esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Autonomous Driving in Adverse Weather Conditions
### Autonomous Driving's Dataset Analysis for Adverse Weather Challenges
#### 2.5.3 Insights from the Comparative Analysis  
The analysis of the performance data reveals several key insights, each offering guidance for selecting the appropriate model and dataset combination for specific use cases:  
YOLOv8 demonstrates high efficiency, achieving an AP of 56% at 60 million parameters. It excels in real-time object detection due to its streamlined architecture, making it suitable for use cases where processing speed is critical. However, its performance tends to plateau with increased complexity, suggesting diminishing returns at higher parameter counts [57].  
Self-supervised learning methods show a strong ability to adapt to new environments, with an AP of 63% at 60 million parameters. These models benefit from their ability to learn from large volumes of unlabeled data, making them highly effective in scenarios with  
Figure 3: Performance comparison of AI models on object detection.  
diverse and dynamic weather conditions [58]. This adaptability makes self-supervised learning a promising approach for environments where labeled data are scarce or expensive to obtain.  
Sensor fusion techniques, which integrate data from LiDAR systems, cameras, and radar systems, reach an AP of 59%. The combination of multiple sensors allows these models to maintain high depth perception and spatial awareness in low-visibility conditions like dense fog or heavy rain [55; 59]. However, the increased complexity and computational demands of sensor fusion models can limit their applicability in real-time scenarios.  
Vision Transformers provide a balance between accuracy and computational efficiency, achieving up to 60% AP. Their ability to capture long-range dependencies in image data allows them to perform well in complex urban scenarios where context is crucial [60]. This makes Vision Transformers suitable for applications that require a detailed understanding of the environment but can afford higher computational costs.  
GAN-based models, while not directly competitive in AP, play a crucial role in data augmentation. By generating synthetic training data, these models enable other algorithms to improve their robustness against rare weather events, such as blizzards or sandstorms. Their ability to simulate extreme conditions makes them valuable for training models that must operate in environments where real-world data are difficult to obtain.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 2 Materials and Methods
### Finite Element Model of the AMBs  
A nonlinear model of the AMBs can be set up in ANSYS based on the 2-D finite element strategy, using ANSYS Maxwell (V.2022 R2) [24]. The software enables the execution of many steps, including geometry definition, physics application, meshing, equation solving, and result visualization [25]. Using [PERSON]'s formulas for a steady magnetic field:  
\[\
abla\times\mathrm{H}=\mathrm{J} \tag{8}\]  
Here, \(\mathrm{J}\) represents the steady current density (\(\mathrm{A}/\mathrm{m}^{2}\)).  
The material equation may be expressed using the reluctivity v:  
\[\mathrm{H}=\mathrm{vB} \tag{9}\]  
The variable v represents the reciprocal of the material-dependent (\(1/\upmu\)).  
The magnetic vector potential A (\(\mathrm{Wb}/\mathrm{m}\)) is a measure of the magnetic flux density as:  
\[\mathrm{B}=\
abla\times\mathrm{A} \tag{10}\]  
The basic equation of the vector potential formulation for the magnetic field can be obtained by substituting Equations (9) and (10) into Equation (8).  
\[\mathrm{J}=\
abla\times\mathrm{(v}\
abla\times\mathrm{A}) \tag{11}\]  
The structural design of AMB includes a rotor and stator, both equipped with coils. The chosen material for the rotor and stator components is JFE_Steel_50 JN1000, whereas copper is selected as the material for the coils. Figure 1 illustrates the heterpolar architecture of the AMB, which has eight poles spaced 45 degrees away from one another. Table 1 lists the AMB's parameters. The parameters mentioned are used to design AMB by implementing the finite element technique, explicitly using the ANSYS software. Figure 3 illustrates the mesh configuration of AMB with eight poles. Figure 4 shows the magnetic flux density of an eight-pole Active Magnetic Bearing (AMB).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Height of the stack & 71.42 mm \\ Rotor diameter & 50 mm \\ Operating air gap & 0.5 mm (radial) \\ The outer diameter of the AMB & 140 mm \\ The inner diameter of the AMB & 120 mm \\ Number of turns & 80 \\ \hline \end{tabular}
\end{table}
Table 1: Different parameters used.  
Figure 3: Mesh of eight pole AMB.
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
## 6 Conclusions  
This paper proposed an efficient three-stage system for avoiding the BER floor formation in most of the traditional two-stage systems. The design and performance curves for the three-stage wireless video communication system are provided. For efficient video transmission, we utilized the latest H.265 video codec. H.265 is widely adopted for achieving compression efficiency twice as much that of its precursor H.264 standard. The main constituents of the system were an SBC encoder, a URC precoder, and the modulation technique of SP. The URC assists in uniform distribution of information across the decoders. In addition, we investigated the performance of ISCD system with the combinational gain MIMO technique of LSSTC. More specifically, the system profited from the STC, V-BLAST and beamforming techniques. The system incorporated AAs for introducing transmit diversity gain. The BER and PSNR performances recorded significant improvements with the increase in \(d_{H,\text{min}}\) value. EXIT analysis verified BER improvement by the prevention of BER floor formation with the aid of optimized SBC for the three-stage system. Conclusively, an \(E_{b}/N_{0}\) gain of about 22 dB is observed for the advocated system utilizing optimized SBC with \(d_{H,\text{min}}\) of 3 relative to the benchmarker with \(d_{H,\text{min}}\) of 1 at the PSNR degradation point of 41 dB. In future, we aim to incorporate deep learning-based CNN in the iterative decoding process to estimate the statistical behavior of the correlated Rayleigh fading model. Moreover, we aim to investigate the performance of the proposed three-stage system with the H.266 standard for ultra-high resolution (4 K to 16 K) video streaming.  
Conceptualization, N.M. and I.A.; methodology, N.M.; software, N.M.; validation, I.A.; formal analysis, I.A.; investigation, N.M.; resources, I.A.; data curation, J.F.; writing--original draft preparation, I.A.; visualization, K.S.K.; supervision, N.M.; project administration, N.M.; funding acquisition, N.M. and J.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovak Grant Agency for Science (VEGA) under grant no. 1/0157/21.  
Not applicable.  
Work accomplished under the research project NCBC-UETP, in the field of Multimedia Streaming and Analytics.  
The authors declare no conflict of interest.
## 5 Performance Evaluation
### Simulation
#### 5.2.3 Population Churn  
Now, we allow the population churn. This is necessary for more realistic scenario because vehicles come into and go out of the communication range even in stable driving environments. It is even more dynamic when roads merge or in the intersection, etc.. To simply the experiment setting, we let new vehicles come in to the communicating group of vehicles at a churn rate \(\lambda\) (%/s) and some in the group exit at the same rate. For example, the churn rate of \(\lambda=0.01\) at \(CBR=20\%\) corresponds to 5 vehicles newly joining/leaving from the entire population of 500 vehicles every second. It simulates a very stable group of vehicles in terms of churn. On the other hand, \(\lambda=0.2\) at \(CBR=90\%\) corresponds to 450 vehicles changing per second, which is a very high dynamics scenario. We assume that the newly joining UEs randomly choose their initial resource locations, without any chance for sensing. Please note that it is a rather harsh assumption because as vehicles join, they will be able to perform sensing at least for a short duration. However, to stress the two schemes, we assume so.  
Figure 11 shows the results. We observe that with the churn rate increasing, the collision probability increases in both schemes. This is in line with the observation made by [PERSON] et al. (2016). It is expected because under our harsh assumption above, the initial collisions are not preventable. We also observe that SPS/LA is more heavily affected by the harsh assumption. It reflects the fact that the SPS/LA scheme effectively controls the other type of collisions, namely those arising from the lack of coordination among vehicles as to the resource reselection in SPS. In contrast, the SPS performance only slightly degraded. It implies that for the SPS without the lookahead, most of the collisions are of similar blind nature to the newly joining vehicles in this experiment. Overall, the SPS/LA performs better than SPS, even with very high churn rates.  
Figure 11: Collision probabilities under population churn. (**a**) SPS. (**b**) SPS/LA.
## 3 Computer-Aided Analytical Modeling of Class-E Series-Tuned RFPAs
### Maximum Value of \(Q_{s}\)

Table 2: \(Q_{min}\) and \(Q_{sat}\) for different topologies. Figure 7: Time-domain normalized voltage (in blue, its corresponding axis is marked by the arrow) and current (in red) waveforms from ADS simulation results for (**a**) Topology 1, (**b**) Topology 2.1, and (**c**) Topology 2.2.  
optimizing the resonator's performance, particularly in the context of optimal Class-E series-tuned RFPAs. A novel finding from this analysis is the relationship between the quality factor of the resonator and its impact on the amplifiers' overall performance, thereby defining the bounds on \(Q_{\text{s}}\), which, to the authors' knowledge, has not been reported elsewhere. For optimal operation, \(Q_{\text{s}}\) must be carefully chosen within specific bounds, given by Equation (37), as follows:  
\[Q_{\text{min}}<Q_{\text{s}}\leq Q_{\text{sat}} \tag{37}\]  
where  
\(Q_{\text{s}}\): Quality factor of the resonator. \(Q_{\text{sat}}\): Saturation quality factor, defining the upper-performance limits. \(Q_{\text{min}}\): Minimum quality factor defining lower performance limits. Increasing \(Q_{\text{s}}\) beyond \(Q_{\text{sat}}\) only degrades the efficiency-bandwidth response, making no significant improvement in the amplifier's performance. This inequality obtained through HB simulations establishes the fundamental bounds for \(Q_{\text{s}}\), which can be applicable across various tuned topologies, guiding the design process to achieve the desired balance between efficiency, bandwidth, and overall performance. It is important to note that the numerical values presented in this analysis, as reported in Table 2, are based on the assumption of normalized input parameters, as depicted in the simulation setup in Figure 3. Any variation in these normalized values will result in corresponding changes in the limits. Nevertheless, the techniques described in Sections 3.2 and 3.3 allow for the straightforward determination of these limits even under varying conditions.
## 3 Study Area and Data
### Data Collection  
#### 3.1.1 Mobile GPS Data  
These data are derived from the open data provided by Mobike company in the Mobike Cup Algorithm Challenge Match. The initial data cover the whole-day (0:00-24:00) GPS data of Mobikes in Beijing from May 10, 2017 to May 19, 2017, amounting to 2,466,817 data records (Table 1). After desensitization, each datum included seven numeric fields: order id, user id, bike id, bike type, start time, origin Geohash encoding, and destination Geohash encoding. The origin-destination location information is denoted as a 7-bit encoding in the Geohash system, a geocoding system incorporated into the public domain that can be decoded as the latitude and longitude in reverse. The deviation can be \(\pm\)76 m, which translates into a rectangular region with a side length of approximately 150 m; this is treated as traffic zones for bike sharing in this research study.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology
### Rationale for Metrics  
Execution time and memory usage were chosen as the main metrics due to their direct impact on the scalability and efficiency of distributed systems. These metrics were chosen for the following reasons:  
* They serve as industry-standard indicators for evaluating computational performance.
* They help to identify bottlenecks in resource allocation and operator configuration.  
Thus, the metrics proved essential in building predictive models for Apache Spark configuration optimization as well as overall system performance.
## 4 Changing the Cultivation Conditions for Optimizing the Production of Bioactive Compounds
### Pigments
#### 4.1.2.2 Light Intensity  
Microalgae and cyanobacteria are confronted with variations of light by movements in the water column and emersion for coastal benthic species. For these reasons, more than terrestrial plants, marine microorganisms acclimate to light level and light quality by optimizing pigment content and composition [208,233-235]. Therefore they display a wide diversity of light harvesting pigments (Table 1) allowing optimal photosynthesis at different depths in seawater.  
Autotroph microorganisms sometimes experience high levels of UV-radiation, in shallow areas and during low tides when they are deposited on intertidal flats. To cope with high sunlight intensities, microalgae have developed different photoprotective mechanisms. One of these, the xanthophyll cycles (Figure 1), consists in the reversible conversion of violaxanthin, anthearaxanthin and zeaxanthin in green algae and in the reversible conversion diadinoxanthin and diatoxanthin in brown algae [115,234,236]. Several authors have shown that UV exposure increases carotenoid content in the following hours. It is true for microalgae [237-239] and cyanobacteria [98,240]. In cyanobacteria, carotenoid glycosides play an important role in photoprotection [97]). In cyanobacteria submitted to high light and nitrogen deprivation, the carotenoid content can increase [241]. A high light stress alone leads to an increase of myxoxanthophyll and aphanizophyll, and to a decrease of \(\beta\)-carotene content [97-99]. The increase of myxoxanthophyll amount is in agreement with a coordinated activity of the enzymes of the myxoxanthophyll biosynthetic pathway, with no rate-limiting intermediate steps [99].  
Acclimation of microalgae or cyanobacteria to low irradiance intensity or blue enriched light increases the synthesis of specific carotenoids (e.g., fuco in phaeophyceaeae [196,208,241]). Generally, the photoprotection or the low photoacclimation leads carbon to carotenoid synthesis whereas in nonstressfull conditions, carbon serves mainly to growth (cell wall edification). In the marine diatom _Haslea ostrearia_, carbon fixation by \(\beta\)-carboxylation is almost equal to that in the C3 pathway whereas under low irradiation C3 fixation dominates [242].
# Influence of a Helium-Nitrogen RF Plasma Jet on Onion Seed Germination
## 2 Materials and Methods  
### Cold Plasma Treatment  
Tested material were Wolska seeds (_Allium cepa_ L.) purchased at Przedsiebiorstwo Nasiennictwa Ogrodniczego i Szkolkarstwa PNOS, Ozarow Mazowiecki (Poland). They were exposed to afterglow plasma gas. Plasma was generated in the radio frequency jet reactor (Figure 1). The plasma jet was excited by an AG 1021 RF generator (T&C Power Conversion, Rochester, NY, USA) with integrated amplifier and meter of forward and reflected power. In order to increase the voltage between electrodes (sinusoidal signal of 890 V RMS), an air transformer (1:12 winding ratio) was used with parallel connected capacitor for impedance matching. Load power was set to 45 W (forward power 47 W) with frequency of 14.16 MHz. Plasma was generated in the discharge gap between an external tubular-shaped electrode made of stainless steel (14 mm diameter) and internal tungsten rod-type electrode (5 mm diameter) [13]. Substrate gas was a mixture of He + N\({}_{2}\) (ratio 3:2) at total flow rate of 710 L/h. For the gas dosing, glass tube variable area flow meters (Zaklady Automatyki \"ROTAMETR\", Gliwice, Poland) were used. One layer of seeds was placed on the movable strainer located in the glass container (60 mm in diameter and 60 mm length). The distance between the nozzle's outlet and surface of the sample was 20 mm (for treatment times up to 15 s) or 50 mm. The energy density inside the RF plasma reactor was quite high, ranging to 230 kJ/m\({}^{3}\). It is difficult to calculate directly the power transferred to the seeds as they were placed at selected distance from the nozzle, in the afterglow area. Direct gross power calculation result was around 7 W forwarded power per 1 g of seeds.
## 4 Results and Discussion
### Estimation of the Measurement Uncertainty  
This section describes the error measurement approach that was adopted for each type of selected method. The maximum error method was used to determine the measurement uncertainties. This method determines the maximum error of an indirect measurement from all measured variables. To determine this, the function is derived after each variable with a measurement uncertainty. The values of the derivatives are then added together.
## 3 Results and Analysis  
### Dynamic Trajectories at Different GVOs  
The runaway processes after load rejections with servo motor failure at four different GVOs of a model pump-turbine were simulated. Figure 3 shows the dynamic trajectories and the static characteristic curves. The speed factor \(n_{\mathrm{ED}}\), discharge factor \(Q_{\mathrm{ED}}\), and torque factor \(T_{\mathrm{ED}}\) are defined as follows:  
\[n_{\mathrm{ED}}=nD_{1}/(60\sqrt{gH}) \tag{3}\]  
\[Q_{\mathrm{ED}}=Q/({D_{1}}^{2}\sqrt{gH}) \tag{4}\]  
\[T_{\mathrm{ED}}=T/(\rho{D_{1}}^{3}gH) \tag{5}\]  
where \(n\), \(Q\), \(T\), \(\rho\), \(g\), and \(H\) represent the rotating speed, flow rate, torque, water density, gravitational acceleration, and water head, respectively. Here, the water head was defined as the pressure between the spiral case inlet and draft tube inlet.  
As can be seen in Figure 3, during the runaway processes, the dynamic trajectories of operating point form loops in the S-shaped region. At the primary stage, the dynamic trajectories of operating parameters are nearly consistent with the measured static characteristic curves, which indicates that the scheme of numerical simulation has certain reliability. The variation tendencies of four loops are different. The pump-turbine experiences a damped oscillation for GVO at \(6^{\circ}\), while it undergoes a self-excited oscillation for GVO at \(9^{\circ}\), \(15^{\circ}\), and \(24^{\circ}\). [PERSON] [26; 27] has proved that the runaway instability of this simple rigid hydraulic system depends on the slopes of torque factor \(dT_{\mathrm{ED}}/dn_{\mathrm{ED}}\) at no-load point. For GVO at \(6^{\circ}\), the slopes \(dT_{\mathrm{ED}}/dn_{\mathrm{ED}}\) of dynamic trajectories are negative at the no-load point, while for GVO at larger values (\(9^{\circ}\), \(15^{\circ}\), and \(24^{\circ}\)), the slopes \(dT_{\mathrm{ED}}/dn_{\mathrm{ED}}\) are positive at the no-load points, which satisfy and dissatisfy the runaway stability criterion, respectively.  
In fact, the slope of the dynamic trajectories near the no-load point relates to the energy transfer between the hydraulic energy and the mechanical energy. If the exciting energy transferred from the unstable flows to the runner is larger than the dissipated energy, the oscillation will be un-damped. Therefore, the difference in slopes on dynamic trajectories may be attributed to the differences of flow evolutions within the pump-turbine during runaway processes between \(6^{\circ}\) GVO and other larger GVOs.  
Figure 3: Dynamic trajectories and static characteristic curves. (**a**) \(n_{\mathrm{ED}}\)–\(Q_{\mathrm{ED}}\); (**b**) \(n_{\mathrm{ED}}\)–\(T_{\mathrm{ED}}\).
## 4 The Compressive Acquisition Model for Gas Data Flow
### Compressive Acquisition Model of Gas Data Flow  
In the current study, a mobile gas detection instrument was mounted on a miner's lamp, in which the GS1011 low-power consumption Wi-Fi chip was the hardware core. Since the miner's lamp took multiple functions (sensing, lighting, and energy communication), low energy consumption of the mobile gas detection instrument should be an important constraint. Considering the lamp's limited energy, the multi-hop communication mode was preferable when several mobile gas detection instruments moved between two wireless access points.  
Accordingly, the signals on \(M\) time-independent multi-hop communication links with different initial nodes should be collected. As illustrated in Figure 5, by assuming that \(y_{i}=\sum\Phi_{i,j}x_{j}\) denotes the \(i\)-th multi-hop signal received by a wireless access point (\(i=1,2\cdots M\)), in which \(\Phi_{i,j}\) denotes the random coefficient generated at the \(j\)-th node in the \(i\)-th multi-hop communication link and \(x_{j}\) denotes the gas data measured at the \(j\)-th node, the receiving vector of \(M\) multi-hop communication links was denoted as \(\mathbf{y}=[y_{1},\cdots,y_{M}]\), i.e., \(\mathbf{y}=\mathbf{\Phi\
abla s}\).  
Figure 4: Multi-hop wireless networks.  
Selection of measurement matrix is a critical step in the practical application of compressive sensing theory. The related theory has proved that, when \(M\geq CK\log(N/M)\), all \(K\)-sparse vectors can be stably recovered from some random measurement matrices such as Gaussian matrix, Bernoulli matrix or Fourier random matrix. However, if a random matrix was generated in each measurement and recorded for finally reconstruction, the system cost would be increased in practical applications, which is not beneficial for physical implementation. Therefore, some mathematicians proposed to use deterministic measurement matrix and structured measurement matrix to replace the random matrix. Some theoretical and experimental results also proved that structured measurement matrix is an effective choice. In the proposed compressive acquisition model, the communication links are generated according to the adopted multi-hop route which is determined, and the coefficient generated at the \(j\)-th node in the \(i\)-th multi-hop communication link is random, so the \(\mathbf{\Phi}\) is pseudorandom. It satisfies the RIP properties [26].