esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Conclusion  
We proposed several enhancements for the PHOCS algorithm that builds hierarchies from flat clusterings in order to enhance classification accuracy. To build a better hierarchy we used absolute values of errors, employed the number and structure of nodes, cluster sizes and individual performance values for each cluster. We changed the stopping criteria as well to compare our hierarchy with a flat one.  
The experimental study shows effectiveness of the enhancements. We have removed one of the parameters as well. As a result, it has become easier to use the PHOCS.  
Our future work will be related to improving the performance estimation function and adding a possibility to return to a previous layer in case of false prediction. We would like to use or create a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset name**} & \multirow{2}{*}{**Classifier topology**} & \multicolumn{3}{c}{**MICRO**} & \multicolumn{3}{c}{**MACRO**} \\ \cline{3-8}  & & **F1** & **P** & **R** & **F1** & **P** & **R** \\ \hline \multirow{3}{*}{Mediamill} & Flat & 0.54 & **0.66** & 0.45 & 0.10 & 0.24 & 0.08 \\  & PHOCS v1 & 0.53 & 0.58 & **0.52** & **0.13** & 0.24 & **0.14** \\  & PHOCS v2 & 0.53 & 0.55 & **0.52** & 0.11 & 0.22 & 0.10 \\ \hline \multirow{3}{*}{Bibtex} & Flat & 0.31 & **0.81** & 0.19 & 0.14 & 0.40 & 0.11 \\  & PHOCS v1 & 0.37 & 0.61 & 0.21 & **0.22** & 0.38 & **0.18** \\  & PHOCS v2 & **0.39** & 0.65 & **0.28** & **0.22** & **0.44** & **0.18** \\ \hline \multirow{3}{*}{Medical} & Flat & 0.80 & 0.85 & 0.75 & 0.26 & 0.32 & 0.25 \\  & PHOCS v1 & 0.82 & 0.84 & **0.81** & **0.30** & 0.34 & **0.30** \\  & PHOCS v2 & **0.83** & 0.86 & 0.79 & **0.30** & 0.34 & **0.30** \\ \hline \multirow{3}{*}{Enron} & Flat & 0.46 & **0.66** & 0.35 & 0.09 & 0.13 & 0.08 \\  & PHOCS v1 & **0.50** & 0.62 & 0.42 & 0.10 & 0.15 & 0.09 \\  & PHOCS v2 & **0.50** & 0.55 & **0.46** & 0.11 & 0.15 & **0.11** \\ \hline \multirow{3}{*}{Yeast} & Flat & 0.59 & 0.61 & 0.58 & 0.38 & 0.41 & 0.38 \\  & PHOCS v2 & 0.59 & 0.59 & 0.60 & 0.39 & 0.39 & 0.39 \\ \hline \multirow{3}{*}{Genbase} & Flat & 0.97 & 1.00 & 0.95 & 0.67 & 0.70 & 0.65 \\  & PHOCS v2 & 0.98 & 1.00 & 0.96 & 0.68 & 0.70 & 0.67 \\ \hline \multirow{3}{*}{Corel5k} & Flat & 0.04 & **0.24** & 0.02 & 0.00 & 0.01 & 0.00 \\  & PHOCS v2 & **0.09** & 0.18 & **0.06** & 0.01 & 0.01 & 0.00 \\ \hline \multirow{3}{*}{CAL500} & Flat & 0.37 & **0.50** & 0.30 & 0.10 & 0.13 & 0.10 \\  & PHOCS v2 & **0.40** & 0.37 & **0.43** & **0.16** & **0.16** & **0.20** \\ \hline \multirow{3}{*}{Scene} & Flat & 0.61 & 0.67 & 0.57 & 0.52 & 0.61 & 0.50 \\  & PHOCS v2 & 0.63 & 0.66 & 0.59 & 0.54 & 0.61 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PHOCS experiment results.  
classifier that can take advantage of a hierarchy. Another goal is to compare our algorithm with EkNN [3] that has won the second Pascal challenge.
## 2 Materials and Methods
### Application of the SCP Filter  
In the GPM laboratory (University of Rouen Normandie), we developed a \"Selection of Contiguous Pixels\" (SCP) algorithm to filter images containing a SIMS signal and select the spatially contiguous non-zero pixels. These pixels were considered to be true positive pixels. The principle was as follows: for each pixel with coordinates (x, y, z) whose signal was non-zero, we looked for another non-zero pixel in the coordinate interval (\([\mathrm{x}-1;\mathrm{x}+1]\); \([\mathrm{y}-1;\mathrm{y}+1]\); \([\mathrm{z}-1;\mathrm{z}+1]\)). If such a pixel existed, then the pixel with coordinates (x, y, z) was \"selected,\" and its value was preserved; otherwise, its value was replaced by zero. All selected pixels were then used to reconstitute an image named the \"SCP filtered\" image.
# Electric Stimulation of Astaxanthin Biosynthesis in _Haematococcus pluvialis_
## 1 Introduction  
Astaxanthin (3.3'-dihydroxy-\(\beta\)-carotene-4,4'-dione) is a common high-value keto-carotonoid pigment (-US $7150 per kg) involved in various metabolic functions, including anti-oxidative, anti-inflammatory, and immune-stimulatory functions [1, 2]. The green microalga _Haematococcus pluvialis_ produces astaxanthin in large quantities (-4% of dry cell weight) at the dormant palanospore (cyst) stage and has been studied extensively as a model strain in research settings as well as in industrial applications [3, 4].  
Algal astaxanthin synthesis is usually induced as a defense mechanism against environmental oxidative stress [2, 5, 6]. Various stress-inducing conditions, such as nutrient starvation [7, 8], exposure to intense light [9, 10], high temperatures [11, 12], high salinity [13, 14], and nanomaterial addition to culture [15], have been applied to increase the content and productivity of algal astaxanthin. Among them, nitrogen-nutrient depletion has been employed extensively for effective astaxanthin induction in _H. pluvialis_ cultures [2, 16]. However, this approach is time-consuming and associated with a risk of biocontamination from external sources; moreover, it significantly reduces the biomass productivity of _H. pluvialis_[17]. Therefore, the development of an effective astaxanthin induction strategy remains a major challenge in _H. pluvialis_ biorefinery approaches.  
Electric stimulation or induction, a relatively new technique in algal biorefinery approaches, involves the application of an electric current to live algal cells through the anode and cathode of a bioelectrochemical system. Recently, [PERSON] et al. [18] treated _H. pluvialis_ seed cells at 100 mA (voltage: 25 V) for a short duration (1 min) in a single-chamber electrochemical system. After cultivation for seven days, the cell number density of the electro-treated cells increased by 20%, a considerable increase compared to the cell number density of untreated cells, owing to the enhancement of nitrogen consumption and chlorophyll biosynthesis in _H. pluvialis_ cells. However, the intracellular astaxanthin content remained almost constant regardless of the treatment. Recently, we reported the effective induction of triacylglycerglycerols (TAGs) and total fatty acids in _Chloella_ species by the application of a mild cathodic current of 31 mA for 4 h, which is a relatively long duration, in a two-chamber bioelectrochemical system [19]. Compared to that in the untreated control, the TAG and total fatty acid contents in electro-treated _Chloella_ cells increased by 210% and 24%, respectively. Of note, the astaxanthin and fatty acid biosynthesis pathways are closely related in _H. pluvialis_, and astaxanthin is present in TAG bodies mostly in mono- and di-ester forms with fatty acids [2; 20]. In connection with the previous study on algal lipids [19], in this study, we aimed to investigate the technical feasibility of using electric stimulation to improve astaxanthin accumulation in _H. pluvialis_ using a two-chamber bioelectrochemical system.
## 3 Simulation Results
### Radar-Based Deep Learning
The DNN's estimated range values of 30.51 m and 306.297 m are closer to the actual target range of 30 m and 305 m compared to the traditional approach's estimates of 31.02 m and 306.317 m. This indicates that the DNN model has learned to make more accurate predictions for target range by leveraging a larger dataset and its inherent learning capabilities. Furthermore, the results show that the range RMSE curves for the two cases, the traditional and DNN approaches, have the same trend at a high SNR regime, suggesting a consistent performance improvement by the DNN approach even in challenging signal conditions. * **Velocity Estimation:** Similarly, the DNN approach achieves better velocity estimation compared to the traditional approach. The DNN's estimated velocity values of 31.250 m/s and 134.442 m/s are closer to the actual target velocity of 30 m/s and 140 m/s, whereas the traditional approach's estimates of 31.746 m/s and 134.421 m/s deviate further from the actual value. This suggests that the DNN model is more adept at predicting target velocity accurately by effectively capturing subtle variations in the signal. Additionally, the results show that the velocity RMSE curves for the two cases, the traditional and DNN approaches, have the same trend at a high SNR regime, indicating consistent improvement in the velocity estimation by the DNN approach across different signal strengths.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 3 Methodology  
In the first step of the proposed method, the channels undergo Savitzky-Golay filtering, which enhances the SNR without causing significant information loss. Subsequently, the signals are decomposed into their spontaneous alpha and beta bands using DWT to decrease the dimensions and volume of the data. Features such as mean, variance, skewness, kurtosis, entropy, and energy undergo extraction within 1 s epochs, overlapping by half intervals. Then, the training of a binary classifier using a linear kernel SVM is conducted, followed by the classification of a set of test signals. Validation is carried out using cross-validation. A diagram illustrating the method outlined is shown in Figure 3.
## 5 Results  
Assume that the diameter of the transmitter and receiver of the inter-satellite terahertz communication system is 0.5 m, the carrier frequency is 180 GHz, and the link transmission distance is 100 km. In this scenario, through the simulation of the above models, the relationship between the pointing error angle, beam radius at the transmitter and signal power attenuation is obtained, as shown in Figure 8.  
It can be seen from Figure 8 that when the pointing error angle and the beam radius of the transmitting end are different, as the beam radius increases, the signal power attenuation increases continuously. As the pointing error angle increases, the signal power attenuation also increases. In addition, as the beam radius continues to increase, the signal power attenuation changes more and more slowly with the pointing error. This shows that when the beam radius becomes larger, the effective radius of the beam reaching the receiving end also increases, and the power attenuation caused by the pointing error increases relatively slowly.  
In this section, STK software is used to simulate the motion scenarios of low-orbit satellites and geostationary satellites affected by perturbation forces in space. The variation of the pointing error angle with the satellite running time is analyzed. The relationship between the pointing error angle and the azimuth and elevation angles of the transmitter is studied. Taking a low-orbit satellite and a geostationary satellite as an example, the pointing error angle, signal power attenuation, and signal-to-noise ratio of the terahertz communication system are simulated based on the satellite perturbation model and the inter-satellite terahertz communication link model. The effect of pointing error on the inter-satellite terahertz communication link is analyzed by simulation.
## 3 Results and Discussion
### Characteristics of Filter Residues  
#### 3.6.1 Magnetic Response Analysis of Filter Residues in Solution and After Heat Treatment  
To investigate the influence of different preparation conditions on the magnetic characteristics of filter residues, this study systematically compared magnetic property changes under four distinct treatment conditions (as shown in Table 1). Single ferrite treatment samples (A1) showed no magnetic properties in both solution state and after 100 \({}^{\circ}\)C drying, only exhibiting complete magnetic attraction after calcination at 650 \({}^{\circ}\)C. This indicates that simple ferrite treatment alone cannot form magnetic iron oxide structures. With the introduction of H\({}_{2}\)O\({}_{2}\) pretreatment (B1), slight magnetic response emerged in solution, suggesting that H\({}_{2}\)O\({}_{2}\) addition promoted initial formation of magnetic species. However, this magnetic characteristic disappeared after drying at 100 \({}^{\circ}\)C, possibly due to the reorganization of nascent magnetic phases under mild heating. Interestingly, the sample regained near-complete magnetic attraction capability after calcination at 650 \({}^{\circ}\)C, indicating that high-temperature calcination favors the formation of stable magnetic phases. Samples treated with H\({}_{2}\)O\({}_{2}\) pretreatment combined with stepwise ferrite addition (A2) demonstrated the most desirable magnetic evolution process. Partial magnetic separation of precipitates was observed in solution state, with magnetism further enhanced after 100 \({}^{\circ}\)C drying, and complete magnetic attraction achieved after 650 \({}^{\circ}\)C calcination. This progressive enhancement of magnetic properties suggests that the synergistic effect of H\({}_{2}\)O\({}_{2}\) pretreatment and stepwise ferrite treatment facilitates the formation and growth of stable magnetic phases. Samples with pure stepwise ferrite addition strategy (B2) exhibited the strongest magnetic characteristics in both solution and dried states, achieving complete magnetic control of precipitates. However, unexpectedly, these samples completely lost their magnetic properties after calcination at 650 \({}^{\circ}\)C. This phenomenon suggests that without H\({}_{2}\)O\({}_{2}\) pretreatment, magnetic phases formed through stepwise ferrite treatment may undergo unfavorable phase transitions at high temperatures. Supplementary videos demonstrate the magnetic response of filter residues prepared under different conditions in solution state and after heat treatment.
## 3 State-of-the-Art
### Big Data Analytics  
The BDA could be used for identifying the pattern and correlation of fuel consumption with respect to the environment and ship data recorded, to improve the fuel efficiency through optimal vessel speed and voyage route. A research study on vessels fuel consumption by the use of BDA has been conducted in [29; 30], where it is reported that the power, and thus the fuel required to propel the ship through water depends on the trim of the vessels. The optimum ship speed during the time of vessel delivery for fuel consumption changes over time due to a variety of factors such as engine wear, coating of vessels, etc. The use of BDA can help shipowners determine the optimum speed for fuel consumption, taking into consideration factors such as bunker cost, freight rates and schedules [31]. The use of BDA to calculate potential fuel savings can provide ship owners with detailed insight on all aspects of vessel operations impacting the fuel efficiency to inform on the return of investment (ROI) decision. Additionally, the Fujitsu Laboratory has developed technology that uses BDA for large ships to estimate fuel efficiency, speed and other performance in actual sea conditions in 2016 [32]. Other research on suggesting optimal vessel speed decisions in maritime logistics using weather big data has also been conducted [33; 34]. It is also evidenced by a report in [35] that BDA has helped shipping lines such as Maersk in cutting its fuel consumption by 13%. Big data analytics are also used to investigate the speed optimization process for large container ships [36].
## 3 Experimental Tests and Results  
### Samples and Experimental Setup  
Two experimental campaigns were carried out: one with a CFRP sample without a defect and another test with a sample with a defect, which corresponded to two cases of the numerical simulations (defect-free and a centrally located defect, respectively). The adhesive used in the study was Araldite(r) 2015, which is a two-component epoxy paste adhesive. It is particularly suitable for SMC and GRP bonding. The carbon fibers are the SikaWrap(r)-301 C. The composite laminate was defined as the stacking of two unidirectional piles with two different fiber orientations (perpendicular). The sample is depicted in Figure 13 shown below. The defect was represented by the presence of air (the absence of an epoxy adhesive layer with a thickness of 1 mm) over an area of 100 mm \(\times\) 100 mm.  
The experimental setup of the tests with the CFRP samples is shown in Figure 14. The same microwave system as in the numerical model, with a frequency of 2.45 GHz, was used to heat the samples. The pyramidal horn antenna was placed at the same position and in the same direction as in the numerical model (200 mm from the sample and with a 45\({}^{\circ}\) direction). A heating power of 360 W was used for heating the samples for 150 s. In order to obtain the surface temperatures of the heated specimen for comparison with the numerical model, a medium wave range (3 to 5 \(\upmu\)m wavelength) infrared camera was used to record thermograms with a frame rate of 1 image per second. In order to detect the whole surface of the specimen, the following position of the infrared camera was chosen: 55\({}^{\circ}\) direction with a distance of 1.5 m from the sample. The thermograms were recorded for a duration of 475 s in order to observe the cooling phase of the experiments.  
Figure 14: Experimental test setup with the CFRP specimen and MIRT.  
Figure 13: Examined sample with a visible layer of CFRP at the concrete surface. Red rectangle indicates the defect’s position under the composite layer.
## 3 Creating Artificial Defects on MV Cable
### Internal Defect  
As for internal defect, a cylindrical cavity was modelled inside the cable insulation and we calculated the electric field profile (leg 1) by COMSOL. The defect is modelled such that a portion of the cavity is filled with semicon material, and the remaining portion contains air, as shown in Figure 5, with the purpose of giving rise to a sudden change in permittivity value from the semicon (\(\epsilon_{r}=1000\)) to air (\(\epsilon_{r}=1\)). This results in a significant intensification of the orthogonal electric field at the boundary of the air and semicon (Figure 6a), which can incept gas internal discharges at a relatively low voltage. In the present study, a cylindrical cavity (with height of 3 mm and diameter of 6 mm) was carved onto the cable insulation (thickness 4.8 mm). A 2/3 rd portion of the cavity was filled with semicon material. Matching the electric field distribution (Figure 6a) with the internal PD-inception model [21], the PDIV is estimated to be 4.0 kV. The size of the cavity and the filled portion of the semicon material inside the cavity were selected in order to design an internal defect that generates internal discharges at a voltage lower than that of the PD-inception voltage of the surface discharges that occur along the triple point (cable insulation, semicon tape, and air) and cable termination (as shown with red dotted line in Figure 5). By matching this simulated tangential electric field profile (Figure 6b) with the surface PD-inception field calculated from PD-inception model [21], the estimated surface PDIV is 5.3 kV. This confirms that the internal discharges across the cavity will occur at a relatively lower voltage than that of the surface discharges. Hence, the present configuration of the defect (as in Figure 5) is validated as a proper internal defect.  
Figure 4: (**a**) Cross-sectional view of MV cable with surface defect and (**b**) photo of the MV cable with manufactured surface defect. Path ABC represents the creepge line. A is the point of contact of cable conductor (HV electrode) with XLPE insulation through semiconducting paste, B is the edge of the cable insulation, and C is the triple point of the Al tape (ground electrode).  
Figure 5: Cross-sectional view of MV cable with internal defect (cylindrical cavity).  
For manufacturing the internal defect on a MV cable, semicon is peeled off, and a portion of XLPE insulation (15 mm \(\times\) 10 mm \(\times\) 1.3 mm) is cut from the cable insulation. After drilling the cavity, the XLPE portion is glued to the remaining part of insulation. Then, a semicon tape is wrapped around the cable above the internal defect, and it is connected to the ground, while the conductor of the cable is connected to HV.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 4 Results of the Positioning Analyses
### Analysis of Other Constellations: Navic and GAGAN  
The _multiGNSS_ software [4] has the capability to analyze the observations from NavIC and SBAS, in addition to GPS, GLONASS, Galileo, BeiDou and QZSS. Septentrio has kindly made available RINEX data of days 25-29 May 2017 from their station located in Brussels (not listed in Table 1). This gave the opportunity to analyze the NavIC time offsets for these five days. This station is equipped with a POLARX5 5.11 receiver, referred to as SEPT hereafter. It is worth noting that the observation  
Figure 10: Time series since 1 January 2014 of offsets of GLGP (**a**), GPGA (**b**), BDGP (**c**) and QZGP (**d**) differentiated relative to Septentrio.  
data of the NAVIC satellites are available for the L5 carrier only, for which NAVIC has been processed in single frequency.  
Figure 11a shows the results. The offset of NAVIC time scale relative to GPS is colored in purple. The NAVIC time offset is characterized by a daily variation which can reach 20 ns. Across the five days, NAGP seems to be quite constant, with a mean value of about \(-85\pm 9\) ns.  
Figure 11b shows the time offsets of SEPT differentiated to the other six Septentrio stations currently analyzed. GLGP and BDGP are smaller than those of other stations, whereas GPGA is very close. More in detail, the difference of GLGP varies suddenly from \(-16.5\) to \(-7.5\) ns on 26 May 2017, with a standard deviation of 3 ns. The GPGA of six Septentrio stations show spikes at 12:00 and 24:00 of each day, whereas SEPT does not. Otherwise the alignment is within 1 ns. The difference of BDGP is almost constant, with a mean value of about \(-12.5\pm 5\) ns.  
For SBAS data for satellites S27 and S28, both belonging to the Indian overlay system GAGAN in geostationary orbits have been used. These satellites are tracked by seven stations (DLF1, KIRU, REDU, TLSE, WTZ3, WTZZ, ZIM)). The GAGAN data were analyzed only from 25 September 2016, so as yet not enough results are available to perform a calibration of the GAGAN time offset like those described in the previous paragraph.  
Figure 12 shows the results obtained. Trimble stations give higher offsets, Javad stations give lower offsets and Septentrio stations give offsets comprised of those from the previous two receiver types. Septentrio GNGP seems to be quite constant in time, whereas for Trimble and Javad higher variations are observed.  
Figure 11: Time offsets of Septentrio station. (**a**) Time offsets of GLONASS, Galileo, BeiDou and NAVIC (purple) relative to GPS; (**b**) GLGP, GPGA and BDGP relative to the other Septentrio stations.  
Figure 12: Time series since 25 September 2016 of offset of the GAGAN time scale relative to GPS.
## 4 Experiments and Results  
### Experimental Data  
The datasets in this paper were selected from 12 high-dimensional datasets in the University of California, Irvine (UCI) repository [33], as shown in Table 1 of the high-dimensional datasets in the UCI repository, most of which are from challenging high-dimensional datasets. For example, the AsianReligionsData dataset in Table 1 is divided into eight categories and features a high-dimensional feature space (8265 dimensions), which makes the classification problem more challenging.