esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 2 Sensing Techniques
### Electricity Meters
#### 2.3.1 Instantaneous Measurements
Interferometric techniques use a Sagnac interferometer to perform interference measurements on two counter-propagating light beams in fiber optic coils enclosing the current-carrying conductor, inducing the magnetic field to alter the polarization state of the light beams [146]. Whereas polarimetric sensors suffer from linearity of measurement and inaccuracy due to bend stress-induced birefringence, polarimetric techniques eliminate or attenuate these artefacts but are themselves prone to thermal drift and vibration vulnerabilities, but the overall Faraday effect-based metrology offers superior accuracy, galvanic isolation, and large dynamic range, albeit at high costs [147]. Apart from the sensing technique employed, other important system-level considerations also need to be taken into account and are usually implemented in the meteringinfrastructure. As described earlier, these analogue sensors are almost always tied to an analogue-to-digital (A/D) interface that converts the sensed values into a digital domain, where the major issues at the grid level are the sampling rate, the reporting rate, and the accuracy. Another aspect is the number of phases, with the typical number being either one or three. The connection of the sensing device with the power interface determines the type of measurement being made as either line-to-neutral or line-to-line. The values being recorded are usually in RMS and are averaged out to reduce the noise. The recorded values are further processed statistically to measure the spread and trend of the consumption and production.
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 3 Simulation Analysis
### Results of Simulation Analysis
#### 3.3.4 Pressure Analysis in Cavitation Area  
Figure 25 depicts the flow field pressure distribution at \(n=6000\) rpm. The texture dimples affect the pressure distribution, but the oil pressure distributed consistently overall except for the oil near the dimples, and the cavitation vapor appears at the same location as the low-pressure area.  
Figure 24: Cavitation volume distribution in texture dimples at different depths ((**a**) is cavitation contour when texture depth is 0.05 mm; (**b**) is cavitaion contour when texture depth is 0.1 mm; (**c**) is cavitaion contour when texture depth is 0.15 mm).  
Figure 23: Effect of texture depth on oil film cavitation.  
Figure 26 depicts the radial distribution of oil pressure on the upper surface of oil film, with overall pressure decreasing along the radial direction; \(\theta=6^{\circ}\) is in the texture region, and the oil flow through the texture dimple causes a significant pressure pulsation, where \(\theta=5.5^{\circ}\) is in the non-texture region, and the pressure curve is relatively smooth. As a consequence, pressure turbulence caused by the weaving structure is the primary cause of cavitation.  
Conversely, whether cavitation affects pressure requires further investigation. To statistically measure the pressure distribution inside a single texture, we ran simulations under two conditions: one considering the cavitation effect and one disregarding the cavitation effect [42, 43]. Figure 27 depicts the pressure distribution inside a single texture dimple at various rotational speeds while accounting for cavitation effect (0.9-1.1\({}^{\circ}\) for the texture region, 0.8-0.9\({}^{\circ}\) and 1.1-1.2\({}^{\circ}\) for the non-texture region). The oil is strongly squeezed near the texture root due to the strong shearing effect and barrier effect in dimple, resulting in a significant decrease in the low-pressure value and a significant increase in the high-pressure value.  
Figure 26: Radial pressure distribution of oil film.  
Figure 25: Pressure contour of oil film (**(a)** is pressure distribution of oil film with textures; **(b)** is pressure distribution of oil film without textures).  
Figure 28 depicts the pressure distribution in the texture dimple at various rotational speeds while ignoring the cavitation effect. The pressure distribution in the non-texture region has the same pattern regardless of cavitation effect, whereas the pressure distribution in the texture region has the same pattern at low rotational speeds, i.e., when cavitation does not occur. As the speed increases, particularly at \(n=6000\) rpm, the low-pressure region has a lower peak pressure of \(-530\) Pa when the cavitation effect is ignored, whereas the low-pressure peak increases when the cavitation effect is considered, implying that the cavitation effect partially enhances the fluid dynamic pressure effect.
## 6 Conclusions and Future Directions  
A conceptual framework has been proposed in this paper to aid the transition from reactive to proactive maintenance approaches to enable energy reduction. An integration-type adaptation of CRISP-DM is developed to incorporate domain expertise into the analysis. The novel integration of engineering knowledge and a data science process model has led to a structured cycle whereby knowledge-based approaches solve and avoid visible problems, and data-driven approaches solve and avoid invisible problems. The IDAIC framework therefore addresses the lack of direction to transition from reactive to proactive maintenance approaches. The second novel contribution of this paper is the extension of CRISP-DM for the purpose of managing the \"3B\" data issues. The contributions of greatest importance are firstly, the data contextualization phase whereby domain expertise is incorporated to understand if the features available are capable of solving visible problems. Secondly, the data assessment phase incorporates knowledge-based FDD for the novel purpose of assessing data quality rather than problem identification. Thirdly, knowledge-based FDD is applied to retrospectively analyze the asset or system to isolate visible problems that must be solved before the value of data-driven analysis may be realized on invisible problem solving.  
While the proposed IDAIC framework has been discussed at a high level in this paper, practical implementation must follow in future work. As noted by [PERSON] and [PERSON] [54], \"if the early adopters fail with their data mining projects, they will not blame their own incompetence in using data mining properly but assert that data mining does not work\". As discussed in Section 5, we plan to apply the IDAIC framework to an air handling unit in a large medical devices manufacturing facility to test the applicability of the framework in the real world.  
**Author Contributions:** Conceptualization, M.A., D.T.J.O. and K.B.; methodology, M.A. and K.B.; investigation, M.A., D.T.J.O. and K.B.; resources, D.T.J.O. and K.B.; writing--original draft preparation, M.A.; writing--review and editing, M.A., D.T.J.O. and K.B.; visualization, M.A., D.T.J.O. and K.B.; supervision, D.T.J.O. and K.B.; project administration, D.T.J.O. and K.B.; funding acquisition, D.T.J.O. and K.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication has emanated from research that was part-funded by Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate, and Marine [Grant No: 12/RC/2302_P2], with supporting funding obtained from DePuy Synthes. For the purpose of OpenAccess, the author has applied a CCBY public copyright license to any Author Accepted Manuscript version arising from this submission.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Application--Results
### Economic Assessment
This reduction could be attributed to the decommissioning of lignite units, which present a higher LCOE compared to RES (wind and PV installations) in 2019. Natural gas units gradually replace lignite ones by 2030. Carbon dioxide allowances are expected to rise to a higher level by 2030; therefore, the operational expenditures of conventional units are increased, resulting in a higher LCOE for natural gas units. The expected decrease of the CAPEX required for RES installations contribute to the significant decrease of the weighted average LCOE of the system. By 2050, a further reduction of the system's cost is expected; the levelized cost is approximately 75 \(\upepsilon\)/MWh, reduced by 30% compared to current levels. Increasing the participation of renewable energy sources in the energy production with the simultaneous significant reduction of their CAPEX leads to a decrease in the levelized cost of the system. Hydro-pumped storage is among the technologies presenting the lowest LCOE, following wind and PV. The CO\({}_{2}\) prices are significantly higher compared to 2030 resulting in the increase of the levelized cost of energy of natural gas power plants. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**2050**} \\ \hline
**Technology** & **Natural Gas** & **PV** & **Wind** & **PHS** & **Hydro** & **CHP** & **Biomass** \\ \hline Nominal power & 7100 & 12,000 & 12,000 & 2700 & 3565 & 125 & 600 \\ \hline Produced Energy (GWh) & 18638 & 16,924 & 25,620 & 5334 & 5203 & 402 & 1929 \\ \hline CAPEX (\(\upepsilon\)/kW) & 700 & 500 [64,65] & 800 [49] & 1200 & 1800 & 1100 & 2650 \\ \hline Fixed O\&M (\(\upepsilon\)/KW) & 21 & 10 & 32 & 18 & 18 & 40 & 79.5 \\ \hline Variable O\&M (\(\upepsilon\)/MWh) & 2 & - & - & - & 2.05 & 2 & 3.2 \\ \hline Cost (\(\upepsilon\)/t\({}_{\text{ignite}}\) \(\upepsilon\)/m\({}^{3}\) Natural gas) & 0.3 & - & - & - & - & 0.3 & 130 \\ \hline Lower Heating Value (GJ/ t\({}_{\text{ignite}}\) \(\mid\) GJ/ m\({}^{3}\) Natural gas) & 0.03 & - & - & - & - & 0.03 & 19 \\ \hline Efficiency (\%) & 60\% & - & - & - & - & 35\% & 35\% \\ \hline Heat rate (GJ/MWh) & 6.0 & - & - & - & - & 10.3 & 10.3 \\ \hline Fuel unit cost (\(\upepsilon\)/MWh) & 60 & - & - & - & - & 103 & 70 \\ \hline CO\({}_{2}\) emissions (t/Mwh) & 0.5 & - & - & - & - & 0.5 & - \\ \hline CO\({}_{2}\) cost (\(\upepsilon\)/t) & 88 [27] & - & - & - & - & 0 & - \\ \hline Carbon Cost (\(\upepsilon\)/MWh) & 44 & - & - & - & - & 0 & - \\ \hline Life time (years) & 30 & 25 & 25 & 50 & 50 & 30 & 30 \\ \hline CRF & 0.081 & 0.086 & 0.086 & 0.072 & 0.072 & 0.081 & 0.081 \\ \hline
**LCOE (\(\upepsilon\)/MWh)** & **135.5** & **37.5** & **47.1** & **53.1** & **102.6** & **138.9** & **164.6** \\ \hline
**Weighted average LCOE (\(\upepsilon\)/MWh)** & & & & **75.06** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data used for LCOE calculation for 2050.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## Materials and Methods
### UAV Platform and Data  
#### 2.2.1 UAV Platform and Multispectral Instrument  
The UAV flights took place over the wheat trial plots on the corresponding ground measurement dates (Table 1) using a hexa-copter platform (DJI Matrice 600) at a height of 100 m above ground level and a constant speed of 3.1 m/s. Each of these flights were carried out close to solar noon (between 11:00 and 14:00 GMT+1) to avoid errors due to a low solar elevation angle. In order to ensure consistent and comparable multi-date coverage, the platform was equipped with a real-time kinematic Global Navigation Satellite System (GNSS) and was flown autonomously using the same flight mission, which was pre-programmed using the DJI Ground Station Pro software, for each of the measurement dates.  
Multispectral imagery was acquired throughout the UAV flights using the MAIA camera system (SAL Engineering/EOPTIS), which is composed of an array of nine monochromatic sensors, each having a 1.2 Mpixel resolution. Furthermore, the nine sensors of the MAIA camera (referred to hereafter as the MAIA/Sentinel-2) have band-pass filters that have the same central wavelength and width as that of the first nine bands (i.e., bands 1 to 8A, Table 2) of the ESA Sentinel-2 multispectral instrument [37]. During the UAV flights, these sensors imaged the trial plots from a fixed nadir position with the aid of a three-axis stabilisation gimbal (DJI Ronin-MX).  
The pixels in the nine MAIA/Sentinel-2 sensors collected data simultaneously via global shutters, thus allowing all the nine band images to be recorded in a single acquisition [38]. The sensors had horizontal and vertical angles of view of 33.4\({}^{\circ}\) and 25.5\({}^{\circ}\), respectively, and a fixed focal length of 7.5 mm, which corresponded to a ground-sampling interval of 47 mm for our flight mission at 100 m above ground level. The MAIA/Sentinel-2 system also had a standard GNSS receiver that synchronously logged the position and time at which the camera's shutter was activated. Throughout each of the UAV flights, a total of 18 MAIA/Sentinel-2 images were acquired over the trial plots and saved in a proprietary raw format with a 12-bit radiometric resolution.
## 2 Materials and Methods
### Synthesis of MIPs by Pickering Emulsion Polymerization  
Bacterial-stabilized Pickering emulsions were prepared using a method similar to that described by [PERSON] et al., except that the NAC-QD complex was used to replace NAC to form a bacteria-pre-polymer complex [23]. First, 900 \(\mu\)L of the NAC-QD solution and 300 \(\mu\)L of the _L monocytogenes_ PBS suspension (OD\({}_{600}\) = 2) were mixed; the mixed solution was set aside for 30 min to form the NAC-QD bacteria network, which was used as the water phase in the Pickering polymerization. Second, 0.6 mL of TRIM, 0.6 mL of DVB, 6.2 mg of BPO, and 31.3 \(\mu\)L of DMA were added in another 5 mL tube as the oil phase. Subsequently, the two phases were mixed by vigorous hand shaking for 10 min, such that a stable Pickering emulsion was established. The Pickering emulsion was stabilized via the NAC-QD-bacteria network and was kept still for 24 h at room temperature without agitation. To remove the template, the resulting polymer beads were successively washed with 10% acetic acid, 1% SDS, water, and methanol. After drying in a vacuum chamber, MIPs with specific binding sites for _L. monocytogenes_ were obtained.  
As a control, non-imprinted polymer (NIP) beads were also prepared using the same procedure, with the exception of the absence of _L. monocytogenes_.
## 4 Results and Discussion
### Effects at Biological Level: ROS
The analysis of carotenoids after 96 h of SMF exposure shows a clear increase in violaxanthin with respect to the control, revealing the action of the defense of the cell against ROS oxidative stress [56; 60]. On the contrary, in the case of Species 1, there is a significant inhibition of SOD with respect to the control. To explain the behavior of the enzyme for these cells, we can hypothesize several points. (i) SODs are metalloprotein that catalyze the dismutation of O\({}_{2}\)\({}^{-}\) to H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\)[53; 61; 62]. Depending on the transition metal found in its active site, SODs can be categorized into three types: Cu/Zn-SOD, Mn-SOD, and Fe-SOD. They are found mainly in cytosol, chloroplasts, and mitochondria [63]. Therefore, the malfunction of the enzyme in this process could be because metal ions present in the active site are being affected by the SMF avoiding the enzyme-substrate assembly and thereby inhibiting the functioning of SOD. (ii) Another aspect to consider is that, when some ROSs such as H\({}_{2}\)O\({}_{2}\) are found in large intracellular concentrations, it can irreversibly inhibit the enzyme [64].
## 4 Mathematical Verification of Total Blocking Voltage  
The blocking voltage is the voltage that appears across a switch when it is reverse biased, and the total blocking voltage is the summation of the individual blocking voltages of the requirement of each switch of a topology. The TBV of the conventional cycloconverter shown in Figure 1a can be calculated as shown in Equation (9).  
\[\mathrm{TBV}=\mathrm{V_{T1}}+\mathrm{V_{T2}}+\mathrm{V_{T3}}+\mathrm{V_{T4}} \tag{9}\]  
The blocking voltage of T1, T2, T3, and T4 switching device is V.  
Therefore, the total blocking voltage is:  
\[\mathrm{TBV}=\mathrm{V}+\mathrm{V}+\mathrm{V}+\mathrm{V}=4\mathrm{V} \tag{10}\]  
Therefore, the total blocking voltage/voltage = 4 for the conventional cycloconverters. The proposed cycloconverter in Figure 3a has the blocking voltage for Triac devices T1, T2, T3, T4, and T5 as V, (V/2), (V/2), (V/2), and V, respectively. The TBV/V for the proposed cycloconverter equals 3.5 less than that of the conventional one.  
Figure 16: Physical experiments output responses of proposed cycloconverter for one-fourth frequency conversion on the left and one-third frequency conversion on the right.  
Figure 15: Physical experiment setup of the proposed cycloconverter controlling the voltage of resistive load.
## 2 Tool Wear Monitoring Methods  
### Continuous Wavelet Transform of a Signal  
CWT is a signal processing method that provides joint analysis in terms of time and frequency, making it particularly suitable for analyzing non-stationary signals. The CWT time-frequency map, a two-dimensional representation of the energy density of the signal obtained through continuous wavelet transform, reflects more detailed and accurate information than one-dimensional signal features [19; 20; 21].  
The Morlet wavelet is chosen as the mother wavelet function, set to \(\varphi(x)\), and the wavelet transform of the input \(f(x)\) is as follows:  
\[W_{f}(a,b)=\int\limits_{-\infty}^{+\infty}f(t)\overline{\varphi_{(a,b)}(t)}dt \tag{1}\]  
where \(a\) denotes the stretch factor, \(b\) indicates the translation factor, and \(a\), \(b\in\mathds{R}\), \(a\
eq 0\), which together control the scale of the wavelet function, \(W_{f}(a,b)\) represents the inner product of the input and the wavelet function, \(W_{f}(a,b)\) denotes the wavelet transform of \(f(x)\). Taking the modulus of \(W_{f}(a,b)\) and squaring it yields the formula for the CWT time-frequency diagram:  
\[G_{f}(a,b)=\left|W_{f}(a,b)\right|^{2} \tag{2}\]  
Equation (2) is the CWT time-frequency diagram conversion formula for \(f(x)\), and \(G_{f}(a,b)\) is the square of the mode of \(W_{f}(a,b)\). Using the above method, the physical signals are converted into CWT time-frequency maps as inputs to the model, which enhances immunity to interference and noise and can improve the accuracy of the final classification results.
## 6 RF Feature Classifiers
### ML-Based Classification  
Machine learning (ML) methods have been widely used in the literature as methods of classification in RF fingerprinting approaches or for transmitter identification and authentication (see the references from Table 1). Typically, three main classes of ML approaches are encountered, namely: unsupervised learning (k-means, fuzzy k-means, etc), supervised learning (e.g., kNN, SVM, random forest, gradient boosting, etc.), and reinforcement learning (e.g., Markov decision processes, etc.). In addition, deep learning methods, such as CNN, can be applied typically both in a supervised or unsupervised manner. The fact that the data are not annotated or labelled in unsupervised approaches makes the unsupervised approaches less useful than the supervised ones in the context of RFF, where one would like to have the exact labels of the genuine transmitters. Moreover, reinforcement learning methods are typically rather complex and rely on harnessing additional data from the environment. They have not been studied yet in the context of RFF for GNSS to the best of the authors' knowledge and are highly unlikely to work with GNSS pre-correlation data as their complexity combined with the huge amount of pre-correlation data to be processed will be prohibitive. A recent, not yet peer-reviewed work on reinforcement learning (i.e., a policy gradient method) with RFF for an ADALM-PLUTO software defined radio (SDR) can be found in [85], but the focus in there was to apply reinforcement learning to enhance the spoofer capabilities in the context of a quadrature phase shift keying (QPSK) communication systems, not to identify the spoofer. For these reasons, only the supervised and deep learning approaches have been investigated to date in the context of RFF and these are also the ones we will briefly describe in the next sub-sections.
## 5 Experiments and Results
### Experimental Setup and Evaluation Metrics
To further validate the classification performance of the proposed model, we plotted the PR (Precision-Recall) curve and ROC (Receiver Operating Characteristic) curve of the GAN-CNN-LSTM model, as shown in Figure 11. The trends of these curves indicate that the proposed model demonstrates excellent classification performance in the fault detection task. Furthermore, to assess the stability and reliability of the proposed GAN-CNN-LSTM model, a bootstrap analysis (n = 1000) was conducted to estimate the confidence interval for the AUC metric. The results show that the average AUC of GAN-CNN-LSTM is 0.9927, with a 95% confidence interval of [0.9909, 0.9942], indicating highly robust and consistent fault detection performance on the test set. To assess the deployment feasibility of the proposed model, we measured the inference time and model size on a workstation equipped with an NVIDIA RTX 4090 GPU and an Intel i9-14900K CPU (single-threaded, batch size = 1, sequence length = 256, feature dimension = 8). The average inference time per sample is approximately 0.44 milliseconds on the CPU, and the total number of model parameters is 81,889. Given that typical BMS systems have data acquisition intervals of several seconds, our model's latency is orders of magnitude faster than real-time requirements, making it well-suited for integration into embedded or edge computing environments in battery management systems. In summary, the proposed GAN-CNN-LSTM model exhibits outstanding overall classification accuracy and, more critically, shows high robustness and practical utility in detecting battery faults. These results validate the model's effectiveness in the power battery fault detection task.