esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 3 Solution Method
### Hmposo  
Particle swarm optimization (PSO) is an intelligent optimization technique that was firstly put forward in 1995 [31; 32; 33], and the fundamental idea of the PSO algorithm is to randomly generate particles in the solution space and make each particle gradually approach the optimal solution [34; 35]. In this paper, a hybrid MOPSO algorithm integrated with a non-dominated sorting genetic algorithm (NSGA-II) is programmed by MATLAB (Version R2010b, the MathWorks, Natick, MA, USA, 2010) to optimize the placements and sizes of ESSs in a hybrid wind/diesel power system. MOPSO is utilized to update the position and velocity for each particle to search the best allocation of the ESS, and NSGA-II is used to find a substantially improved spread of solutions and an improved convergence [36]. The procedure of the proposed method can be summarized as follows:  
1. Forecast the difference between wind power and load demand and calculate the total capacity of the ESSs.
2. Randomly generate a population with a certain number of particles for initializing all generators' voltage, the output power, and the position and size of the ESSs. The random selections of the swarm of particles considering constraints and corresponding velocity for each particle are initialized.
3. Discretize the joint wind power distribution into a three-point distribution by the proposed estimation method, which is discussed in Section 3.1.
4. Select the candidate buses for installing ESSs via \(P\)-\(V\) sensitivity analysis.
5. Through probabilistic power flow, evaluate the particles by fitness function and recall their best positions associated with the best fitness value.
6. Check and preserve the _pbest_ (particle best) and _gbest_ (global best); if the algorithm has not yet found the minimum cost, emissions, and voltage fluctuations, update the _pbest_ and _gbest_.  
Figure 3: Non-linear auto-regressive model with exogenous inputs—back-propagation neural network (NARX-BPNN).  
Duplicate the initial population to another population to form a combined population and update the position and velocity of each particle.
* Sort the members in the new population through NSGA-II with an elitism algorithm for selecting the best solutions to renew the original population.
* Repeat Steps 5-8 until all scenarios are considered.
## 1 Introduction
Recently, the Croatian Energy Market Operator (HROTE) launched a public bid to allocate 350 MW for utility-scale solar PV projects and another 100 MW of solar PV for small and  
Figure 1: Change in total installation costs (TICs) and levelized cost of electricity (LCOE) by renewable energy technology between 2010 and 2023. 2030 and 7.4-20.3% in 2050 due to the increased frequency and severity of heatwaves [26]. Heatwave events lead to increased electricity demand and greenhouse gases emissions for the power sector [27]. Daily electricity consumption can increase by 40% during heatwaves when the minimum nighttime temperature remains above 24 \({}^{\circ}\)C [28]. However, during heatwave days, wind and solar PV generation profiles are complementary and their combined generation can improve grid stability [29]. Generally, wind power decreases during daytime heatwaves because of reduced wind speeds [30]. However, wind power plants situated near the coasts may benefit from enhanced sea-land breeze during the night [31]. The wind power output can be reduced by up to 30.8% during heatwaves in southern European countries [32]. Solar radiation and solar PV production, on the other hand, increase during daytime heatwaves [33]. Part of the increased solar PV production may be lost due to the reduction of PV module efficiency caused by elevated ambient temperatures [34]. The increased use of air-conditioning in the future will amplify the effect of climate change on peak loads [35,36]. This study investigates the combined performance of wind and solar PV generation during an extreme heatwave scenario in Croatia, using a high-temporal-resolution (15-minute interval) simulation model. The literature is limited in studies that investigate the combined role of variable renewable energy and dispatchable sources in meeting electricity demand during present and future heatwave events. Furthermore, previous research has generally overlooked the impact of expanding solar PV and wind power on renewable energy oversupply and electricity prices. This study addresses that gap by modelling Croatia's power system at a 15-minute resolution, simulating the July 2024 heatwave under projected 2030 scenarios with increased penetration of wind and solar PV. The rest of the article is organized as follows. Section 2 explains the methodology, including data sources, the modeling approach, and the assumptions for simulating the Croatian power system. Section 3 presents the analysis of the results and the discussion, first looking into the impact of the July 2024 heatwave and then foreseeing how the power system is expected to perform in 2030, assuming increased wind and solar PV capacities. Section 3 also examines potential problems such as renewable energy oversupply and electricity prices. Finally, Section 4 summarizes the main findings and outlines guidelines for future research.
## 3 Experiment and Discussion
### Interpretability Analysis  
In order to improve the interpretability of the model, this paper uses the integrated gradient (IG) [45] method to analyze the impact of input features on the prediction model. Integrated gradient is a technique used to explain the prediction results of deep neural networks. It performs linear interpolation between the input features and the reference point and calculates the gradient integral of the model output along this path to determine the importance score of each feature, which is represented by IG. The higher the IG value, the more important the feature is to the model prediction. We calculated the IG values of all words in the clinical notes of the test set and obtained the important feature set sorted by value. As shown in Table 6, we select the top 18 clinically significant terms to analyze their impact on mortality prediction performance.  
As shown in Table 6, clinically significant words such as \"pain\", \"fever\", and \"cough\" exhibit high IG values. These terms represent common symptoms in ICU care and are  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**IG Rank** & \
\begin{tabular}{c} **Clinically** \\ **Significant Words** \\ \end{tabular} & **Common Words** & **IG Rank** &
\begin{tabular}{c} **Clinically** \\ **Significant Words** \\ \end{tabular} & **Common Words** \\ \hline \hline
1 & pain & the & 10 & conditions & diseases \\ \hline
2 & fever & with & 11 & failure & from \\ \hline
3 & cough & medical & 12 & drug & history \\ \hline
4 & respiratory & diagnosis & 13 & pulses & admitted \\ \hline
5 & pneumonia & year & 14 & sob & end \\ \hline
6 & heart & old & 15 & acute & let \\ \hline
7 & brain & will & 16 & insulin & visit \\ \hline
8 & clear & not & 17 & increasing & unspecified \\ \hline
9 & mental & possible & 18 & seizure & status \\ \hline \end{tabular}
\end{table}
Table 6: The top 18 words used for model interpretability analysis calculated by IG.  
strongly correlated with the severity of a patient's condition, making them important indicators for model predictions. Other words, such as \"fever\" and \"seizure\", are often considered to be obvious manifestations of acute clinical symptoms and also have prognostic significance in predicting mortality. In contrast, common words in clinical notes, such as \"will\", \"possible\", and \"the\" lack specialized medical information and provide little useful semantic value for model predictions. By calculating the IG values of different words and then analyzing the top 18 clinically significant words that contribute to the model's mortality prediction, it is possible to provide a rational and reliable basis for the results of clinical prediction, assisting clinicians in making more accurate treatment and diagnostic decisions.
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 3 Our Proposed Methods
### Improved ECA  
Referring to the ground-based cloud image sample in Figure 1, due to the all-sky imager being utilized to acquire the image, only the inscribed circular part of the image is the effective area, and the surrounding four corners are invalid black pixels. Therefore, although GAP has strong noise suppression capabilities, it still does not work well for the special images. To avoid the above-mentioned problems, global max pooling (GMP) is used to prevent the introduction of invalid parts in the feature calculation, which improves the ability of channel feature extraction to a certain extent. The GMP is shown in Equation (5)  
\[\gamma_{gmp}=\max_{\begin{subarray}{c}i\in[1,w]\\ j\in[1,h]\end{subarray}}(\mathbf{X}_{ij:}),\mathbf{X}\in\mathbb{R}^{w\times h\times c}, \tag{4}\]  
Like global average pooling, the vector \(\gamma_{gmp}\) is multiplied by the band weight matrix \(\mathbf{V}_{k}^{gmp}\). The output obtained is the channel weight vector \(\mathbf{\eta}_{gmp}\)  
\[\mathbf{\eta}_{gmp}=\sigma(\mathbf{V}_{k}^{gmp}\gamma_{gmp}),\mathbf{V}_{k}^{gmp}\in \mathbb{R}^{c\times c}, \tag{5}\]  
Then, the ECA attention module is spliced with the pooling layer, GAP and GMP are employed to jointly extract the global features of \(\mathbf{X}\). It should be noted that GAP and GMP are used here in parallel to aggregate the spatial information of the two feature maps. If GAP is executed before GMP, then after the GMP operation, all the values less than the maximum value in the feature map will be discarded, and this is the useful information processed by GAP.  
\[\mathbf{X}^{\prime}=\sigma(\mathbf{\eta}_{gap}+\mathbf{\eta}_{gmp})\mathbf{X},\mathbf{\eta}_{gap} \in\mathbb{R}^{1\times 1\times c},\mathbf{\eta}_{gmp}\in\mathbb{R}^{1\times 1 \times c}, \tag{6}\]  
where \(\mathbf{X}^{\prime}\) represents the output of the entire attention module.  
In the ECA module, there are two combinations of GAP and GMP. As illustrated in Figure 5(a),b, we named them the weight sharing method (ECA-WS) and the weight independent method (ECA-WI), respectively.  
Figure 5: \(3\times 3\) overlap pooling kernel.  
In ECA-WS, GMP uses the same band weight matrix with GAP, that is, \(\mathbf{v}_{k}^{gap}=\mathbf{v}_{k}^{gap}\). It hardly increases the parameters of the ECA module but improves the feature extraction capability of the module. In ECA-WI, GMP uses the different band weight matrix with GAP, that is, \(\mathbf{v}_{k}^{gap}\
eq\mathbf{v}_{k}^{gap}\). However, this method not only increases the network parameters but also reduces the correlation between the two global pooling. This may lead to the instability of the combined network performance, which is also proved by comparative experiments. Therefore, ECA-WS attention mechanism is used to improve the characterization ability of the channel characteristics of the VGG16 sub-network after each stage of pooling.
## 2 Results and Discussion
### Leu/Ile-Derived VOCs
Proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) is a quick, direct, non-invasive, and highly sensitive (parts per trillion by volume) online method that can be used to monitor the production of VOCs [69]. Such an approach would be useful in determining how VOCs change over time in the DM supplemented with different amino acids. Among the fermentation-derived VOCs detected in the present study, dimethyl trisulphide, methional, 2-nonanone, 2-undecanone, 1-hexanol, and 1-octanol were characteristic odour-active VOCs in cooked meat [70]; acetic acid, 3-methyl butanoic acid, methional, dimethyl trisulphide, and hexanoic acid were odour-active VOCs in dairy yoghurt [71]; and acetic acid, butanoic acid, 3-methyl butanoic acid, 3-methyl butanol, methional, methanethionl, dimethyl disulphide, dimethyl trisulphide, 2-phenylethyl acetate, benzaldehyde, ethyl hexanoate, 2-heptanone, 2-nonanone, and 2-undecanone were odour-active VOCs in dairy cheese [61; 72]. Hence, the current study has reinforced the critical role that substrate composition plays in VOC production, and the knowledge gained will help researchers to develop plant-based fermentations designed to generate specific VOCs that, when added to a product, will contribute to meat or dairy flavours. For example, using a plant-based substrate high in methionine or cysteine is likely to result in the production of higher concentrations of sulphur-derived VOCs such as methional, methanethiol, methionol, dimethyl sulphide, dimethyl disulphide, and dimethyl trisulphide, which are important contributors to meat or dairy flavours.
## 2 FRCCs-Based Electrical Impedance Acquisition System and Crack Localization Algorithm  
In this section, the instantaneous multi-channel electrical impedance acquisition system incorporated with FRCCs and the corresponding crack localization algorithm is introduced. First, the instantaneous multi-channel electrical impedance acquisition system, enabling to generate the instantaneous alternating current (AC) input with broadband frequency range and simultaneously measure the corresponding responses, is introduced. Then, the manufacturing process of FRCCs used for the subsequent experiments is described. Finally, the corresponding crack localization algorithm is also explained.
## 3 Related Work and Methodology
### Global and Local Moran's I
#### 3.3.3 Local Moran's I  
Local Moran's I (LMI), is a statistical measure used to assess the spatial autocorrelation of data within a geographic area. It identifies and quantifies the degree to which individual data points are clustered or dispersed in relation to their neighboring data points. The Local Moran's I for a location \(i\) is given by [43]:  
\[I_{i}=\frac{\big{(}X_{i}-\overline{X}\big{)}}{S^{2}}\sum_{j}w_{ij}\big{(}X_{j} -\overline{X}\big{)} \tag{7}\]  
where \(S^{2}\) is the variance of \(X\), and the other terms are as defined previously. The significance of Local Moran's I values is typically assessed through permutation tests, providing insights into the local structure of the spatial correlation. High positive values suggest clusters of similar high or low values, while negative values indicate spatial outliers.  
While the GMI provides an overall measure of spatial autocorrelation, it does not allow for the detection of local patterns of spatial association. Local Moran's I (LMI), also known as the Local Indicators of Spatial Association (LISA), identifies clusters of similar values and outliers in spatial data [39; 44]. LISA are applied to depict the spatial dynamics of earthquake hazard. LISA cluster the mapped region into different clusters. High-high (H-H) areas are characterized by high concentrated seismic activity and are surrounded by regions with similarly high seismic hazard indices. In contrast, low-high (L-H) areas exhibit potentially low seismic hazard, yet are encircled by regions with high seismic hazard. Low-low (L-L) regions have low seismic hazard and are bordered by areas with corresponding low indices. Finally, high-low (H-L) areas display high seismic hazard but are surrounded by regions with lower seismic hazard levels. Utilizing these classifications, LISA evaluate the seismic hazard of earthquakes at a significance level of \(p=0.05\), and those provinces, autonomous regions, or municipalities that surpass this threshold are subsequently illustrated on a LISA cluster map. The mathematical expression for LISA is structured as follows [44]:  
\[I_{i}=\frac{(\eta_{i}-\overline{\eta})}{S^{2}}\sum_{k=1}^{m}\mu_{ik}(\eta_{k}- \overline{\eta})(i\
eq k;i,k=1,2,\cdot\cdot\cdot\cdot\cdot,m) \tag{8}\]  
where \(S^{2}=\frac{1}{m}\sum_{i=1}^{m}(\eta_{i}-\overline{\eta})(i=1,2,\cdot\cdot \cdot\cdot\cdot,m)\), and \(I_{i}\) represents the local autocorrelation index.
# An Interleaved DC/DC Converter with Soft-switching Characteristic and high Step-up Ratio
## 4 Illustrative Example and Experimental Results  
A 200 W prototype was built and tested to verify the feasibility of the proposed circuit. The circuit specification is shown in Table 1 and the values of the circuit component are shown in Table 2. The input and output voltages are 48V\({}_{\text{dc}}\) and 400V\({}_{\text{dc}}\), respectively. The switching frequency of 50 kHz is chosen. From Equation (31), the output power is inverse proportional to leakage inductance \(L_{l}\). Assuming a circuit efficiency of 95%, \(L_{l}\) can be obtained.  
\[L_{l}=\frac{\eta{V_{in}}^{2}R_{o}}{2{V_{o}}^{2}f_{s}}=\frac{0.95\times 48^{2}}{ 2\times 200\times 50\times 10^{3}}=0.11\,\text{mH}\]The control circuit is illustrated in Figure 6. It includes a half-bridge self-oscillating driver (IR2153), two inverters (74 LS04P) and two power metal-oxide-semiconductor field-effect transistor (MOSFET) gate drivers (TLP250). The IR2153 provides two complementary square voltages with dead time between them. After being inverted by 74 LS04P, the output becomes two square voltages with overlapping times. Finally, the overlapping voltages are sent to the TLP250 and their outputs are used to drive the MOSFETs.  
The waveforms of the gate voltages, \(v_{DS1}\) and \(v_{DS2}\) are shown in Figure 7. As shown, there are overlapping time between them. Figure 8 shows the current waveforms in the primary and the secondary windings of the coupled inductor. As expected, both currents decrease from the peak value to a negative value near zero. The current and voltage waveforms of the active switches are shown in Figure 9. It demonstrates that both active switches are operated at ZVS. The drain-to-source voltages of \(S_{1}\) and \(S_{2}\) are clamped at 0.7 V when the current is negative. It is believed that the negative currents flow through the intrinsic diodes of \(S_{1}\) and \(S_{2}\). The output voltage and output current are shown in Figure 10. The measured voltage and current are 400V and 0.5A, consistent with the theoretical prediction. The measured circuit efficiency is 94.3%.  
\begin{table}
\begin{tabular}{c c} \hline \hline \
\begin{tabular}{c} Input voltage \(V_{in}\) \\ Output voltage \(V_{o}\) \\ Output current \(I_{o}\) \\ Load Resistance \(R_{o}\) \\ Switching frequency \(f_{s}\) \\ \end{tabular} &
\begin{tabular}{c} 48 V\({}_{\text{dc}}\) \\ 400 V\({}_{\text{dc}}\) \\ 0.5 A \\ 800 \(\Omega\) \\ 50 kHz \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the proposed converter.  
\begin{table}
\begin{tabular}{c c} \hline \hline Leakage inductance \(L_{1}\), \(L_{2}\) & 0.11 mH \\ Mutual inductance \(L_{M}\) & 0.58 mH \\ Output capacitance \(C_{o}\) & 220 μF \\ Diodes \(D_{1}\), \(D_{2}\) & C3D10060A \\ Active switches \(S_{1}\), \(S_{2}\) & 35N65M5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Component parameters.  
Figure 6: Control circuit.  
Figure 8: Current waveforms of \(i_{L1}\) and \(i_{L2}\). (\(i_{L1}\), \(i_{L2}\): 2 A/div, time: 5 \(\upmu\)s/div)
## 6 Conclusions  
In this paper, considering the uncertain parameters and external load disturbances in the LIM, the PACFTB controller is designed for the LIM with end effects, which achieves the precise control of the LIM's velocity. Concretely, the steady state tracking precision and response speed of the system are improved and the system converges at a finite time using the proposed controller. The following conclusions can be drawn.  
1. The problem of insufficient modeling, unknown nonlinear components and uncertain parameters in the LIM is solved by the FLS combined with an adaptive law.
2. The introduction of the command filter solves the differential expansion problem in the conventional backstepping algorithm, and the inherent filter error is compensated via the proposed compensation algorithm.
3. The introduction of projection operator guarantees the boundedness of estimated parameters and FLS.
4. The simulation results and experimental results indicate that the proposed PACFTB control strategy has remarkable speed tracking performance of the LIM with end effects.  
In the future work, the sensor fault or actuator fault would be considered and the corresponding control strategy would be investigated.  
**Author Contributions:** Conceptualization, L.Z. and Y.X.; methodology, W.Z. and W.Y.; software, L.Z. and Y.X.; validation, W.Z. and D.X.; writing--original draft preparation, L.Z.; writing--review and editing, Y.X. and W.Y.; funding acquisition, W.Y. and D.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the National Natural Science Foundation of China (61973140, 61903158), Fundamental Research Funds for the Central Universities (JUSRP41911, JUSRP22030) and the National Science Foundation of Jiangsu Province of China (BK20180595).  
Figure 11: Speed tracking performance of LIM.  
Figure 12: Speed tracking error of LIM.
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 2 Materials and Methodology
### Methodology  
In this study, the finite element method, multibody dynamics, and topology optimization were carried out using ANSYS 2024 R2 and ANSYS Discovery 2024 R2 software. The TO process was conducted for various percentages on a trial-and-error basis to determine the minimum von Mises stress and the results were verified for their longevity during dynamic conditions. The whole TO process from the connecting rod, based on static structural analysis, to the validation of the topology in multibody dynamics is shown below in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Engine Type** & **Cylinder Bore** & **Stroke Length** & **Engine Speed** \\ \hline Inline 4-cylinder & 81 mm & 82.25 mm & 1000 rpm (Max) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assumed engine parameters for the initial design of the connecting rod.  
Figure 2: Virtual assembly of the crankshaft, connecting rod, piston, and cylinder liner.