esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Coupling of a Hybrid Power Plant with a Water Generation System
## 2 Methodology
### Economic Analysis  
The economic analysis is realized using the method of the Total Revenue Requirement (TRR). First, the Fixed Capital Investment (FCI) of the plant is estimated and specific assumptions for the analyzed scenario are taken from [11; 46]. To calculate the cost of a component based on a reference component of a different capacity, we use Equation 4. The calculation is based on a scaling exponent, \(\alpha\), which remains constant within a given size range and takes values lower than unity [44].  
\[C_{PE,1}=C_{PE,2}\left(\frac{X_{1}}{X_{2}}\right)^{\alpha} \tag{4}\]  
The calculated costs are scaled to the reference year (2018) by means of the chemical engineering plant cost index (CEPCI), as published in the Chemical Engineering Magazine [46].  
\[C_{ref}=C_{calc}.\frac{Index_{ref}}{Index_{calc}} \tag{5}\]  
The Levelized Cost of Electricity (LCOE) compares unitary costs over the economic life of energy systems. The LCOE is calculated in \(\frac{\epsilon}{KWh}\) using Equation (6) and includes the TRR over the 20 years of the plant life and the total energy generation (\(E_{gen}\)). The TRR contains the required initial investment (\(C_{capital}\)), the cost of fuel (\(C_{fuel}\)) and the operation and maintenance costs (\(C_{O\&M}\)) in current euros. For its part, the total energy generated is calculated taking into account the capacity factor of the plant (\(C_{f}\)), the working hours (\(t\)), and the total amount of power generated over this period of time (\(P_{gen}\)) [47].  
\[LCOE=\frac{TRR}{E_{gen}}=\frac{C_{capital}+C_{O\&M}+C_{fuel}}{C_{f}\cdot t\cdot P _{gen}} \tag{6}\]  
Currently, no reliable approaches for the calculation of the costs of wastewater treatment systems have been published in the literature. A mathematical model based on the Levenberg-Marquardt algorithm and the SigmaPlot Version 12.0 software has been used here to synthesize cost results for general applicability [48]. In this way, we can establish a relationship between the treatment technologies and the costs within the water reclamation facility. To fit the available cost data adequately over several orders of scale of systems, a logarithmic variant of [PERSON] law has been applied [49].
## 3 Influence of the Discharge Current
### Variation of the Ballast Resistor  
Maintaining the corona self-sustained discharge regime, we identified the influence of the ballast resistor in the argon single-stage EHD thruster, and we uncovered that, by decreasing the resistor value, we increased the number of charged particles, as was expected.  
Both thrust and thrust-to-power ratios were computed directly by the software and shown in Table 2. As can be observed, by changing the discharge current from 1 mA to 6 mA, the propellant force increases more than five times, from 4 nN to around 26 nN. Even with the increase in current, which consequently was accompanied by the power consumed on the plasma, the T/P efficiency ratio improved, highlighting the fact that there was a significant enhancement in the work regime of the thruster.
## 6 Conclusions  
Blind-RIS-SR-NOMA and blind-RIS-AP-NOMA uplink users' outage probability expressions are derived in this paper. Monte-Carlo simulation curves are utilized to substantiate the derived closed-form expressions. The analytical and simulation curves are nearly identical, indicating that the obtained expressions are accurate. The probability of an outage decreases as the number of reflecting elements grows. The optimal powers to be allocated to the users are also determined to maximize the uplink sum capacity. Optimal power allocation has a higher sum capacity than sub-optimal power allocation. By increasing the number of reflecting elements, the sum capacity can be increased even further. The sum capacity of blind-RIS-SR-NOMA is likewise slightly higher than that of blind-RIS-AP-NOMA. In terms of sum capacity for 20 dB SNR and 32 RIS elements, the blind-RIS-SR-NOMA system surpasses the conventional NOMA system by \(\approx\)38%. This study can be expanded to include intelligent RIS-assisted communication, in which the  
Figure 11: Sum capacity (b/s/Hz) comparison of blind-RIS-AP-NOMA for optimal and sub-optimal power allocations and various choices of \(N\).  
Figure 12: Sum capacity (b/s/Hz) comparison of NOMA, blind-RIS-SR-NOMA and blind-RIS-AP-NOMA for optimal and sub-optimal power allocations.  
channel information is known in advance at the RIS. This work can be practiced for discrete phase shifter-assisted RIS and generalized to a large number of users.  
The manuscript was written through the contributions of all authors. [PERSON] was responsible for the conceptualization of the topic; article gathering and sorting were carried out by V.B.K., A.L.I, F.R.C.S., P.G.S.V., S.J.T., A.M. and V.V.G.; manuscript writing and original drafting and formal analysis were carried out by [PERSON], A.L.I, F.R.C.S., P.G.S.V., S.J.T., A.M. and V.V.G.; writing of reviews and editing were carried out by [PERSON], A.L.I, F.R.C.S., P.G.S.V., S.J.T., A.M. and V.V.G.; [PERSON] led the overall research activity. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported in part by the Nigerian Petroleum Technology Development Fund (PTDF) and in part by the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.  
Not applicable.  
The data that support the findings of this paper are available from the corresponding author upon reasonable request.  
The authors thank the anonymous reviewers for their valuable comments which help to improve the quality of this work.  
The authors declare  
Taking antilog with respect to base-2 on both sides results in  
\[1+\frac{\mu_{1}\ \kappa\alpha_{1}}{\mu_{2}\ \kappa\alpha_{2}+1}<2^{\bar{D}_{1}} \tag{11}\]  
\[\frac{\mu_{1}\ \kappa\alpha_{1}}{\mu_{2}\ \kappa\alpha_{2}+1}<\underbrace{2^{\bar{D}_{ 1}}-1}_{D_{1}} \tag{12}\]  
\[\mu_{1}\ \kappa\alpha_{1}<D_{1}(\mu_{2}\ \kappa\alpha_{2}+1) \tag{13}\]  
Simplifying (13) results in (10).
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 4 Partial Discharge Measurement Using Different Types of Sensors on the Power Transformer and GIS
### Mechanism in Acoustic Sensors  
The PZT ultrasonic sensor is sensitive to signals generated by PD in oil at wideband frequencies (from 10 kHz to 500 kHz). Most of the PD detection and monitoring devices use acoustic sensors, which are usually located far from high-voltage hardware. A PD coupler is required for acoustic detection equipment that detects harmonic distortion. The vaporization of the oil's molecule results in a mechanical energy explosion, in which a pressure field is produced from which a sonic wave emerges. The capacitive properties of PD are unimportant when the acoustic technique is used due to its insensitive to changes in the capacitance of the test object [123]. When comparing the two PD signal detection systems, the acoustic one has a lower sensitivity than the electrical one. On the other hand, by measuring the time differences of arrival (TOA) of acoustic signals at multiple sensors deployed, it is possible to estimate the location where partial discharge occurs.
## 4 DPI Applied to Honeynet Traffic and Attacks
### Layer 4 Header Analysis
#### 4.3.1 Classification of Traffic by Its Destination Port  
The attacks to the Honeynet were organized in relation to the port trying to be exploited in all of the honeypots. Figure 8 shows the distribution of ports destination of the attacks. In the data collected and analyzed, there is a significant preference for ports 23 and 22.  
Figure 7: Most attacked honeypots.  
Table 7 presents the absolute number of packets directed to each port of the honeypots during the time of the collected data. From the data analyzed and based on the architecture of the Honeynet, it may be inferred that ports 23 and 22 together correspond to 90.1% of the attacks, respectively Telnet and SSH services and protocols.  
Detailing the analysis performed on those ports and protocols, the attacks on port 22 corresponded mainly to SSH brute force. Once the payload of SSH packets are encrypted, the log analysis of the attackers had to be done. Thus, it was possible to collect information about the users and passwords trying to login into the system. The same consideration applies to the traffic on port 443 (HTTPS protocol) and the payload cannot be understandable without log analysis.  
On the other hand, the attacks directed to port 23 (Telnet), 445 (microsoft-ds), 80 (HTTP) and 139 (netbios-ss), are not encrypted and, therefore, they can be analyzed with DPI. Although not present in the top six destination ports, malicious users tried to exploit port 21 (FTP) and also tried use port 123 (NTP) to perform a DoS attack on hosts outside the Honeynet. These protocols exchange the messages in plain text as well, allowing the study of the attackers' behavior.
## 1 Introduction
### True Experimental Data Processing  
(1) Obtain raw current data.  
The original grounding current data can be obtained by simulating the normal state of the low-voltage distribution area and different grounding fault types through the grounding test platform and simulation model in the low-voltage distribution area (because the protective resistance of the metallic grounding in the low-voltage distribution area is small, it is dangerous to obtain the original grounding data by means of the simulation model). However, there are redundant data and a lot of noise and harmonics in this data, so a series of data preprocessing operations are needed.  
(2) Remove void data and reference values.  
During the grounding experiment, adding the DC component to the test circuit can prevent the diode from burning out and ensure that the conditioning circuit is not reversed, but it will also cause the measurement baseline value of the current transformer to increase. Therefore, in order to ensure the accuracy of the total residual current detection, it is necessary to cut off the load, collect the DC data in the no-load line, and calibrate the  
Figure 4: Flowchart for constructing a low-voltage system fault detection model based on improved PSO-optimized SVM.  
current transformer. Take an average of all no-load data and subtract that average from all current data to eliminate the baseline value of the conditioning circuit, resulting in accurate total residual current data.  
(3) Proportional reduction.  
Due to the parameter setting of the conditioning circuit design in this experiment platform, there is a certain multiple difference between the collected data and the real data, because, in order to obtain the most authentic grounding data, the data need proportional restoration processing.  
(4) Filter denoising  
Due to the influence of the experimental environment and other aspects, the original grounding data collected by the NI equipment have great noise, so the original grounding data need to be denoised and filtered. The data waveform before and after processing is shown in Figure 5. It can be seen that the grounding waveform after wavelet transform filtering is smoother. With the decrease in the signal-to-noise ratio, the denoising effect slightly reduces.
## 3 Results  
### Environmental Variables, Land Use, and Hydrological Conditions  
LS was characterized by a mesotrophic to eutrophic condition with low Chl \(a\) values. Suspended particular matter (SPM) and particulate organic matter (POM) values were also low, showing high transparency conditions during the entire study period, with a Secchi disk mean value of 1.6 m (Table 2) and a mean euphotic zone value of 4.3 \(\pm\) 1.9 m. LS is a lake rich in DO with alkaline conditions (Table 2). Nutrient concentrations were also high during the entire study period (Table 2). With respect to land use, 74.8% of the lake's area of influence (220 km\({}^{2}\)) was represented by agricultural systems, 12.1% by forests, 8.2% by marshlands, and 1.4% by human settlements. According to SPI values, 2014 was a year with wet conditions. The lake experienced great changes during the study period associated with the water inflow and the consequent water level fluctuations (Figure 3). The mean lake volume during the study period was 0.0117 \(\pm\) 0.0075 km\({}^{3}\) (Figure 3a). During 2014, the total amount of water that entered the lake was 0.0025 km\({}^{3}\), reaching a maximum of 0.00005 km\({}^{3}\) day\({}^{-1}\) during October 2014. The water volume varied in response to the water input (Figure 3a). The maximum water level values were recorded in October 2014 and the minimum during February 2014 (Table 2). Conductivity was high, classifying LS as a mesosaline lake. Depending on the water level fluctuations, the conductivity values presented minimum values during October 2014 and maximum during March 2014. Precipitation values presented minimum values in January 2014 and maximum in March 2014 (Table 1). \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Equation** \\ \hline Hourly NEP & \(NEP_{h}=\Delta DO-\frac{F}{z}\) \\ Diffusive oxygen exchange & \(F=k(DO-DO_{sat})\) \\ Coefficient of oxygen exchange & \(k=k_{660}\left(\frac{Sc}{660}\right)^{\frac{1}{2}}\) \\ Schmidt number for LS & \(S_{c}=1953.4\ -\ 128.0\ T\ +\ 3.9918\ T^{2}\ -\ 0.046527\ T^{3}\) \\ Schmidt number for SG & \(S_{c}=1800.6-120.1\ T\ +\ 3.7818\ T^{2}\ -\ 0.0476\ T^{3}\) \\ Gas piston velocity & \(k_{660}=\left(2.07+0.215\ U_{10}^{1.7}\right)/100\) \\ Wind speed at 10 m & \(U_{10}=a\ U_{2.1}\) \\ Correction factor & \(a=1.4125h^{-0.15}\) \\ NEP during daytime & \(NEP_{daytime}=mean\ NEP\ during\ daylight\times dayfraction\times 24\ h\) \\ R during daytime & \(R_{daytime}=R_{hr}\times 24\ h\times dayfraction\) \\ R & \(R_{day}=R_{hr}\times 24\ h\) \\ NEP & \(NEP=GPP-R_{day}\) \\ GPP & \(GPP=NEP_{daytime}+R_{daytime}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Equations for the estimation of ecosystem metabolism in the La Salada (LS) and Sauce Grande (SG) lakes during the study period. NEP: net ecosystem production; GPP: gross primary production; R: ecosystem respiration.
# Enhancing the Quality of Sampled Phase-Only Hologram (SPOH) Based on Time-Division Comb Filtering
## 3 Proposed Method to Generate an Enhanced SPOH with Time-Division Comb Filtering  
Based on the analysis in Section 2, a method to enhance the reconstructed image of a SPOH is presented. Our proposed method is shown in Figure 4.  
A SPOH of a source image \(I(m,n)\) is generated according to the steps discussed in Sections 2.1 and 2.2. The reconstructed image of the SPOH is then processed by two-dimensional (2-D) time-division comb filtering (TDCF) to suppress half of the replica spectrums, resulting in decreasing the down-sampling factor by 2 times. TDCF is similar to classical comb filtering, with the exception that filtering is realized in the time domain instead of through numerical computation. In the following subsections, the principles of comb filtering, and our proposed method for enhancing the reconstructed image of SPOH with TDCF, are described.  
Figure 3: Frequency spectrum of signal \(I(m)\): (**a**) without down-sampling, (**b**) after down-sampled by 2 times, (**c**) after down-sampled by 4 times.  
Figure 2: (**a**) Source image, (**b**) reconstructed image with the phase component of the hologram of the source image, mostly invisible with most of the contents removed but with slight edge information, (**c**–**e**) reconstructed images of the sampled phase-only hologram (SPOH) of the source image with down-sampling factor \(\tau=8\), 12, and 16, respectively.
## 6 Discussion
### Quality Evaluation by Medical Doctors  
Global and large structures, e.g., the lateral ventricle, are well unconditionally generated with the proposed DDPM. However, relatively small structures, e.g., the hippocampus and cortical white matter contrast, are not very well generated with the proposed DDPM, as commonly indicated by the three medical doctors in the evaluation results. Nevertheless, most of the anatomically important structures scored more than 2.0 on average. Since in this study a score of 2 means \"slightly identifiable\", we believe that our proposed method can reconstruct these important structures properly. In the clinical domain, the corticomedullary contrast and the volumes of the hippocampus are important in disease diagnosis and prognosis prediction (e.g., Alzheimer disease [25]). Therefore, we need to improve the depiction correctness of these structures. Note that such a clinically relative evaluation of AI-generated volumes was rarely performed in previous studies and is thus one of our contributions in this paper.  
As the difficult structures (corticomedullary contrast and hippocampal) are slightly visualized in the results, we can also apply any additional postprocess to improve the depiction of these structures by, e.g., deep-learning-based filtering methods [26]. Generally, however, simple noise reduction postprocessing cannot recover the structures that are not represented in the original generated volume. In other words, for evaluating the quality of generated medical images, the ordinal contrast-noise ratio (CNR) or structural similarity index measure (SSIM) is not enough and the faithful recreation of relative anatomical structures must be confirmed by medical experts.  
On the other hand, the amount of statistical variety of generalized images is another quality measure of generative models and it should be evaluated by further experiments. It is one of our future works. However, it would be difficult to fairly evaluate the amount of variety of medical images generated by a given model, because medical image generation should be not only diverse but also resemble the distribution of a real population of human beings. The ordinal inception score (IS) and Frechet inception distance (FID) are based on and rely on another classifier model, which is rarely customized for medical images. Inventing a new methodology to evaluate the amount of variety of generated medical images will be another future work.
# Energy Efficiency Analysis of Fixed-Speed Pump Drives with Various Types of Motors
###### Abstract  
The paper presents a comparative analysis of energy consumption by 2.2 kW electric motors of various types and energy efficiency classes in the electric drive of a pump unit with throttle control in a water supply system. Line-start permanent-magnet synchronous motors of the IE4 energy efficiency class and induction motors of the IE4 and IE3 energy efficiency classes of various manufacturers were considered (IE4 and IE3 are labels of energy efficiency classes of electric motors according to IEC 60034-30-1 standard). Energy consumption at a hydraulic load changing under a typical duty cycle was calculated based on the nameplate data of the pump and electric motors. The developed method shows that selecting an electric motor based on the IE energy efficiency class under the IEC 60034-30-1 standard (i.e., based on efficiency at a rated load) may not provide the minimum energy consumption of a variable flow pump unit over a typical duty cycle. In particular, the considered IE4 class line-start permanent-magnet synchronous motors do not provide significant advantages over IE4 class induction motors, and sometimes even over IE3 class induction motors when they are used in variable flow pump units.  
centrifugal pump; energy efficiency; energy efficiency class; induction motor; line-start permanent-magnet synchronous motor; throttling control Article
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 2 Related Work
This highlights a critical gap in the literature for Python-based code smell detection: the absence of robust ensemble frameworks that incorporate both feature selection and data balancing strategies. Other Python-focused works, such as [PERSON] et al. [9] and [PERSON] et al. [39], explored severity detection and rule-based tools, respectively, but similarly lacked rigorous ensemble-based evaluations with cross-validation and statistical testing. The reviewed literature demonstrates that ML, ensemble learning, and deep learning techniques have been extensively applied to Java-based code smell datasets. However, the application of these methods to other programming languages, particularly Python, remains underexplored. [PERSON] et al. [3] demonstrated the feasibility of using ML models for detecting code smells in Python, marking an important step toward expanding beyond Java-centric approaches. Nonetheless, comprehensive investigations assessing the effectiveness of ensemble learning techniques, feature interpretability, and class balancing remain unaddressed on Python code smell datasets. A primary challenge is the lack of diverse, language-specific datasets, especially those tailored to Python, which hampers the generalization and applicability of existing methods. Most existing studies are tightly coupled to the structural and semantic features of Java, and few attempt to examine the cross-language generalizability of their methods. This leaves a critical gap in our understanding of how programming language paradigms influence the design and effectiveness of ML-based code smell detection approaches. This study addresses these gaps by employing a comprehensive ensemble-based framework using five ensemble methods (Bagging, Gradient Boost, Max Voting, AdaBoost, and XGBoost) on two Python-specific code smell datasets, LC and LM, combined with chi-square FST to retain only the most informative metrics, and SMOTE to correct class imbalance. This combination not only enhances model robustness and interpretability but also directly addresses the shortcomings of prior Python-based studies, particularly the reliance on individual classifiers and unbalanced datasets. Moreover, by evaluating the approach using 10-fold cross-validation and multiple performance metrics, including MCC and Cohen's Kappa, we aim to ensure balanced and reproducible outcomes.