esa-sceva/satcom-chunk-collection huggingface dataset:
# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales
## 5 Results and Analysis
### The Influence of Renewable Energy Volatility on the Proposed Energy Storage System  
In order to demonstrate the influence of renewable energy volatility on the proposed energy storage system, the red and blue solid lines shown in Figure 8 are used to represent the system average daily generation cost under different renewable energy volatility rates for the best case (i.e., the wind speed and light intensity tend to increase at most of time during the day) and the worst case (i.e., the wind speed and light intensity tend to decrease at most of the time during the day), respectively. Similarly, the red and blue dashed lines are used to represent system LOLP under different renewable energy volatility rates for the best case and the worst case, respectively.  
As shown by the red lines, for the best case, with the increase of the renewable generation volatility coefficients, the outputs of the renewable energy and the average daily generation cost will gradually increase as well. However, due to the capability limitation of the renewable generation system, most of the time, the outputs of the renewable generation system can reach saturation value, after the fluctuation rate reaches 30%. Meanwhile, the average daily generation cost is close to saturation, and its growth rate will reduce. In addition, as the renewable generation increases, the system can improve its ability to cope with the system's peak load, which eventually reduces the system's LOLP.  
Figure 7: Real-time battery output power (positive represents discharging) and regulated loads (“regulated loads” is defined as “original loads minus hybrid energy system output”).  
Similarly, as shown by the blue lines, for the worst case, with the increase of the renewable generation volatility coefficients, the outputs of renewable energy will reduce, and more electrical power will be provided by diesel generators. Since the LCOE of the diesel generators is much lower than that of the renewable energy generators, the average daily generation cost of the system will reduce significantly. It is worth noting that the average daily generation cost reduces faster after the volatility reaches 40%. This is because when the wind speed and solar irradiance are less than their threshold values, the outputs of renewable energy will reduce to zero immediately, rather than realizing a smaller value. In addition, due to the rapid reduction of renewable generation, the system's peak demands cannot be supplied by diesel generators, which results in a significant increase of system LOLP.  
Therefore, the designed capacity of the hybrid energy storage system can properly cope with climatic change. In other words, if the renewable energy volatility rate can be limited to within 10%, the stability and economy of the distributed network will not be significantly affected. However, when the renewable energy volatility rate increases to more than 20%, the optimal capacity of the energy storage system presented in this paper cannot guarantee the reliability of the off-grid system.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 3 The Compression Framework
### Reduction of the Primary Data  
Basis functions can span the spectrum from simple principal/independent component analysis (PCA/ICA) to overcomplete dictionaries. Here, we use the Tucker decomposition [34] to obtain a simple factorization. Each tensor is thereby converted into a set of coefficients, and subsequently, we use an ML autoencoder (AE) with the mean squared error (MSE) measure on the converted Tucker coefficients. The output of the decoder is projected back into the original tensor space to produce a reconstruction. The residual of the reconstruction is then analyzed using an error-bounded lossy compressor to ensure that the PD are within prespecified error bounds. In our approach, we process residuals using MGARD. Note that to get an actual codec, we have to quantize the latent space coefficients in the AE. We use PQ to obtain a codec, with the result being a set of integers for each tensor. Varying the latent space cardinality of the AE yields different compression ratios.  
Figure 2: A high-level description of the overall framework. The framework mainly consists of two parts—Primary Data (PD) reduction and Quantities of Interest (QoI) satisfaction. In the PD reduction part, we use Tucker decomposition as a preprocessing step. Then, an autoencoder (AE) compresses the Tucker basis coefficients. After training, a product quantizer (PQ) encodes the AE latent space. AET denotes the combination of Tucker decomposition, an AE, and PQ. MGARD is used for residual post-processing and it provides the guaranteed error bound on PD. AETM is AET followed by MGARD. In the QoI satisfaction step, we run constrained optimization on the reconstructed data via AETM. The purpose of this step is to directly control QoI preservation and obtain the new output, while keeping the change of the PD errors as small as possible. Another product quantizer is used to encode Lagrange multipliers obtained in this step. The overall framework is called AETMC, denoting a combination of AETM and constraint satisfaction.
## 5 Case Studies: Part II: Real-World Measured Data Analysis
### Comprehensive Performance Comparison with the Existing PQD Classification Algorithms
With the increasing deployment of power electronic devices leading to a massive growth in PQ monitoring data, the proposed scheme can \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \
\begin{tabular}{c} **Visualization** \\ **Conversion Time** \\ \end{tabular} &
\begin{tabular}{c} **Model** \\ **Classification Time** \\ \end{tabular} & **Accuracy** \\ \hline GAF + CNN [11] & 109.55 ms & 9.76 ms & 67.63\% \\ TC + ResNet18 [12] & 34.58 ms & 41.95 ms & 78.81\% \\ ITC + ResNet50 [13] & 57.78 ms & 73.05 ms & 84.54\% \\ Proposal & 12.26 ms & 11.22 ms & 91.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comprehensive performance evaluation metrics of various PQD classification algorithms. Figure 10: Comparison of accuracy and efficiency performance between the proposed algorithm and three advanced deep learning algorithms. further enhance PQD data processing and classification efficiency, thereby achieving real-time situational awareness of the power system's health status and providing crucial support for the subsequent timely adoption of effective solutions.
# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 4 FL Node Selection Algorithm Based on DQN
### DQN-Based Algorithm for FL Node Selection
Global Network Updates  
Upon completing a predetermined number of iterations, the parameters of the global DQN network are synchronized with the local network of each thread, ensuring consistency and updated information across all instances. The global DQN network is trained by sampling a random batch of data from the shared experience replay buffer. The training process is executed concurrently, distributing the computational load across multiple threads for increased efficiency. Every specified number of steps (circle), update the target network with the parameters of the global DQN network to ensure consistency and continued learning progress. Iterate through steps 1 to 3 until the model converges. After the global network model converges, the agent utilizes the trained model to determine suitable actions according to different environmental states. It then selects an optimal set of nodes to participate in the aggregation process of federated learning. Algorithm 1 provides a detailed outline of this procedure. The computational complexity of the DQN-based node selection algorithm is primarily determined by the size of the DQN network, the number of episodes and sub-episodes, and the complexity of the reward calculation and Q-value calculation steps. ```
Input: FL Task Information Q network initial state Output: Node selection scheme Initialize network, edge device and task information, along with system state and experience replay buffers. forepisode\(\in\{1,\cdots,EP\}\)do forsub_episode\(\in\{1,\cdots EP_{s}\}\)do  Each agent executes node selection action \(\alpha_{i,t}\) according to global DQN policy \(\alpha_{i,t}=\pi(s_{t})\)  Each agent calculates the reward \(r_{i}\) and the next state \(S_{t+1}\) according to expression (8) and stores the result as a new tuple Update current network and device status information  end for  Each agent uploads the collected data to the global network service in synchronization  Calculate the Q-estimate of the current state and the maximum Q-estimate of the next state  Calculate the target Q value according to expression (14)  Update network parameters using mean square error (MSE) loss function end
```  
**Algorithm 1**DQN-based node selection algorithm
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 6 Conclusions  
A low-loss active compensator based on dual unit PCC VFFCs has been proposed to counteract the effects of grid impedance on the inverter current control performance. The grid impedance compensator has been proven to feature  
1. Extremely low power loss;
2. Requiring no additional passive LC filtering components;
3. Strong harmonic rejection capability against grid disturbance; and
4. Eliminating the negative effects of grid impedance on the inverter current control performance.  
Both simulation results and experimental verifications are provided to validate the feasibility of the compensator. It reveals that the inverter stability and injected power quality even under different grid conditions have been enhanced compared with traditional capacitor current-based active damping scheme with unit PCC VFFC. The digitally controlled inverter with the active compensator counteracts the effect of equivalent grid impedance. Although the proposed concept introduces additional hardware and losses, it is thought that the benefits may justify the extra costs in the applications of distributed power generation systems with LCL filter-based grid-connected inverters.  
Conceptualization, Y.H.; methodology, Y.H. and L.H.; software, Y.P.; validation, Y.P.; formal analysis, Y.H. and Y.P.; investigation, Y.H.; resources, L.H.; writing--original draft preparation, Y.P.; writing--review and editing, Y.H., Y.P., and L.H.; supervision, Y.H. and L.H.; project administration, Y.H. and L.H.  
This research was funded by the National Natural Science Foundation of China, grant number 51707051 and 51777049, and the Zhejiang Provincial Natural Science Foundation of China, grant number LY20E070003.  
The paper was supported by the renewable energy and microgrid technology laboratory center, and Zhejiang Provincial Engineering Laboratory (Engineering Research Center)-Regional Energy Internet Technology.  
The authors declare no conflicts of interest.  
Figure 26: Power loss comparison between the proposed LV-VSC and the one in [34].
## 2 Dynamics Model
### Model Validation  
The axlebox is in direct contact with the wheelset, and the characteristics of high frequency vibration between wheel and rail are reflected in it. The axlebox acceleration of a high-speed EMU was measured from field test at 155.9 km/h. The validation of the vehicle model established in Section 2.1 is verified by comparing the simulated axlebox acceleration with the measurement. Case 1 adopts the ballastless track spectrum of the China high-speed railway. Case 2 includes the track spectrum in Case 1, and the short-wavelength track irregularities in which the wave length range is 0.01-1 m. The comparison between the simulated and measured data is shown in Figure 2.  
Figure 2 shows that the amplitude of the axlebox acceleration is small in Case 1, which is far different from the measurement. This is because the track irregularities in Case 1 is the long wave in the range of 1-120 mm, which cannot effectively reflect the actual operating condition. In Case 2, the simulated axlebox acceleration is in good agreement with the measurement, the high frequency excitation caused by the short--wavelength irregularities is directly transmitted from the wheelset to the axlebox. However, due to the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{Wheeelset} & Mass (kg) & 1627 \\  & Moments of pitch inertia (kg m\({}^{2}\)) & 825 \\  & Moments of roll inertia (kg m\({}^{2}\)) & 132 \\  & Moments of yaw inertia (kg m\({}^{2}\)) & 830 \\  & Nominal rolling radius (m) & 0.46 \\  & Wheel head & S1002G \\ \hline \multirow{6}{*}{Axlebox} & Mass (kg) & 66.7 \\  & Moments of pitch inertia (kg m\({}^{2}\)) & 0.3 \\  & Moments of roll inertia (kg m\({}^{2}\)) & 2 \\  & Moments of yaw inertia (kg m\({}^{2}\)) & 2 \\ \hline \multirow{3}{*}{Bogie} & Mass (kg) & 2056 \\  & Moments of pitch inertia (kg m\({}^{2}\)) & 1390 \\  & Moments of roll inertia (kg m\({}^{2}\)) & 2590 \\  & Moments of yaw inertia (kg m\({}^{2}\)) & 3800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of vehicle dynamic model.  
Figure 2: Simulation and verification of the axlebox acceleration.  
track, irregularities cannot completely replace the real line operating conditions and the fastener is simplified into linear force elements, therefore, there are still some differences between the simulated and measured data. It is worth noting that the measured data has the multiple frequency peak with the equal space of 30 Hz, which is caused by 2 nd--order wheel polygon (\(f_{0}=nv/(2\pi R)=2\times 155.9/3.6/(2\pi\times 0.46)=30\)). Therefore, the track irregularities in Case 2 is used in the simulation. The simulation result is in agreement with the measurement. This ensures the accuracy of the established vehicle model.
# Shell Waste Management and Utilization: Mitigating Organic Pollution and Enhancing Sustainability
## 3 Environmental Applications
### Ocean Deacidification  
Acidification is not only related to WW and contaminated soils, it is a major problem in our seas and oceans, endangering health and the development of aquatic biota. Compared to open oceans, seas in populated coastal regions are more prone to ocean acidification (OA) due to hydrodynamic processes and human activities, often leading to rapid and extreme pH decreases [58] (Figure 3).  
Figure 3: Schematic diagram of the sources of ocean acidification. Coastal land is exposed to a variety of acidic sources, adding to the coastal sea, and varying across time and space, as adapted from [PERSON] and [PERSON] [59].  
Ocean acidification is considered detrimental to marine calcifier organisms, particularly coccolithophores, calcifying algae, and corals [60]. They appear to be more sensitive at the larval stage than at the adult stage, OA affecting both natural and farmed organisms. Shellfish production may be negatively impacted by increasing the seawater \(\text{PCO}_{2}\)[61]. Many actions have been proposed to increase seawater alkalinity, such as the restoration of macroalgae and seagrass beds, which remove \(\text{CO}_{2}\) form seawater during photosynthesis [62]. One of such actions is aimed at the utilization of waste shells as a useful material, which can provide a variety of ecosystem benefits. The placement of shell reefs adjacent to shellfish farms is a way for buffering the decreases of seawater carbonate due to OA [61]. On the North America's Atlantic coast, the returning of crushed waste shells to mudflat sediment elevated the pH and carbonate geochemical conditions within sediment pore water, and increased clam settlement and survival in comparison to unbuffered sediments [59]. Waste shells deposited in the mussel banks not only control the benthic acidity, but also aid to avoid the speciation of heavy metals, such as Cd associated with sulfur at low pH, which prevents mineralization leading to the dissolution and migration of Cd into the water column [63]. Shells can be used to mitigate the effects of organic pollution on marine sediments, particularly in defaunted sites, as they reduce the accumulation of by-products from anaerobic metabolic pathways, decrease the release rate of ammonium to the water column and prevent the negative ecological consequences of eutrophication [64]. Waste shells in the ocean also act as settlement substrates for free-living shellfish larvae and as shelter for other aquatic organisms [65]. They can thus serve as reef restoration substrates (Figure 4) and promote biodiversity through complex habitat formation [5]. However, in spite of the agreement for improved and coordinated governance regarding OA, consistent OA policies are lacking [66].
## 3 Results and Discussion
### Optimized AChE Assay Application in Lettuce and Strawberry
#### 3.2.2 TU Approach in Lettuce and Strawberry Extracts  
The co-occurrence of multiple pesticide residues in a single sample is not unusual indicating the need to evaluate their potential cumulative effect on the AChE assay. Actually, this topic has drawn EFSA interest, which recently published an opinion on cumulative risk assessment to pesticides affecting the nervous system [42]. In this way, mixtures containing two analytes (carbofuran, carbofuran-3-hydroxy or dichlorvos) were prepared in lettuce and strawberry blank extracts. The TU approach was applied and antagonism was noticed for all the binary pesticide mixtures in both matrices (Table 4). Important to notice is that in previous studies the cumulative inhibitory effect was evaluated only in buffers [29, 43]. The presence of food matrix components in extracts may alternate the results of TU approach as synergistic effect was reported for a carbofuran-malathion mixture whilst mild antagonism for a carbofuran-chlorpyrifos mixture [29]. The cumulative effect may also vary in vivo, for example additive or synergistic effect was reported for salmon exposed to binary CM and/or OP mixtures [44]. In any case, implications related to pesticide cocktail effects should be considered carefully [45].  
Figure 2: Extracted ion chromatograms (XIC) of carbofuran, carbofuran-3-hydroxy and dichlorvos at 0.020 mg Kg\({}^{-1}\) in (**a**) lettuce and (**b**) strawberry matrix matched solutions using the LC–MS/MS system.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{3}{c}{**Matrix**} & \multicolumn{3}{c}{**Lettlec**} & \multicolumn{3}{c}{**Strawberry**} \\ \cline{3-8}  & **Analytic** & \({}^{1}\)**IC\({}_{\text{0g}}\) (mg Kg-1)** & \({}^{1}\)**IC\({}_{\text{0g}}\) mix (mg Kg-1)** & \({}^{1}\)**IC\({}_{\text{0g}}\) (mg Kg-1)** & \({}^{1}\)**IC\({}_{\text{0g}}\) mix (mg Kg-1)** & **TU** \\ \hline \multirow{2}{*}{carbofuran +} & carbufofuran & 0.055 (0.038-0.078) & 0.046 (0.014-0.14) & 1.70 (antagonism) & 0.050 (0.032-0.065) & 0.043 (0.022-0.098) & 1.74 (antagonism) \\  & carbufofuran-3-hydroxy & 0.78 (0.41-1.5) & 0.68 (0.21-2.1) & 0.85 (0.055-1.8) & 0.75 (0.32-1.7) & \\ \hline \multirow{2}{*}{Carbofuran +} & carbufofuran & 0.055 (0.038-0.078) & 0.041 (0.021-0.080) & 1.50 (antagonism) & 0.050 (0.032-0.065) & 0.037 (0.018-0.005) & 1.52 (antagonism) \\  & dichloros & 2.8 (0.22-3.5) & 2.1 (1.0-4.0) & 3.2 (1.8-3.0) & 2.5 (1.2-4.7) & 1.52 (antagonism) \\ \hline \multirow{2}{*}{carbofuran-3-hydroxy +} & carbufofuran-3-hydroxy & 0.78 (0.41-1.5) & 1.0 (0.68–2.1) & 2.5 (antagonism) & 0.85 (0.55-1.8) & 0.95 (0.75-3.0) & 2.14 (antagonism) \\  & dichloros & 2.8 (2.2–3.5) & 3.8 (2.9–4.8) & 3.2 (1.8-3.0) & 3.3 (2.5-5.2) & 3.14 (antagonism) \\ \hline \hline \end{tabular}  
* 1.9% confidence intervals in brackets  
\end{table}
Table 4: \(\text{IC}_{\text{0g}}\) for the tested binary mixtures in lettuce and strawberry extracts. TU were calculated as explained in Section 2.7. TU \(<\) 1, TU \(>\) 1 shows synergism, dose addition and antagonism, respectively.
# Prediction of Manufacturing Quality of Holes Based on a BP Neural Network
## 5 Conclusions  
The BP neural network algorithm was used to predict the manufacturing quality of holes in stack materials made of T300 CFRP and 7050-T7 Al alloy superimposed in this study. An 8-14-1  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Optimization** & **Feed** & **Spiable** & **Drilling** & **CFRP** & **Al** & **CFRP** & **Al** & **Cushion** & **Prediction** \\
**No.** & **Rate** & **Speed** & **Diameter** & **(3 mm)** & **(2 mm)** & **(3 mm)** & **(2 mm)** & **Plate** & **Number** \\  & **(mm/s)** & **(_t/min_)** & **(mm)** & **(Y1, N0)** & **(Y1, N0)** & **(Y1, N0)** & **(Y1, N0)** & **(Y1, N0)** & **(Y1, N0)** & **of Defects** \\ \hline P1001 & 0.007 & 5500 & 6.35 & \(1\) & \(1\) & \(1\) & \(0\) & \(0\) & \(0\) & \(0\) \\ P1002 & 0.02 & 3300 & 4.83 & \(0\) & \(1\) & \(1\) & \(0\) & \(1\) & \(0\) \\ P1003 & 0.04 & 7000 & 3.26 & \(0\) & \(1\) & \(1\) & \(1\) & \(0\) & \(0\) \\ P1004 & 0.007 & 7000 & 7.94 & \(1\) & \(1\) & \(1\) & \(0\) & \(1\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Some applied optimization parameters.  
three-layer topology model was designed, and a prediction model was established according to different hole-making processes in the four types of stack material by using the same drilling tool. Then, the training results were compared with the experimental data. Finally, the prediction model was verified by theoretical analysis and experimental results. The main conclusions are summarized as follows:  
1. The octahedral composite drill and ABS plastic cushion plate with 3 mm thickness were firstly chosen to manufacture all holes. The parameters of input layer were the feed rate, spindle speed, drilling diameter, and cushion plate, with CFRP/Al, Al/CFRP, Al/CFRP/Al, and CFRP/Al/CFRP composites. The output layer parameter was the number of defective holes.
2. According to the BP neural network prediction model with 8-14-1 three-layer topology, which underwent error correction of 170 steps, the error was reduced to 0.00016882, the regression fit was 0.99978, and the magnitude of training sample fitting error was about \(10^{-2}\)-\(10^{-5}\).
3. Based on the BP neural network prediction model, the optimized processes of hole-making were obtained. The qualified rate of manufactured holes (\(\Phi\)3-\(\Phi\)8 mm) for stack materials composed of T300 CFRP (thickness 3 mm) and 7050-T7 (thickness 2 mm) Al alloy reached 97%.  
Overall program guidance, W.L.; program implementation and analysis, A.J.; experimental research and testing, G.Z. and B.L. All authors read and agree to the published version of the manuscript.  
The paper was supported by \"High-end CNC machine tools and basic manufacturing equipment\" (2016 ZX04002005) of National Major Science and Technology Projects, China, by the Natural Science Foundation of Liaoning Province (2019-ZD-0029), and by the University of Science and Technology Liaoning Talent Project Grants (601011507-32).  
The authors declare no conflicts of interest.