esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
## 4 Conclusions  
In this work, a fuzzy tracking and control algorithm was successfully developed for a single-channel SSVEP-based BCI system. The SSVEP can be successfully elicited by visual stimulation on an LCD monitor with a 60-Hz refresh rate. A fuzzy tracking algorithm was able to identify the changes of EEG signals accurately. Because the quality of EEG signals for a single-channel BCI system is low, this fuzzy tracking algorithm can improve quality by tracking a previous status. Thus, the recognition rate of the BCI system with the fuzzy tracking algorithm can be clearly improved when compared with that of a CCA baseline system. For frequencies lower than 10 Hz, the recognition curves for the CCA baseline and the proposed approach show that the recognition rates were degraded when the stimulus frequency was increased because the SNR of the peak value for a high stimulus frequency was lower than that of the low stimulus frequency. The proposed approach was successfully implemented as a BCI-based interface to control an air swimmer drone vehicle, and users were interested in this application. Fundamentally, this self-developed single-channel SSVEP-based BCI system, with its fuzzy tracking and control algorithm, can be used by general subjects without prior training. Because the computational complexity of the proposed approach is relatively simple, this approach can be implemented as a standalone embedded BCI system in the near future with numerous applications such as text input systems, neuroimaging, and home appliance control. After this single-channel SSVEP-based BCI system has finished its performance tests, a more convenient and cheaper EEG acquisition device with dry electrodes could be adopted and integrated with visual stimulators and a new standalone signal processing module.  
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contracts MOST 104-2627-E-006-001 and MOST 104-2221-E-218-019.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents, materials, and analytical tools; [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper.  
The authors declare that there is no conflict.
## 4 Validation
### Validation Datasets
#### 4.1.2 Independent Random Samples  
A sub-area encompassing the southern part of Vinton County in southeast Ohio (Figure 1) was selected to validate the performance of the proposed algorithm. According to previous studies [7, 32], the land-cover change in this area is highly dynamic as a result of human activities such as logging and reforestation, industrial mining and abandonment, and agricultural use and abandonment. The sub-area contains 60 \(\times\) 80 columns/rows on a 250-m MODIS image, from which 244 pixels were randomly selected. Major land-cover changes contained in these time series include transitions from forest to bare soil/mining field, forest to cropland, forest to bare soil/mining field and then to grassland, bare soil to grassland, and shrubs to bare soil/mining field. At each of the 244 pixels, the change date(s) of the time series were manually identified to serve as \"ground truth\" by visual inspection. A series of 94 Landsat images were downloaded from the USGS Landsat archive for the period from 2000 to 2011, including Landsat 5 TM scenes from 2000 to 2011 and Landsat 7 ETM+ scenes from 1999 to 2003. Only images with clear skies over the sub-area were selected. When identifying change dates from the Landsat time series, we tried to convert the time index between Landsat and MODIS as accurately as possible. Nevertheless, it is challenge to obtain the actual \"truth\", and the Landsat images do not correspond perfectly to MODIS images in many cases because quite a few of the Landsat images were unavailable due to cloud contamination and/or satellite malfunction. In addition, the exact change date may be confused given that the MODIS 16-day composite data could miss land-cover changes if the changes occurred within the 16-day composite period [17].  
Figure 4: IGBP (International Geosphere–Biosphere Program) stable samples (**left**); grouped land-cover types (**middle**) and simulated changes (**right**).
# A Fundamental Study on Circularly Polarized Double-Loop Antennas with Quasi-Two Sources
## 5 Experimental Work  
Up to this point, we have discussed an antenna with a straight feedline \(B\)-\(F_{2}\), as shown in Figure 6b. This section modifies the straight feedline into a crank one for impedance matching.  
Figure 10 shows the antenna configuration with the crank feedline \(B\)-\(F_{2}\), specified by parameters (\(\ell_{1}\), \(\ell_{2}\)) [9]. We selected the crank parameters for a VSWR of less than 2. The other configuration parameters were held at the same values as those for the straight feedline \(B\)-\(F_{2}\)' in Section 4.  
Figure 8: Simulated frequency responses of axial ratio and gain for an inner parasitic loop with \(S=0.03\lambda_{0}\).  
Figure 7: Simulated axial ratio and the selected loop perimeter \(P\) versus loop spacing \(S\) for an inner parasitic loop.  
Figure 9: Simulated radiation patterns for an inner parasitic loop with \(S=0.03\lambda_{0}\) at \(f=f_{0}\). (**a**) \(\phi=0^{\circ}\) plane. (**b**) \(\phi=90^{\circ}\) plane.  
The simulated frequency response of the VSWR for \((\ell_{1},\ell_{2})=(0.02\lambda_{0},0.24\lambda_{0})\) is shown with a solid line in Figure 11, together with the axial ratio and gain. The VSWR was evaluated for a 50 \(\Omega\) coaxial line. It was found that the VSWR and axial ratio are less than 2 and 3 dB, respectively, in a 3 dB gain drop bandwidth of 31% with a peak gain of 7.1 dBi. The simulated radiation patterns are shown with solid and dotted lines in Figure 12. The antenna radiates a CP beam like that of the straight feedline \(B\)-\(F_{2}\)\({}^{\prime}\) (see Figure 9).  
The abovementioned simulated results were verified by experimental work. An antenna was fabricated at \(f_{0}=3\) GHz using a ground plane of \(5\lambda_{0}\times 5\lambda_{0}\). Figure 13 shows photographs of a prototype. The experimental results are shown with small circles and dots in Figures 11 and 12. An agreement was observed to exist between the experimental and simulated results. Note that the radiation efficiency may theoretically be 100%, since there are only conductor losses that are known to be negligible up to a 12 GHz band. Also, note  
Figure 11: VSWR, axial ratio, and gain versus frequency for an inner parasitic loop with a crank feedline \(B\)-\(F_{2}\)\({}^{\prime}\).  
Figure 10: Antenna with an inner parasitic loop having a crank feedline \(B\)–\(F_{2}\)\({}^{\prime}\). (**a**) Perspective view. (**b**) Side view in the \(y\)–\(z\) plane.  
that the experimental work was performed at \(f_{0}\) = 3 GHz, where we can make a prototype by hand, and our experimental equipment works well to verify the simulated results. As seen in Figure 13, the wires used in the prototype may be thick since we took the wire radius to be the same as that in [9] to facilitate a comparison.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 2 Analysis
### OTDR Receiver Noise Floor Reduction
#### 2.4.2 OA/OC/OTDR Dynamic Range Prediction Model  
From (13), analogously to (14), the noise figure of the whole cascade consisting of the 4-port OC, OA and OTDR, is [12]:  
\[F_{\text{OC+OA+OTDR}}=F_{\text{OC+OA}}+(F_{\text{OTDR}}-1)/G_{\text{OC+OA}} \approx F_{\text{OC}} \tag{15}\]  
where we considered \(G_{\text{OC+OA}}\approx G_{\text{OA}}>>A_{\text{OC}}\) (quite justifiably as \(G_{\text{OA}}\) is of the order of \(10^{3}\) and thereby much larger than the OC insertion loss \(A_{\text{OC}}\)).  
Thereby, recalling (14), from (15) it is obvious that [12]:  
\[F_{\text{OC+OA+OTDR}}\approx A_{\text{OC}}\cdot F_{\text{OA}}<<F_{\text{OTDR }}=A_{\text{DC}}\cdot F_{\text{APD}} \tag{16}\]  
i.e., that introducing the OTDR front-end optical pre-amplifier significantly reduces the noise figure, and thereby widens the dynamic range \(\Delta DR\) as it implies from (10):  
\[\Delta DR=[P_{\text{BS}}(0)-NEP_{\text{OC+OA+OTDR}}]-[P_{\text{BS}}(0)-NEP_{ \text{OTDR}}]=NEP_{\text{OTDR}}-NEP_{\text{OC+OA+OTDR}} \tag{17}\]From (12), it is obvious that, for certain input noise power \(P_{\rm ni}\), the noise floor \(NEP_{\rm OIDR}\) for the OTDR alone is:  
\[NEP_{\rm OIDR}=(F_{\rm OIDR}-1)\cdot G_{\rm OTDR} \tag{18}\]  
whereas for the cascaded OC, OA and OTDR, it is:  
\[NEP_{\rm OC+OA+OTDR}\approx NEP_{\rm OA}=(F_{\rm OA}-1)\cdot G_{\rm OA}\cdot P _{\rm ni} \tag{19}\]  
So, the noise floor reduction (and consequent dynamic range extension) as a benefit of inserting the preamplifier, can be obtained if we divide (18) by (19), or make the according subtraction in dBs, which finally provides the following prediction [12]:  
\[\Delta DR=(P_{\rm SOA+OTDR}\ /P_{\rm noA+OTDR})/1=G_{\rm OA}\cdot NEP_{\rm OIDR }/(G_{\rm OTDR}\cdot NEP_{\rm OA}+NEP_{\rm OIDR}) \tag{20}\]  
By adopting common values for the relevant parameters of interest, specifically, the \(NEP_{\rm OIDR}\) to be close to \(-50\) dBm, \(G_{\rm OA}\) of 30 dB and \(F_{\rm OA}\) of 6 dB, resulting with \(NEP_{\rm OA}=447\times 10^{-5}\) nW, whereas considering \(G_{\rm OTDR}=10\) dB, (20) provides the predicted dynamic range extension value of \(\Delta DR\approx 30\) dB [12].  
Thereby, the high-SNR condition for accurate BER floor estimation is fulfilled with large credibility.
# _Applied Sciences_
## 2 Methods and Materials
### Obfuscation Method Based on a Variational Autoencoder
Any of the channels can then be used for a subsequent DL model training on obfuscated images. From the trained VAE, only the encoder is retained as a black-box obfuscation tool. As there is no need for a reconstruction once an image has been obfuscated, the decoder is discarded. Figure 1 displays the workflow described for the obfuscation method based on a VAE. For our experiments, the VAE is trained on the Medical MNIST dataset [29]. The dataset contains 6 classes of X-ray images, that are randomly distributed for training (30,000 images) and validation (12,000). More details about the Medical MNIST dataset are presented in Section 2.5. During training, the 64 \(\times\) 64 images are passed through three convolutional layers of 32, 8, and 4 filters, respectively, with a 3 \(\times\) 3 receptive field. ReLU is the activation function chosen for each layer. The architecture of the decoder consists of three convolutional, ReLUactivated layers of 4, 8, and 32 filters, followed by one dense layer. The VAE is trained for 10 epochs. The trained encoder can be used for obfuscating medical images. A channel option must be selected, depending on the desired result. The first channel, corresponding to the mean of the normal distribution, usually assures a better privacy level than the second channel, as it does not preserve as much detailed information from the input. This limits, though, its usefulness in further AI-algorithms. The channel corresponding to the standard deviation of the normal distribution tends to preserve more useful information from the original image. As a result, it is preferred in cases where the obfuscated images would be used in machine learning tasks. This channel, although depending on the initial structure of the original image, may or may not ensure the imposed or desired level of privacy. For example, in the encoding of an image with a monochromatic background, most probably sensitive details will be visible, which could uncover the nature of the original image. Such an example is shown in Figure 2, where the original image, representing a coronary angiography, has an almost monochromatic background. As a result, in the image obtained from channel 2, the main vessel can be seen.
## 2 Materials and Methods
### HPLC Quantification  
High-performance liquid chromatography (HPLC) was used to analyze the content of phenolic and flavonoids accumulated in _Aronia_ cell biomass. The used HPLC system was Waters 1525 Binary Pump and Waters 2484 Detector. Separation was performed on Supelco Discovery HS C18 column (5 \(\upmu\)m, 25 cm \(\times\) 4.6 mm). The elution solvents were: Solvent A (1% acetic acid) and Solvent B (methanol). Gradient elution at 1.0 mL/min flow rate was used as described previously [54]. For compounds quantification, standard calibration curves (gallic acid, protocatechic acid, chlorogenic acid, caffe acid, ferulic acid, vanillicacid, syringic acid, p-coumaric acid, salicylic acid, rosmarinic acid, catechin, epicatechin, hesperidin, rutin, quercetin and kaempfer) were used.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 3 Silicon on Insulator (SOI) Detector for Microdosimetry
### Charge Collection Characterisation of SOI Mushroom Microdosimeters  
#### 3.3.1 Ion Beam-Induced Charge Collection (IBIC) Technique at ANSTO  
The charge collection properties of the Mushroom microdosimeters with different structures were investigated using the ion beam-induced charge collection (IBIC) technique at the 6-MV accelerator SIRIUS at the Australian Nuclear Science and Technology Organisation (ANSTO) [23,24]. A microbeam of 1.78, 3 and 5.5 MeV He\({}^{2+}\) ions were raster scanned over the surface of the microdosimeter. The spot size of the microbeam was approximately 1 \(\upmu\)m. The position \"\(x\)\" and \"\(y\)\" of the microbeam and the deposited energy \"E\" for each event were processed into a list mode file. The median energy maps of the charge collection of the devices were then produced. To calibrate the energy deposited into the microdosimeter, a calibrated pulse generator was used. The pulse generator was calibrated using a silicon \(p\)-i-n photodiode with 100% charge collection efficiency (CCE) in response to 1.78 and 5.5 MeV He\({}^{2+}\) ions.
## 4 Results
### Use Case #2: Voltage Arcing Event Prediction Results  
The RMS histograms are calculated using a window size of 60 cycles, 960 samples, a step size of 30 cycles or a total of 480 samples, and 256 bins per histogram. The results presented in Figure 5 show the absolute difference in RMS variance between Phase \(A\) and Phase \(B\) histograms at intervals of five, Figure 5a, ten, Figure 5b, thirty, Figure 5c, and sixty, Figure 5d minutes over a twenty-four-hour period. The arcing events occurred between these two phases, but a similar approach can be applied to arcing between any combination of two or more phases. DFR-triggering arcing events occur six and four hours before the third event at \(t=0\). IFP necessitates the detection of the significant voltage variations that can occur during an arcing event. These voltage variations are due to increases or changes in the resistance for a loose connection.  
The vector-based metric for five-minute generated RMS histograms is shown in Figure 5a. This shows that the RMS variance difference is largest 24, 22, 19.5, and 7 h before the first arcing event; however, the difference is erratic, making IFP difficult because there is no clear point at which arcing is definitively predicted due to the presence of multiple high-variance points.  
Figure 5b shows the vector-based metric for RMS histograms generated every ten minutes. The absolute difference is smoother than the five-minute interval case, Figure 5b, but remains sufficiently erratic to minimize its IFP value.  
The absolute difference in RMS variance calculated using RMS histograms generated every thirty minutes is shown in Figure 5c. The RMS variance's absolute difference is smoother than in the previous two cases, offering a lower initial value 24 h before the third arcing event. The absolute difference is less than 10 V 23.5 h before the last event and remains less than 10 V until 15 h, before peaking 13.5 h before the final event and 6.5 h before the first arcing event. Despite its IFP potential, the thirty-minute-based results are still not a good predictor of arcing because the highest absolute difference in RMS variance occurs 18 h and 24 h before the first and last arcing events.  
The vector-based metric for sixty-minute-generated RMS histograms is shown in Figure 5d. The absolute difference does not exceed 20 V until 13 h before the final event and 7 h before the first event. The single maximum occurs 7 h before the first event provides an IFP potential that utility personnel can act on. As noted at the end of Section 4.1, the sixty-minute-based histograms are advantageous because they require the least memory.
## 2 Identification Methods
### Optimal Combined Strain Influence Line
In the following, the proportional selection operator \(p_{k}\) is adopted to determine the likelihood of descendants being left behind. The probability that the \(k\)th individual will be selected in the next generation is calculated by fitness function as follows. \[p_{k}=\frac{f_{c}(\mathbf{X}_{k})}{\sum\limits_{k=1}^{M}f_{c}(\mathbf{X}_{k})} \,,\;k=1,2,\ldots,\text{M} \tag{14}\]  
As improved individuals are selected based on selection operator \(p_{k}\), the advantages of the previous generation are retained in the new generation. The calculation is repeated until the condition of convergence is met; thus, the optimization solution can be obtained. The basic principle of GA is shown in Figure 3.  
The optimal combined strain response is used to identify the combined strain influence line according to Equation (15):  
\[\mathbf{I}_{\text{z}}=\mathbf{A}^{+}\mathbf{\epsilon}_{\text{z}}=\left( \mathbf{A}^{\text{T}}\mathbf{A}\right)^{-1}\mathbf{A}^{\text{T}}\mathbf{ \epsilon}_{z} \tag{15}\]  
where \(\mathbf{\epsilon}_{z}\) and \(\mathbf{I}_{\text{z}}\) denotes combined strain responses and combined strain influence lines, respectively. To reduce noise interference, multiple transverse positions loading can be performed to identify the corresponding combined strain influence lines, and the results are averaged.
# High-Accuracy Clock Offsets Estimation Strategy of BDS-3 Using Using Multi-Source Observations
###### Abstract  
Satellite clock offsets are the critical parameters for The Global Navigation Satellite Systems (GNSSs) to provide position and timing (PNT) service. Unlike other GNSSs, BDS-3 uses the two-way superimposition strategy to measure satellite clock offsets. However, affected by some deficiencies of the two-way superimposition strategy, the accuracy of BDS-3 clock offsets parameters is 1.29 ns (RMS), which is the main bottleneck for BDS-3 to improve its space signal accuracy. After analyzing problems in the clock offsets measurement process of BDS-3, the paper proposes a new strategy to real-time estimate high-accuracy satellite clock offsets. The clock offsets estimated by the new strategy show a good consistency with GBM clock offsets. The averaged STD of their differences in MEO is 0.14 ns, and the clock offsets estimated by the new strategy present less fluctuation in the 1-day fitting residuals. Applying the new clock offsets to prediction, BDS-3 can reduce its clock offsets errors from 1.05 ns to 0.29 ns (RMS), about 72%. The above results indicate that the new clock offsets estimated strategy can improve the accuracy of clock offsets parameters of BDS-3 effectively.  
BDS-3; clock offsets estimated; inter-satellite links; two-way satellite time and frequency transfer 2022
## 2 Materials and Methods
### Measurement of Photocatalytic Activity  
The photocatalytic activity of the metal titanate nanorods was determined in the process of toluene degradation in the gas phase. Toluene, an important volatile organic compound (VOC), was used as a model air contaminant. The photocatalysts' activity tests were carried out in the flat stainless steel reactor (V = 30 cm\({}^{3}\)) equipped with a quartz window, two valves, and a septa. As irradiation sources there were used two arrays of 25 LEDs (\(\lambda_{\text{max}}\) = 375 nm, light intensity 54 mW/cm\({}^{2}\), emission spectrum in the range 326-404 nm Figure S1) and \(\lambda_{\text{max}}\)= 400 nm (light intensity 11 mW/cm\({}^{2}\), emission spectrum in the range 376-428 nm Figure S2). In a typical measurement calculated mass of semiconductor powder was suspended in a small amount of water and loaded as a thick film on a glass plate (3 cm \(\times\) 3 cm) using the painting technique. The obtained semiconductor's coated support was dried and then placed at the bottom side of the photoreceptor followed by closing the reactor with a quartz window. The gaseous mixture from a cylinder was passed through the reactor space for 1 min. The concentration of toluene in a gas mixture was about 200 ppm. After closing the valves, the reactor was kept in the dark for 30 min to reach adsorption equilibrium. A reference sample was taken just before starting irradiation. To estimate toluene concentration, the samples were taken every 20 min during 60 min of irradiation. The analysis of toluene concentration in the gas phase was carried out using a Perkin Elmer Clarus 500 GC (Perkin Elmer, Waltham, MA, USA) equipped with a 30 m \(\times\) 0.25 mm Elite-5 MS capillary column (0.25 m film thickness) and a flame ionization detector (FID). The samples (200 \(\mu\)L) were injected by using a gas-tight syringe. Helium was used as a carrier gas at a flow rate of 1 mL/min.
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 4 Discussion
Conversely, pollutants are not dispersed effectively during low-wind conditions and tend to accumulate, lingering within the city and leading to higher pollution levels overall. Decision support systems rely on real observational data to update, calibrate/validate model predictions, and provide timely information to stakeholders. HPC facilitates the rapid use of large volumes of observational data in air quality models, enabling the generation of up-to-date forecasts and assessments: HPC systems leverage parallel processing architectures to distribute computational tasks across multiple processors or nodes, enabling the faster execution of simulations and data processing tasks. The parallel strategy by running multiple simulations with varying parameters simultaneously across different scenarios and pollutants allowed us to utilize the full computational capacity of CR54 HPC systems, reducing simulation times and improving the responsiveness of AERQ DSS. By enabling scalability and flexibility, the HPC system supports complex simulations, large-scale data analysis, and the generation of actionable insight. The AERQ DSS distinguishes itself from similar platforms, such as AirNow and CAMS, through its modular and scalable design specifically tailored for urban air quality management. Unlike these platforms, which primarily focus on regional-scale domains, data aggregation, and visualization, AERQ incorporates high-resolution modeling (AERMOD) combined with HPC capabilities to support simulations, scenario development, and detailed analysis. Its user-friendly interface, job-on-demand application, and support for localized assessments make it a comprehensive and adaptable tool for addressing urban air quality challenges.