and 532 nm; \({}^{4}\) malondialdehydedehyde concentration expressed as \(\mu\)g/L; \({}^{5}\) t\({}_{0}\)—defrosting time; \({}^{6}\) t\({}_{1}\)—24 h of storage. Results of _p_\(<\) 0.05 were considered statistically significant; \({}^{\mathrm{a}\)\({}^{\mathrm{b}}\)\({}^{\mathrm{b}}\)\({}^{\mathrm{c}}\)\({}^{\mathrm{d}}\) Means within a row with no common superscript differ (_p_\(<\) 0.05).  
\end{table} 
Table 5: Samples absorbances obtained using the optimized TBARS method and MDA concentrations (_n_ = 3).
# Exploring the Potential of Yellow Mealworm (_Tenebrio molitor_) Oil as a Nutraceuticalal Ingredient
## 2 Materials and Methods
### Assessment of Phenolic Compounds
#### 2.4.2 Phenolic Compounds Profile  
The qualitative and quantitative determination of phenolic compounds extracts was conducted using HPLC-HRMS analysis. The equipment used was the chromatography fraction of Thermo Scientific Ultimate 3000 coupled to Orbitrap Fusion Tribrid Series HRMS analyzer (Thermo FisherScientific, San Jose, CA, USA). Chromatographic separations were executed using the column Luna C18 (2.1 \(\times\) 150 mm, 3 mm) (Phenomenex, Bologna, Italy) with a constant temperature of 40 \({}^{\circ}\)C and a 0.2 mL/min flow rate. A binary mobile phase was A (water-formic acid; 0.1% \(v/v\)) and B (acetonitrile-formic acid) (ITW Reagents, S.R.L., Milan, Italy). The elution procedure was performed as follows: time/[A:B], 0 min/[100:0], 4 min/[95:5], 35 min/[0:100], 37 min/[0:100]. The sample solution injection volume was set to 20 \(\mu\)L. For mass spectrometry, the ion source was an H-ESI working in the positive ion mode, in the full-scan mode range of 100-1000 \(m/z\), with a resolution of 30,000 in FTMS. Xcalibur 4.0 software (Thermo Scientific, Bremen, Germany) was used for acquisition, data evaluation, elaboration, and calculation. Quantification was based on internal calibration curves built using the following standard compounds: malvidin, chlorogenic acid, apigenin, epicatechin, naringenin, quercetin, gallic acid, ferulic acid, and coumaric acid.
## 4 Conclusions  
Novel edge-termination structures were proposed to improve the breakdown voltage characteristics. The proposed structures consist of a polysilicon trench, an oxide trench, and a mixed poly-trench with an FFR structure. They were formed by implanting Al at the bottom of the trench. The effects of this structure increase the cylindrical junction curvature and enable a deeper and wider potential profile.  
Numerical simulations were performed to analyze the breakdown voltage of the FFR and the proposed trenched structures by varying the space and width parameters. The simulation results showed that the highest breakdown voltage of the FFR structure was 990 V (with a space and width of 1.1 \(\upmu\)m and 1.4 \(\upmu\)m, respectively). The trenched structures filled with polysilicon and oxide had a respective breakdown voltage of 790 V (with a space and width of 0.5 \(\upmu\)m and 2.0 \(\upmu\)m, respectively) and 1320 V (with a space and width of 1.3 \(\upmu\)m and 1.2 \(\upmu\)m, respectively). The last structure consisting of the FFR and poly-trench had the highest breakdown voltage of 1280 V (with a space and width of 0.7 \(\upmu\)m and 1.8 \(\upmu\)m, respectively). These results show that the oxide-trench and the structure combining the FFR and the poly-trench improve the breakdown voltage by approximately 29% and 33%, respectively,  
Figure 4: (**a**) Electric field distribution of the conventional FFR (E\({}_{\text{max}}=3.95\) MV/cm), (**b**) Electric field distribution of the trenched structure filled with polysilicon (E\({}_{\text{max}}=2.97\) MV/cm), (**c**) Electric field distribution of the trenched structure filled with oxide (E\({}_{\text{max}}=3.33\) MV/cm), and (**d**) Electric field distribution of a combination of the poly-trench and the conventional FFR (E\({}_{\text{max}}=3.47\) MV/cm).  
compared to the FFR. The proposed trenched structures can support the field profile effectively in a large area and reduce the electric field by all trench rings.  
Conceptualization, J.-H.J. and H.-J.L.; data curation, J.-H.J.; formal analysis, J.-H.C., G.-H.K., S.-H.C. and H.-J.L.; investigation, J.-H.J.; project administration, H.-J.L.; writing--original draft preparation, J.-H.J.; writing--review and editing, H.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2017R1A2B2011106). 2. This work was supported by the Comprehensive Technical Support Project for Power Semiconductor Industry Development Project, funded by Busan Metropolitan City and Busan Techno Park, Korea.  
The authors declare no conflict of interest.
## 1 Introduction
Apart from the present research, there is no other study on loose fibers with varying densities. An in-depth investigation of the structure of Aegagropiles and the characteristics of their fibers is reported in [24]. In general, the study shows that the shape, density, and size of the Aegagropiles can vary significantly, depending on the material included in the first nucleus on which the aggregate subsequently forms; another parameter that can vary their properties is the presence of sand inside--this too will have variable characteristics in terms of grain size and weight that can affect the overall density of the Aegagropiles. Variable densities between 100 and 400 kg/m\({}^{3}\) are detected in [18] (average value 220 kg/m\({}^{3}\)). The purpose of this article is to evaluate the possible use of Posidonia fibers as a sound-absorbing material, limiting mechanical and chemical treatments as much as possible, and to develop an analytical model that allows its acoustical properties to be optimized. To the author's knowledge, this is the first time an article has been published on acoustic data relating to the sound-absorbing performance of loose _Posidonia oceanica_ fibers, and that an analytical model has been presented which calculates the size of the panels (density and thickness) made from this material. Acoustic applications of other marine plants have not been found in the literature; therefore, this is an innovative concept, particularly considering that the ocean covers 70% of the earth. Also, the methodology represents an effective solution that was first proposed by the author in [7; 25]. It consists of a hybrid method between the experimental characterization of some parameters (i.e., density) and the numerical inversion of the acoustic data for other parameters.