esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Effect of Hartmann Number and Round Corner Radius on Nanofluid Thermal Behavior  
The simulations were carried out under the following data:  
* The corner radius was \(R=0.2\); 0.4; 0.6; 0.7
* The solid volume fraction was \(\phi=0.05\)
* The Prandtl number was \(Pr=0.7\)
* The Rayleigh number was \(Ra=5\times 10^{4}\)
* The Hartmann number was \(Ha=0;20;40\)
* The Eckert number was \(Ec=10^{-5}\)  
Figures 7 and 8 show the effect of Hartmann number and the effect of round corner radius on the isotherm contours and the surface temperature distribution, respectively. The Hartmann number varies, from left to right (columns), as \(Ha=0\); 20; 40, while the round corner radius varies, from up to down (lines), as \(R=0.2\); 0.4; 0.6; 0.7.  
Figure 6: Effect of Hartmann number and Rayleigh number on the surface velocity magnitude distribution.  
Results show that the parallelism of isotherms to hot and cold walls was more pronounced at higher corner radius and degenerates as the corner radius decreased from \(R=0.7\) to \(R=0.2\), especially in the central zone of the nanofluid filled cavity, due to the enhancement of buoyancy. Thus, the heat transfer was dominated by convection instead of conduction when the corner radius decreased. This was explained by the fact that the decrease of corner radius permit to give much more space of nanofluid, which favors considerably the convective heat transfer. Results show, also, that the parallelism of isotherms to hot and cold walls was enhanced when the Hartman number increased from \(Ha=0\) to \(Ha=50\), especially in the central zone of the nanofluid filled cavity. This was explained as the same in Section 5.1. It is to note, also, that the temperature homogeneity of the nanofluid was globally improved as the Hartmann number and the round corner radius decrease.  
Figure 7: Effect of Hartmann number and round corner radius on the isothermal contours.
## 4 Numerical Simulation Analysis of Tunnel Stability
### Results of Simulation and Analysis  
#### 4.2.1 Displacement of Surrounding Rock  
(1) Vertical displacement. The nephogram for surrounding rock and the curve for vertical displacement of each key point are shown in Figures 10 and 11.  
From Figure 10, it can be found that with the advancement of each excavation step, the vertical displacement of rock gradually increases and finally tends to be stable, whereas the maximum value of vertical displacement occurs at the vault and bottom, showing that the vault subsides and the bottom rises. After the fifth excavation step, the subsidence value for the vault is 32.06 mm, while the uplift value of the bottom reaches 30.40 mm, accounting for 65.8% and 74.2% of final deformation, respectively. As it can be found in Figure 11, the vertical displacement of each key point changes greatly before the seventh excavation step, and the displacement growth rate is obvious, which indicates that the working face excavation proves to be of significant influence on the stability of soft-rock tunnels. From the eighth excavation step, the vertical displacement of each point increases slowly, and after the nineteenth excavation step, the displacement of each point tends to stabilize. Vertical displacement convergence values of the vault, spandrel, and hance are 48.71 mm, 39.83 mm, and 19.49 mm, respectively. At the bottom and arch foot, they are 40.47 mm and 10.37 mm, respectively, and floor heave appears.  
Figure 11: Variation curves of vertical displacement of the key points.  
Figure 10: Vertical displacement nephogram of surrounding rock of the tunnel: (**a**) the 5 th excavation step and (**b**) the 24 th excavation step.  
(2) Horizontal displacement. The nephogram of surrounding rock and the curve for each key point are presented in Figures 12 and 13.  
As can be seen from Figures 12 and 13, the horizontal displacements at the leftmost and rightmost spandrels, hances, and arch feet are symmetrically distributed with the tunnel centerline, whereas the horizontal displacements at the leftmost and rightmost hances are the largest. With continuous increase in excavation steps, the horizontal displacement of surrounding rock increases gradually, and tends to be stable at the end. Before the ninth excavation step, the horizontal displacement of each key point changes greatly, and the displacement increases obviously, which is caused by the close distance from the working face to the monitoring section and the great rock disturbance during excavation. In the fifth excavation step, horizontal displacements of the leftmost and rightmost hances are 5.04 mm and 4.91 mm, respectively, accounting for 45.08% of the convergence value. After the 10 th excavation step, the horizontal displacements at each point increased slowly, and once reaching the 20 th step, the horizontal displacements at each point tend to stabilize. The convergence values of the horizontal displacements at the hance, arch foot, and spandrel are 22.07 mm, 5.66 mm, and 2.41 mm, respectively.
## 2 Materials and Methods
### MMS POS/LV and GNSS Receivers Used in the Study
To further differentiate the measurement conditions and the behaviour of the different receivers under these conditions, the overall trajectory was divided into different cases: tunnel (Case A), city (Case B) with tall buildings representing a typical urban canyon area, a serpentine road (Case C), and a GNSS vegetation-barrier area in the Karst plateau above Trieste (Case D). The results were compared separately for each of these four areas. To obtain a better idea of the measurement conditions, some characteristic points from the trajectory of the vehicle are shown in Figure 5.  
Figure 4: The surveyed area, the city of Trieste and nearby region, Italy. Figure 5: (**a**) Case A: the entrance into the tunnel (45.642059\({}^{\circ}\)N, 13.7744820\({}^{\circ}\)E); (**b**) Case B: urban canyons in the city centre (45.655748\({}^{\circ}\)N, 13.771247\({}^{\circ}\)E); (**c**) Case C: serpentines (45.657905\({}^{\circ}\)N, 13.813880\({}^{\circ}\)E); (**d**) Case D: straight road with vegetation as only barrier (45.690686\({}^{\circ}\)N, 13.769906\({}^{\circ}\)E).
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 2 System Design and Detection Principle
### Theory of Detection Technology  
When an insulation fault occurs inside the switchgear, the high frequency electromagnetic wave signal will be diffracted to the outside, and the signal from 300 MHz to 1500 MHz can be coupled with the UHF sensor. If the original signal is directly sampled for the obtained UHF signal, the sampling rate of the data collector needs to reach at least GHz [14], which undoubtedly adds a huge installation cost, and the amount of data received is huge. On the other hand, considering the actual situation, it is difficult to completely collect the full-band signal and it is not suitable for online monitoring systems. To collect a useful signal without distortion for ultra-high frequency detection, a signal conditioning and collection system needs to be designed.  
The UHF signal generated by partial discharge in the switchgear will be refracted and reflected many times inside the switchgear, so the UHF signal is usually an exponentially attenuated oscillating signal [15], which can generally be expressed by the double-exponential attenuation oscillation model in Equation (1): where \(A\) denotes the signal amplitude, \(\tau\) denotes the attenuation coefficient of the signal, and \(f_{c}\) is the oscillation frequency. Figure 2a  
Figure 1: Schematic diagram of the monitoring system.  
is the measured UHF signal of partial discharge, and Figure 2b is the simulated signal of double-exponential attenuation oscillation.  
\[f(t)=A(e^{-1.3t/\tau}-e^{-2.2t/\tau})\sin(2\pi f_{c}t) \tag{1}\]  
As can be seen in Figure 2, the UHF signal generated when partial discharge occurs inside the switchgear cabinet is relatively similar to the AM signal, so the signal conditioning method of the UHF signal is used more often in the envelope detection method [15; 16]. The envelope detection method is used to extract the envelope information of the UHF signal, remove the carrier wave, and then extract the discharge information related to partial discharge from the envelope signal. This way the frequency of the signal can be greatly reduced while retaining the original signal characteristic parameters so that it is easy to realize analog-to-digital conversion by the data collector. The detector waveform signal retains the approximate amplitude, phase, and number of partial discharge signals, which can be used to map the PRPD discharge spectrum so as to carry out partial discharge type identification and exclude interfering signals. Therefore, the design of signal conditioning circuits to realize effective wave detection is a research focus of this paper.
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 3 Numerical Investigations of the Methodology
### Noise Immunity Evaluations
#### 3.2.3 Compared with Other Methods  
To demonstrate the advantages of the proposed method, we compare the performance of the method to two alternative damage detection methods, namely the fractal dimension-based method [20] and the deep learning-based method [10]. The beam specimen used for comparison is the same as mentioned above, based on the 2 nd mode shape in the noisy environment with the SNR of 60 dB. The detection results of the three methods are shown in Figures 11 and 12. Although the other two methods can detect damage, some drawbacks still exist. The fractal dimension-based method is heavily disturbed by noise. While local mutations in the fractal dimension can offer a means to detect damage with a high degree of sensitivity, this property is susceptible to interference from noise, resulting in the misidentification of noise as damage features. The deep learning-based method is effective in overcoming noise interference, but the end-to-end regression prediction task makes its results still fluctuate to some degree in non-damaged regions, which complicates the determination of the threshold of damage. Moreover, the implementation of the deep learning-based method necessitates the generation of extensive datasets and the training of complex models, which imposes an additional burden on practical applications. In contrast, the proposed method has the capacity to detect the damage directly and accurately without the aforementioned drawbacks.  
Monte Carlo experiments are performed to statistically compare the detection accuracy of the methods. Considering the trade-off between simulation accuracy and computational resource consumption, a convergence analysis has been performed and the results tend to stabilize after the sample size reached 2000, suggesting that this sample size is sufficient for this specific case. Therefore, the Monte Carlo sample size is set to 2000, adhering to Chebyshev's law. A broad range of SNRs (50 dB-100 dB) is considered with the damage degree maintained at a constant level (\(\alpha=0.2\)). Two damage cases are studied: single damage and multiple damages (with 2 to 4 damages at random). The results are presented in Figure 13, which demonstrate that the proposed method has better noise immunity compared to the other two methods, especially in the cases with multiple damages.  
Figure 11: Compared with other methods on single damage with SNR = 60 dB: (**a**) the fractal dimension-based method, (**b**) the deep learning-based method, (**c**) the proposed method.  
Figure 12: Compared with other methods on double damages with SNR = 60 dB: (**a**) the fractal dimension-based method, (**b**) the deep learning-based method, (**c**) the proposed method.
## 3 Results and Discussion
### Contour Plots (2D) and Response Surface Plots (3D)
#### 3.4.2 COD Removal  
The increase in wastewater pH up to 9 across all the tried PSE doses contributed to COD removal. As shown in Figure 6a,b, the highest COD reduction was observed at pH 10 with coagulant dosages exceeding 10 mL/L. The effectiveness of PSE in COD removal is attributed to its ability to degrade organic matter, facilitated by its bioactive compounds. The high protein content in _Pumpkin seeds_ plays a crucial role in this process, as proteins bind to organic compounds, enhancing their biodegradation by microorganisms, which utilize them as carbon sources through hydrolytic enzymes [45]. However, this activity is highly dependent on pH conditions. Conversely, as shown in Figure 6c,d, the interaction between FeCl3 and pH is stronger than that of the PSE coagulant. The data indicate that  
Figure 5: Contour plots and their corresponding surface plots for the turbidity of wastewater as a function of (**a,b**) pH and PSE dosage at constant FeCl3 dosage; (**c,d**) pH and FeCl3 dosage at constant _Pumpkin_ dosage; (**e,f**) PSE dosage and FeCl3 dosage at constant pH.  
the residual COD concentration remained below 150 mg/L. It is important to note that the reduction in COD with FeCl3 is primarily driven by a chelation process, where Fe ions bind to organic matter, promoting coagulation. However, excessive FeCl3 concentrations negatively impact COD removal by reducing the affinity of organic compounds to adsorb onto Fe ions, thereby diminishing treatment efficiency [45].  
Figure 6: Contour plots and their corresponding surface plots for COD of wastewater as a function of (**a**,**b**) pH and PSE dosage at constant FeCl3 dosage; (**c**,**d**) pH and FeCl3 dosage at constant PSE dosage; (**e**,**f**) PSE dosage and FeCl3 dosage at constant pH.  
The impact of the interaction between chemical and natural coagulants is less significant, as indicated in Figure 6e,f, with the maximum reduction in COD achieved within the range of 200 mg/L to 250 mg/L. The limited interaction between chemical and natural coagulants may be due to competition for binding sites or differences in their optimal working conditions. Further investigations are needed to explore potential synergistic effects and to optimize their combined application for enhanced wastewater treatment efficiency.
## 1 Introduction
In [18], a DFE with error detection capability was employed to equalize signals in 100 Gb/s coherent 16 QAM systems, but this equalizer was sensitive to phase impairments, necessitating additional carrier phase recovery operations for achieving lower error rates. To reduce equalization complexity, a carrier phase recovery-free constant modulus algorithm (CMA)-DFE is proposed in [19] for compensating nonlinear impairments in coherent QPSK systems, achieving a minimum bit error rate (BER) of \(6.8\times 10^{-4}\). However, the CMA is only suitable for signals with arbitrary symmetric two-dimensional signal constellations, such as QPSK and M-PSK, and is unsuitable for PAM4 signals. Therefore, there is a need to develop an equalizer for coherent optical PAM4 systems with low complexity and excellent equalization performance. In this paper, we focus on an access network based on PON, and propose two modified DPEs for coherent optical PAM4 systems, i.e., magnitude-assisted decision feedback equalizers (MA-DPEs) and phase-assisted decision feedback equalizers (PA-DFEs). They can compensate for CD and nonlinear impairments, while being completely robust to carrier phase impairments. Among them, MA-DFEs construct the error function considering the signal amplitude information, whereas PA-DFEs incorporate phase into the error function. We experimentally demonstrate a single-wavelength 80 Gb/s coherent optical PAM4 system in the C-band and conduct comparative analysis of the equalizer's performance parameters. The experimental results indicate that without CD pre-compensation or carrier recovery operations, using only the PA-DFE and MA-DFE effectively compensates for CD and nonlinear impairments over 25 km SSMF. The BER meets the soft decision threshold of \(1\times 10^{-2}\) at a received optical power of \(-23\) dBm and the link budget reaches 27 dB. The experiment also demonstrates different operating states of the SOA at the transmitter. The results show that, when dealing with severe signal impairments caused by SOA gain saturation, the equalization performance of PA-DFE is superior to that of MA-DFE, ultimately achieving a link budget of 29 dB.
## 3 Results
### Interactions between SPEI and Ecosystem Function Indicators  
#### 3.3.1 SPEI and LAI  
The impulse response analysis of SPEI and LAI was shown in Figure 7a, where the \(y\)-axis indicated the standard deviation of SPEI-induced LAI at a lag of n months. After an positive impact of SPEI on LAI, the response trend of deciduous broadleaf forest was similar to the mean curve. Namely, after three months of negative response, LAI started to respond positively and reached its peak in the sixth month. In terms of the cumulative effect, pixels of positive value accounted for more than 80% of the total. LAI of forestland were less affected by the changes of moisture conditions. On the contrary, the response curves of deciduous broadleaf forest, grassland, and cropland were similar in the direction of LAI to SPEI, with all positive responses within 12 months and the greatest response value in the 7 th month, while the mixed forest always showed no significant change (Figure 7b). The pixels of positive cumulative impulse response coefficients occupied about 84.35% of the total (Figure 8a,b). There are more pixels of negative values (15.66%) in the direction of LAI to SPEI. We could infer that when the climate in the study area became wetter, vegetation cover would increase in most areas. Such changes in moisture condition might cause negative effects on grassland and cropland for a certain period. However, the cumulative effect was still dominated by the positive side. In contrast, the better vegetation growth would have the improved effect on regional moisture conditions, thus the drought would alleviate.  
Figure 7: Impulse responses showing the trend with time of SPEI and LAI (**a,b**), ET (**c,d**), and GPP (**e,f**) of different vegetation types after giving shocks to each other. The \(y\)-axis represents standard deviation (i.e., the standard deviation that a variable causing another variable at a lag of n months).
# Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
## 6 Limitations  
The evaluations conducted in this study through direct experiments for the developed models have shown promising results for enabling energy awareness in order to manage the heterogeneous resources of cloud computing. However, this study has a few limitations. These limitations are as follows:  
* Each VM in cloud computing consists of different virtual resources, such as CPUs, memory, and network traffic. These resources affect the total VM energy consumption. Although the workload has been executed by the GPU, the developed energy consumption models in this study have only considered the energy consumed by the GPU and neglected other resources that affect the total VM energy consumption.
* Cloud computing is known for the diversity and heterogeneity of its resources, and the cloud-computing infrastructure hosts a great number of VMs and PMs. However, this work has only considered two VMs and two types of NVIDIA GPU architectures in cloud computing. This is because of the high cost of performing experiments on a real cloud testbed that contains a large number of heterogeneous GPUs. Moreover, simulation tools that support heterogeneous GPU architectures in cloud-computing environments are not available.
# Evaluating Cybersecurity Risks of Bulgaria's Energy Sector: Focus on PV and HVAC-R
## 3 The Bulgarian Energy System--Current State, Issues, Trends, and Prospects
### Price Effect  
The inherent peak of photovoltaic generation and the saturation of the energy system with PV plants inevitably affect electricity prices. The time variation in the electricity exchange prices (orange) in Figure 6 is shown for summer and winter. Prices are based on the energy exchange and are averaged for the respective month during 2023. For comparison, the generated power from 1 kWp (blue) is also shown, using the averaged data from the PVGIS. The correlation between the daily peak in generation and the daily minimum price is obvious and well-studied [76]. The summer abundance of PV-generated energy is the reason for the very low daily price levels. The decrease in energy prices during periods of abundant sunshine and low consumption is very significant, and negative prices are no longer a surprise to market observers.  
Furthermore, in just one week in April 2025, there were three days with negative electricity prices, as shown in Figure 7 (data from IBEX). It is striking how low the negative price is. Given that in April stationary PVs without storage generate power in the interval 8:00-19:00 h, it can be seen that for the three days presented in the figure, the PV plants do not generate a real profit, with the worst day, 20 April, resulting in a net loss. Furthermore, the negative energy prices are also subject to transfer costs over the grid, meaning that a nominally positive low price is already negative on the balance sheet, when the energy price is lower than the transmission price. The data, presented here, are only to illustrate how severely negative prices affect investors.  
Figure 5: PV generation and load profile of the day with the lowest consumption—7 May 2024.  
The phenomenon of negative prices is observed throughout Europe, with a clear trend of increasing the total number of hours with negative prices [77; 78]. For example, according to data from the electricity market information platform \"SMARD\", in 2023 in Germany there were a total of 301 h with negative prices, and in 2024--457 h. The reasons are as follows: the rapid solar and wind energy expansion, inflexible conventional generation, and a lack of storage capacity.  
Electricity does not become cheaper (at least in nominal terms) as revealed by the analysis of average levels (Figure 3), but profits from PV power plants without energy storage are reduced due to the hourly price distribution.  
A typical economic cycle is observed, where oversaturation leads to a decline in the profitability of classical photovoltaic power plants without storage capacity. For this reason, it can be expected that the growth rate in the installation of new grid-connected PVs will slow down, and after the accumulated momentum has dissipated, it will stop.  
Figure 6: Correlation between PV production and electricity price: (**a**) for a day in December, (**b**) for a day in July.  
Figure 7: Negative prices during the third week of April 2025.
## 1 Introduction
### Objectives  
The major contribution of the proposed model is to incorporate a fuzzy-based intelligent device with four-layer architecture in the presence of a Hadoop infrastructure to process the biomedical signals that satisfy the multi-objective functionalities as follows:  
* To maximize the amount of signal strength that is used for recording the signals at various states, using a Hadoop structure.
* Preventing the loss functionality of signals that are transferred at larger distance with high separations inside the Hadoop infrastructure.
* To achieve a better correlation between incorporated fuzzy systems to make the signal travel at large-scale conditions at a low cost.
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 3 Results
### Correlation Analysis between Vegetation Index and Yield  
The vegetation indices constructed from the crown-level hyperspectral data during the wheat milk filling stage were subject to a correlation analysis with the actual wheat yield for each plot, utilizing the SPSS software. Among these indices, twelve exhibited correlation coefficients exceeding 0.5, as presented in Table 4. Most of them achieved significant correlations at the 0.005 significance level, NDVIg, NVI, NDRSR and GNDVI displayed significant correlations at the 0.02 significance level. Remarkably, NDII, NDWI, NDMI and VDI all had correlation coefficients exceeding 0.6, with NDWI displaying the highest correlation coefficient with a yield of 0.705. The lowest correlation coefficient between NDRE and the yield was 0.521.