esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, a compact wideband CP microstrip antenna array for the next generation small satellite application in the Ku-band was designed, simulated, and fabricated. The array is based on the iterative application of the SR phase feeding network. The elements have the C-to-C distance of 0.535\(\lambda_{0}\) at the designed frequency of 8.15 GHz. The IBW of the design is greater than 19.5% and AR is 8.8% (7.8 to 8.52 GHz), respectively. A measured peak gain of 15.5 dBic is achieved at 8.25 GHz. The design has a flat gain that varied from 15 dBic to 15.5 dBic in the desired bandwidth from 8.05 GHz to 8.25 GHz. Moreover, the design performance is measured for the gain and AR in the angular width from \(-10^{\circ}\) to \(+10^{\circ}\) in the Ku-band that validated the gain is greater than 12 dBic and AR is less than 1.5 dBic. The co- and X-pol radiation patterns at five frequency samples are measured in the band that agrees with the simulation results.  
M.E., S.T.-V., Y.Y., K.-Y.L. and K.-C.H. contributed to conceptualization, articulation, original draft preparation, review and the editing of the document. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-00261, De  
Figure 12: Simulated and measured radiation patterns of 4 \(\times\) 4 array at five frequency samples in \(\phi\) = 90\({}^{\circ}\) plane: (**a**) 8.05 GHz (**b**) 8.10 GHz. (**c**) 8.15 GHz. (**d**) 8.20 GHz. (**e**) 8.25 GHz.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Ref.** & **Type** & **Feeding** & **IBW** & **3 dB AR (\%)** & **CP Gain** & **C-C/E-** & **Distance** \\  & & **Mechanism** & **(\%)** & **(dBiç)** & **(\(\lambda_{0}\))** & \\ \hline
[24] & Single Layer & SR feed & 2.7 & 2.7 & 12.5 & 0.48 (C-C) \\ \hline
[25] & Single Layer & SR feed & 53 & 16.5 and 10.37 & 12.5 & 0.45 (C-C) \\ \hline
[26] & Single Layer & SR feed & 14.7 & 12.4 & 18 & 0.74 (E-E) \\ \hline
[27] & Single Layer & SR feed & 6.08 & 4.4 & NG & 0.74 (E-E) \\ \hline This & Single Layer & SR feed & \(>\)19.5 & 8.8 & 15.5 & 0.535 (C-C) \\ work & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed single-feed 4 \(\times\) 4 CP array antenna with the earlier onesvelopment of low power/low delay/self-power suppliable RF simultaneous information and power transfer system and stretchable electronic epineurin for wireless nerve bypass implementation.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
## 4 Results and Discussion
### Urban LULC Prediction  
#### 4.3.1 Identification of Driving Factors  
After detecting the changes in particular time periods, the identification of the driving factors was investigated regarding the changes in the specific time duration. To determine the association between the changes and the factors for the LULC modeling and simulation, Cramer's \(V\) values were calculated. The factors that had a Cramer's \(V\) value higher than 0.15 were selected to generate the transition potential map of land transition in each sub-model based on a root mean square error (RMSE) value less than 0.1 within 10,000 iterations of the MLP neural network parameters. Table 4 shows the influence factors that were used to generate a transition potential map for each specific period for the simulation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Factor** & **Overall V** & **A** & **W** & **U** & **F** & **M** \\ \hline \multirow{6}{*}{1998} & Distance to agriculture & 0.4533 & 0.6302 & 0.9938 & 0.2662 & 0.9176 & 0.1941 \\  & Distance to built-up area & 0.3843 & 0.8002 & 0.7752 & 0.2400 & 0.8207 & 0.0654 \\  & Distance to center of city & 0.1725 & 0.2229 & 0.3752 & 0.1268 & 0.3429 & 0.0918 \\  & Distance to forest & 0.1863 & 0.3178 & 0.2590 & 0.1170 & 0.2409 & 0.3127 \\  & Distance to miscellaneous & 0.1514 & 0.1373 & 0.1381 & 0.0618 & 0.1085 & 0.0806 \\  & Distance to road & 0.1349 & 0.1758 & 0.2737 & 0.0717 & 0.2902 & 0.0338 \\ \hline \multirow{6}{*}{1998} & Distance to water & 0.2699 & 0.3649 & 0.3191 & 0.5405 & 0.2517 & 0.0658 \\  & Economic growth rate & 0.1582 & 0.5003 & 0.3029 & 0.0994 & 0.3112 & 0.1283 \\  & Elevation & 0.2913 & 0.1914 & 0.1896 & 0.6409 & 0.0989 & 0.0349 \\  & Slope & 0.0652 & 0.0151 & 0.0532 & 0.1273 & 0.0303 & 0.0216 \\ \hline \multirow{6}{*}{2003} & Distance to agriculture & 0.4498 & 0.7677 & 0.9949 & 0.2285 & 0.9328 & 0.1356 \\  & Distance to built-up area & 0.4526 & 0.4701 & 0.9383 & 0.1378 &
## 4 Discussion
### Discussion of Multiplicative Fault Isolation and Identification
#### 4.2.2 Discussion of the Indirect Estimation Procedure  
When fault detection is performed through parameter estimation of a discrete model, such as the SWA, faults can only be identified using indirect approaches. However, due to the higher complexity and nonlinearity of the relationship with the model parameters in this case, the accuracy of the method is directly affected by how much the fault magnitude deviates from nominal values.  
Here, too, the advantage of the continuous estimation method over the discrete one becomes evident, particularly in terms of the complexity of diagnosing multiplicative faults.  
Also, more accurate parameter estimation in the indirect identification procedure has been achieved by extracting only selected columns from the Jacobian matrix (matrix of partial derivatives), instead of applying the full matrix as suggested in the literature. This is supported by the analyzed case involving large multiplicative faults that significantly deviate from the nominal operating point, where the literature-proposed method proved inapplicable. In such cases of large faults, when the relationship between model parameters and actual physical parameters is linear, the proposed method provides highly accurate identification. In the case of a nonlinear relationship, the result remains approximate but is still more acceptable than the entirely inaccurate solution obtained using the literature-based approach.
## 2 Data Processing and Track Information Extraction of Floating Vehicle  
### Floating Car Data Characteristics and Preprocessing  
The floating car data used in this paper are mainly longitude, latitude and time, and its data format and examples are as follows:  
Taxi ID, latitude, longitude, passenger carrying state, time point, [speed];  
1, 30.4996330000,103.9771760000,1,2014/08/03 06:01:22, [65.1];  
1, 30.4936580000,104.0036220000,1,2014/08/03 06:02:22, [64.8];  
2, 30.6319760000,104.0384040000,02014/08/03 06:01:13, [66.5].  
The data uploaded by emergency rescue vehicles after the earthquake is also a form of floating vehicle data. Therefore, it is necessary to study the information extraction method from the data characteristics and error analysis of general floating vehicles.  
(1) Data cleaning  
In view of the small amount of data from 00:00:00 to 05:59:59 at night, and the travel time prediction in this period is of no practical significance, this paper excluded the data in this period. If the same vehicle stays for more than 5 min, the recorded data will also be removed. In addition, obvious outliers caused by data transmission need to be eliminated, such as missing part of data or records of longitude and latitude deviating too far from the road section.  
(2) Data filling  
Using fuzzy C- means (FCM) algorithm to fill the missing data of floating car [28]. At the same time, the FCM clustering as Algorithm 1, the filling process is summarized in the following steps:  
```
Begin  Setting template function precision \(\varepsilon\), fuzzy index \(m\) and maximum iteration number \(T_{m}\).  Initializing a fuzzy clustering center \(z_{i}\).  Repeat  Update the fuzzy partition matrix \(\text{U}=\{\mu_{ij}\}\) and the cluster center \(\text{z}=\{Z_{\text{C}}\}\). \(t\gets t+1\)  Until \((\left\lvert\,\middle\lvert f\right\rvert(t)-J(t_{-1})\,\middle\rvert<\varepsilon \text{ OR }c>T_{m})\)  The result of pixel classification is obtained from the obtained \(\text{U}=\{\mu_{ij}\}\).  Make use of cluster center \(z_{i}\) to fill the vacancy. End
```  
**Algorithm 1** FCM algorithm.  
The clustering results of floating car data collected from The Second Ring Road of Chengdu from 3 August to 5 August 2014, based on FCM clustering algorithm, are shown in Figure 1. Table 1 shows the velocity values of different clustering centers obtained according to FCM clustering algorithm; the missing data can be directly replaced by the cluster center value in the table. According to the results in these, vehicle speeds are concentrated in the range of 67.5-82.5, and the speed center value of different clusters is around 75.  
Figure 1: FCM clustering results.
# Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
## 3 Framework and Necessary Preparation
### Implementation of DBSCAN Algorithm  
Based on description of the basic DBSCAN algorithm, a density function is required to realize DBSCAN quantification, which can be denoted as the number of points within a given neighborhood. According to this idea, three types of points via this density function can be denoted, and are shown in Figure 2, namely core points (A), boarder points (B and C), and noise points (N). Assuming a threshold, \(thr\), if the density value is larger than \(thr\), the point is a core point. On the contrary, the point is a boarder point when it is in a core point's neighborhood, otherwise it is a noise point. Generally, both the core points and their neighbor boarder points are clustered into a cluster, but noise points are not.  
In [14], two parameters related to DBSCAN clustering were defined, the neighborhood size (\(s\)) and the density threshold (\(thr\)). Then, given a test point, \(p\), its density function can be expressed as \(N_{s}(p)\) in (2):  
\[N_{s}(p)=\text{Num}(\{q\in\text{T}\ \mid\ dis(p,q)\leq s\}) \tag{2}\]  
where \(q\) is another arbitrary point in \(T\); \(dis(p,q)\) is the distance between these two points (\(p\) and \(q\)); and \(Num(\)*) is denoted as the function to count the number of points. Then, all points can be categorized and grouped to clusters.
## 4 Results and Performance Discussion
### Performance Analysis
This can be improved using higher resolution images. It should be noted that standard deviation, inactive area, sensitivity peak, entropy, and kurtosis are the most sensitive parameters for Class 0 failure type. In contrast, skewness, standard deviation, and kstat parameters played a significant role in predicting Class 1 failures. Figure 6: The accuracy scores of the developed feature vectors for the three ML classifiers. Figure 7: Qualitative evaluation of the correct and wrong predictions based on the proposed algorithms actual and predicted class labels.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 4 Regulation of Virulence and Antibiotic Resistance
### Ribo-Regulation  
Clearly, RNA binding elements are responsible for post-transcriptional modifications, but RNA species are also responsible for ribo-regulation of virulence and antibiotic resistance genes. In termination-based ribo-regulators, the presence of antibiotic allows either read through or premature termination, ultimately, providing for production of antibiotic resistance [158]. Such mechanisms have been identified in _Bacillus subtilis_ (_vmlR_ and _bmrB_), _L. monocytogenes_ (_rli53_ and _rli59_ which control expression of _lmo 0919_ and _lmo1652_). Early termination by the riboswitch occurs in the absence of the antibiotic, but in the presence of limomycin causes a conformational change in the transcriptional terminator to induce the full expression of the antibiotic resistance gene [158]. Certain termination-based regulators act in _cis_ with the promoter. Cis-regulation of RNA elements are known as riboswitches or attenuators, which are often present in the gene's 5' UTR, can cause early termination of transcription by direct ligand binding in order to control bacterial gene expression. In the presence of different metabolite concentrations, the riboswitch structure is altered upon ligand binding, which allows the full expression of the gene due to destabilization of the terminator. Theophylline-sensitive synthetic riboswitches were used to induce gene expression of -galactosidase reporter in the intracellular pathogen _Francisella_. Riboswitching has also been shown to control the expression of FTN_0818 under nutrient limited environments and is required for replication in macrophages. FTN_0818 mutants growth was shown could be rescued in minimal media by riboswitch mediated control of FTN_0818 [159]. In another study, it was found that termination riboswitches regulated numerous antibiotic resistance genes in pathogenic bacteria, but when the antibiotic was present in the cell it allowed for read through or complete expression of the gene. _L. monocytogenes_ has 55 recognized riboswitches that respond to 13 distinct ligands to control virulence and antibiotic resistance [147]. A complete description of these riboswitches is beyond the scope of this review; however, the reader is directed to a 2015 review by [PERSON] et al on riboswitches that impact virulence and metabolism [160]. Suffice it to say that riboswitches are essential to gene regulation involved in bacterial physiology and virulence, and may provide potential targets for new drug therapies.
# Compatible Ground Motion Models for South Korea Using Moderate Earthquakes
###### Abstract  
Due to a heightened interest in the field of earthquakes after two moderately sized earthquakes occurred in Gyeongju and Pohang, this study explores which ground motion prediction equations are compatible for the South Korea region. Due to data availability, ground motions from five earthquakes of moderate magnitude were used for comparing against selected ground motion models. Median rotated response spectral ordinates at a period of 0.2 s were extracted from these ground motions, which served as a basis for comparison. Twelve ground motion models were considered from the Next Generation Attenuation West, West2, and East programs due to their extensive databases and robust analytical techniques. A comparison of relative residuals, z-score, and each event found that the subset of Next Generation Attenuation--East ground motion prediction equations did not perform as well as the suite of Next Generation Attenuation--West2 ground motion prediction equations, most likely due to the regional simulations involved in developing the database. Interestingly, the ground motion models that performed relatively well were from the set designed for rock conditions.  
ground motion prediction equations; ground motion models; South Korea; moderate earthquakes; Gyeongju; Pohang +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Analysis
### Base Classifiers and MFELCM
#### 4.2.2 1D-CNN Experimental Results
The reason why the classifier confuses the samples (see Figure 21) may be that the features of interest to 1D-CNN have some similarities between oil tankers and cargo ships, and between passenger ships and fishing boats. Figure 20: Learning curve (1D-CNN). Figure 21: (a) Confusion matrix of samples (1D-CNN); (b) Confusion matrix of ships (1D-CNN).
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 5 Experiment Setup
### Dataset Description  
The datasets considered for the study include retinal fundus images for glaucoma prediction. The datasets are denoted as \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{k}\) where k = 4 with the same \(K\) classes associated with client 1 to client m where m = 4. The datasets considered for the study are Riga [30], Dhristi [31], Rim-one [32], and HRF [33]. These datasets consists of two classes, class 0 for non-glaucoma and class 1 for glaucoma. Table 4 show the details of the datasets used for the study.  
Data pre-processing is performed in all clients to convert all dataset images into standard format because they may vary in size. To bring all images to the same size, a rescale function is used, which normalizes all pixel values to the range [0, 1]. A 20% shear and zoom is applied to the images on all clients as an initial pre-processing step.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Reference** & **Place** & **Resolution** & **No. of Images** & **Year** & **Format** & \
\begin{tabular}{c} **Camera** \\ **Specifications** \\ \end{tabular} \\ \hline D1 (RIGA) & [30] & Riyadh, Saudi Arabia & 2240 \(\times\) 1488 & 460 & 2018 & JPG and TIFF & NA \\ \hline D2 (Drishti) & [31] & Aravind Eye Hospital, Madurai & 2896 \(\times\) 1944 & 101 & 2015 & PNG & NA \\ \hline \multirow{6}{*}{D3 (Rim-one)} & \multirow{6}{*}{[32]} & Hospital & \multirow{6}{*}{2144 \(\times\) 1424} & \multirow{6}{*}{159} & \multirow{6}{*}{2011} & \multirow{6}{*}{NA} & Nidek \\  & & Universitario & & & & & \\ \cline{1-1} \cline{4-7}  & & de Canarias, & & & & & \\ \cline{1-1} \cline{4-7}  & & Clinico San Carlos & & & & & \\ \cline{1-1} \cline{4-7}  & & and Universitario & & & & & \\ \cline{1-1} \cline{4-7}  & & Miguel Server, & & & & & \\ \cline{1-1} \cline{4-7}  & & Spain & & & & & \\ \hline D4 (hrf) & [33] & Eye Clinic Zlin, Czech Rep & 3304 \(\times\) 2336 & 45 & NA & NA &
\begin{tabular}{c} Canon \\ CR-1 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset details.  
This study could be expanded to incorporate eye-tracking datasets, as they also exhibit quantity skew due to the limited number of images. The experiment can also be conducted on the optic disc detection dataset, which includes radiologists' gaze patterns as they locate optic discs in retinal fundus images [34; 35; 36]. Another dataset related to eye-tracking data focuses on analyzing how individuals with glaucoma navigate their environment, specifically examining how they perceive and process visual stimuli [37].
## 2 Materials and Methods
### Methods
#### 2.2.1 Preprocessing  
Each tile extracted from a Sentinel 2 product is \(10,980\times 10,980\) at 10 m resolution, meaning each pixel represents 10 m\({}^{2}\) of the Earth's surface area. We divide each image into 256 by 256 patches to balance computational efficiency and local feature representation. As we do not utilise a sliding window approach, there is a remainder of 228 discarded pixels along the right and bottom border. Opting for smaller patch sizes could increase the performance of the AE as fewer geographic features are present. While relatively large, the selection of patch size encapsulates enough local context to be discernible without the need to reference neighbouring patches and achieves a good visual representation within the final visualisation tool. The final dataset comprises 1764 images for each tile and a total of 28,224 images across all 16 locations. Sentinel 2 products are recorded in 13 different wavelengths all at varying spatial resolution. In our analysis, we focused exclusively on the red, green, blue, and near-infrared bands as they all record in the highest spatial resolution of 10 m.
## 3 Electricity Distribution Network Development
### Active Distribution Networks and Microgrid Concept  
According to [19], active distribution networks are defined as: \"Active distribution networks (ADNs) have systems in place to control a combination of DERs, defined as generators, loads and storage. With these systems in place, the ADN becomes an Active Distribution System (ADS). \"Microgrids are defined in standards, in which IEEE 2030.7 determines: \"A group of interconnected loads and distributed energy resources with clearly defined electrical boundaries that act as a single controllable entity concerning to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes\" [20]. Microgrids are also defined in IEC-TS 62898-1 [21].  
This paper assumes that microgrids will be widely used and developed in various installations, as envisioned in the recent power system development roadmap for the EU [4]. Roadmaps of the microgrids evolution are presented in [22; 23; 24; 25; 26] in which the microgrid is defined, the microgrid types are introduced, the critical microgrid technologies and functionalities are presented, and the expected benefits are discussed. The roadmaps for commercializing microgrids are presented in [27].  
A review of the microgrid development [28] highlights the arguments and drivers, which include policies and research investments, system operators' activity, various stakeholders, and incentives. One of the key drivers for microgrids' implementation is the more efficient integration of DERs. Their control and protection need novel technical solutions and applications. Standardization is essential, thus providing compliance of the different (vendors) solutions in control and protection. Key issues are data models and protocols. Based on feasible, proposed, and evaluated business cases, the operational targets can be set.
###### Abstract  
This paper presents a comprehensive analytical framework for investigating loss mechanisms and thermal behavior in high-speed magnetic field-modulated motors for flywheel energy storage systems. Through systematic classification of electromagnetic, mechanical, and additional losses, we reveal that modulator components constitute approximately 45% of total system losses at rated speed. Finite element analysis demonstrates significant spatial non-uniformity in loss distribution, with peak loss densities of \(5.5\times 10^{5}\) W/m\({}^{3}\) occurring in the modulator region, while end-region losses exceed central-region values by 42% due to three-dimensional field effects. Our optimized design, implementing composite rotor structures, dual-material permanent magnets, and integrated thermal management solutions, achieves a 43.2% reduction in total electromagnetic losses, with permanent magnet eddy current losses decreasing by 68.7%. The maximum temperature hotspots decrease from 143 \({}^{\circ}\)C to 98 \({}^{\circ}\)C under identical operating conditions, with temperature gradients reduced by 58%. Peak efficiency increases from 92.3% to 95.8%, with the \(\eta\) > 90% region expanding by 42% in the speed-torque plane. Experimental validation confirms model accuracy with mean absolute percentage errors below 4.2%. The optimized design demonstrates 24.8% faster response times during charging transients while maintaining 41.7% smaller speed oscillations during sudden load changes. These quantitative improvements address critical limitations in existing systems, providing a viable pathway toward high-reliability, grid-scale energy storage solutions with extended operational lifetimes and improved round-trip efficiency.  
f  
ywheel energy storage; high-speed motors; magnetic field modulation; loss mechanisms; thermal analysis; electromagnetic-thermal coupling; finite element analysis; efficiency optimization; experimental validation; permanent magnet machines