esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Imaging of Bragg-Dip Profiles and Evaluation of Detailed Inner-Grain Information
### Imaging of Integrated Intensity of Bragg-Dip
#### 4.4.2 Extinction and Mosaicity Evaluation from the Integrated Intensity of Bragg-Dip  
In this section, the mosaicity information in the crystalline grain, obtained by analyzing the integrated intensity of Bragg-dip, is explained. Figure 8a,b,d show that the integrated intensity of Bragg-dip was larger at Mini Region A. The increase in the integrated intensity of Bragg-dip indicates the increase in the crystal thickness or the reduction in the extinction effect. However, in this case, the sample thickness was constant at 5 mm. Hence, we deduced that the increase in the integrated intensity of Bragg-dip at Mini Region A meant the reduction in the extinction effect. The reduction in the extinction effect indicates that the crystallite size was smaller. Because the neutron transmission path length in the sample was constant at 5 mm, the small crystallite size means that multiple crystallites (mosaic blocks) exist along the neutron transmission path. This is consistent with the consideration of the existence of another mosaic block at Mini Region A, described in Section 4.3.1. In other words, imaging of the integrated intensity of Bragg-dip also indicates the existence of another mosaic block at Mini Region A, as well as imaging the width of Bragg-dip.
## 4 Experimental Study
### Results and Analyses  
In this section, we will conduct clustering of the simulated datasets with different degrees and types of noise under the condition of the same pixel loss. In particular, we fixed the number of defects at 10, which was to simulate the chip with a small number of defects in the general scenario, as too many or too little would appear impractical. Table 1 lists the set-ups for the objective datasets and the indexes of those set-ups. Table 2 presents the clustering results with the five performance indicators mentioned above. The results in the table clearly show that HFLRSC not only achieved the best results in all datasets but also achieved 100% excellent performance in five indicators in half of the datasets. With the increase in noise intensity, the performance of the general methods would fall strongly. Specifically, in the process of Gaussian noise rising from 0.1 to 0.6, the accuracy of NCUT and K-means decreased significantly by 64.87% and 53.48%, respectively. The accuracy of HFLRSC was very stable, and even the lowest could reach a 92.62% result, which shows that our method has a very obvious effect on noise suppression and can effectively extract valuable information from low-quality IC images. In order to make this conclusion more intuitive, we drew a broken line graph as shown in Figure 3, in which the accuracy of each algorithm changes with the transformation of Gaussian noise. \begin{table}
\begin{tabular}{c c c} \hline
**Index** & **Noise** & **Defects** \\ \hline
1 & Gaussian 0.1 & 10 \\
2 & Gaussian 0.2 & 10 \\
3 & Gaussian 0.4 & 10 \\
4 & Gaussian 0.6 & 10 \\
5 & Salt-and-Pepper 0.1 & 10 \\
6 & Salt-and-Pepper 0.2 & 10 \\
7 & Salt-and-Pepper 0.4 & 10 \\
8 & Salt-and-Pepper 0.6 & 10 \\
9 & Multiplicative 0.01 & 10 \\
10 & Multiplicative 0.1 & 10 \\
11 & Multiplicative 0.15 & 10 \\
12 & Multiplicative 0.2 & 10 \\ \hline \end{tabular}
\end{table} 
Table 1: Simulated datasets with different settings.
###### Abstract  
The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery's SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.  
Keywords: lithium-ion battery; terminal voltage drop; state of health; support vector machine; capacity fading
## 7 Conclusions  
This study presents a fully automated crop yield forecasting system operating at the administrative-unit scale based on predictors originating from open-access Sentinel-3 satellite data and ERA-5 climate reanalysis. The system gathers satellite and agro-meteorological products, performs preprocessing, calculates yield predictors, transforms them to thermal time (as a proxy for crop development stage), predicts crop yields, and generates the final outputs (tabular, graphical). The prediction module runs an exTreme Gradient Boosting regressor, which is preceded by the iterative predictor selection procedure. The system was intensively validated at NUTS-2 and LAU units in Poland for winter wheat, winter rapeseed, and maize using a leave-one-year-out procedure. The analyzed period covered 2000-2019, however the availability of the reference data differed among administrative units. The performance (relative RMSE) of the end-of-season forecasts for NUTS-2 was 8.15% for winter wheat, and around 13% for maize as well as for winter rapeseed. The system was designed in a way that it can be easily applied to other regions, where reference yields statistics are available, and can also be easily migrated to cloud computing environments (e.g., DIAS or Amazon AWS), where the Copernicus data sets are directly accessible.  
Conceptualization, J.S.B., S.S., J.P.M.; methodology, J.S.B., S.S., J.P.M.; software, J.S.B., S.S., J.P.M.; validation, J.S.B., S.S., P.S.; formal analysis, J.S.B., S.S.; investigation, J.S.B., S.S.; data curation, J.S.B., S.S., J.P.M.; writing--original draft preparation, J.S.B.; writing--review and editing, J.S.B., S.S., J.P.M., E.W., K.D.-Z., P.S., T.M., A.L.; visualization, J.S.B., S.S.; project administration, J.S.B., K.D.-Z., T.M., A.L.; funding acquisition, J.S.B., E.W., K.D.-Z., T.M., A.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Centre for Research and Development-Poland under grant agreement no. GOSPOSTRATEG1/381705/13/NCBR/2018, and by the European Space Agency under contract no. 4000123852/18/NL/CBi.  
Not applicable.  
The authors declare no conflict of interest.
# Comparison of Cooling Different Parts in a High Pressure Ratio Centrifugal Compressor
## 2 Numerical Approach
### Boundary Conditions  
The inlet boundary condition was a total pressure of 100 kPa, a total temperature of 298.15 K, a turbulence intensity of 5% and axial direction for the flow. The outlet boundary conditions first used the same total pressure as the inlet for the choked condition. Then, the mass flow rate at the outlet was reduced to find other operating points. No-slip wall boundary conditions were applied to all of the walls, and the walls are assumed to be smooth. The steady calculations used in this study and the unsteady phenomenon of the flow has been neglected. The steady calculation is very robust for high speed centrifugal compressor, and based on Figure 5, it still has a good prediction of the real compressor.  
In this study, a mixing plane interface was used between the rotating impeller and the stationary diffuser to enable steady calculations. The mixing plane interface transfers the circumferential average of the flow quantity in each span to the other region.  
Compressor solid parts have a high thermal conductivity. Furthermore, the high rotational speed of the impeller produces a high convection coefficient. The radiational heat transfer then can be neglected compared with the high conduction and convection in the compressor [17; 18].  
The centrifugal compressor performance was evaluated for different cooling powers. The boundary condition applied to cooling walls was constant heat flux, which has been calculated from the equation below:  
Figure 4: Polytropic compressor efficiency for the different grid sizes.  
Figure 3: Compressor grid used in the simulation.  
\[q^{\prime\prime}=q_{i}/A_{i} \tag{1}\]  
In this equation, \(q^{\prime\prime}\) is the heat flux, and \(q_{i}\) is the cooling power on different walls (i.e., the diffuser, back plate and shroud). \(A\) is the surface corresponding to each cooling wall.
## 1 Introduction
However, as the current study shows, this is not a significant disadvantage because trustworthy modeling techniques may be used to acquire data [24; 25]. In much prior research, the ANN has been used to replace the predictive controller, which considers the converter as a black box. The main disadvantage of this strategy is that it is limited in terms of flexibility because of the inability to add further constraints to the objective function once the ANN network has been established [26; 27]. This leads to the loss of one of the characteristics of the MPC, which is the potential to deal with several constraints, within a single control law. On the other hand, other studies have employed the ANN to fine-tune several targets in order to obtain reduced THD [28]. The objective of this paper is to apply ANN to optimally define the duty-cycle and thus the dwell time associated with the M\({}^{2}\)PC method in an accurate way. The technique can reserve the converter operation at a fixed-switching frequency, while the THD improves greatly, and the M\({}^{2}\)PC's properties are preserved. The conventional M\({}^{2}\)PC for the 2L-VSI will be discussed in Section 2, while the proposed ANN-M\({}^{2}\)PC will be discussed in Section 3. The simulation results and discussion are given in Section 4, while the conclusion is reported in Section 5.
## 4 Experiments
### Results  
The proposed 1D-CNN-LSTM model showed tremendous improvements in terms of the used state-of-the-art metrics relative to the SVR, GRU, LSTM, and CNN-GRU models. Table 2 confirms the awe-inspiring performance of the proposed model based on the MAPE, RMSE, MAE, and \(R^{2}\) score. These values were obtained using the traffic flow dataset and based on the parameters described in Section 4.4 for prediction over a one-week time horizon.  
Figure 6: Training loss comparison among the different models under study.  
Figure 7: Validation loss comparison among the different models under study.  
Relative to the other baselines, the MAPE of 12.34% achieved by the proposed model is 0.6-20.1% less. On top of that, the proposed model exhibited 1.91-50.6% and 3.02-58.7% performance improvement in terms of RMSE and MAE, respectively, relative to the baselines under consideration. It also obtained an \(R^{2}\) score of 0.873, which indicates that the model correctly predicted 87.3% of the unseen, i.e., missing, data samples.  
Figures 8 and 9 allow visual comparison of the prediction performance of the models under study over 24 h and weekly horizons, respectively, for the traffic speed dataset. It is clear from the plots that the proposed model predicted traffic speed values more accurately than the other baselines. Another critical finding to notice from the included plots is the ability of the proposed model to effectively learn long-range data dependencies. Even for times periods greater than a week, the model can correctly predict traffic speed values.  
Table 3 shows the performance of the proposed model on the traffic speed dataset. The logarithmic hyperbolic cosine (LogCosh) loss function provided these outcomes. Similar to the traffic flow dataset, these values were obtained based on the employed hyperparameters described in Section 4.4 on a prediction over a one-week time horizon. The proposed model outperformed the other baselines under consideration in terms of all the used evaluation metrics. It exhibited MAPE, RMSE, MAE, and \(R^{2}\) score improvements of 12.1-21.83%, 7.6-96.6%, 25.7-82.6%, and 1.64-14.1%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **SVR** & **LSTM** & **GRU** & **CNN–GRU** & **CNN–LSTM** \\ \hline MAPE & 15.46\% & 12.45\% & 12.41\% & 14.45\% & 12.34\% * \\ RMSE & 15.7 & 7.84 & 7.89 & 7.87 & 7.66 * \\ MAE & 14.00 & 5.86 & 5.88 & 6.06 & 5.75 * \\ \(R^{2}\) & 0.863 & 0.867 & 0.865 & 0.865 & 0.873 * \\ \hline \hline \end{tabular} Here, * represents the best-found result among the models under comparison.  
\end{table}
Table 2: Performance comparison of different models under study in terms of the used state-of-the-art metrics on the traffic flow dataset.  
Figure 8: Prediction performance comparison among the different models under study over a 24 h horizon on the traffic speed dataset.
## 3 Results and Discussion
### Effect of Triton X-114 Concentration  
Triton X-114 is responsible for decreasing the cloud point temperature (CPT) below the room temperature. So, the amount of Triton X-114 not only affects the extraction efficiency but also the volume of the surfactant-rich phase. The phase volume ratio (\(V_{org}\)/\(A_{queus}\)) should be minimized to get enhanced extraction efficiency and successful CPE [30]. In this section, the concentration of TX-114 was investigated within the range of 0.08%-0.8% (_w/v_). The efficiency of extraction towards Pb\({}^{2+}\) increases as Triton X-114 concentration increases from 0.08% to 0.4% (_v/v_). Above 0.4% concentration, the recovery percentage begins to decline because of the increase in the total Pb\({}^{2+}\) volume and the viscosity of the surfactant phase. So, a concentration of 0.4% (_v/v_) was selected as an optimal concentration for Triton X-114 to get the highest recovery percentage, as shown in Figure 4.
### Our Contributions  
As discussed above, the traditional OCS cannot meet the challenges such as data rate, capacity, privacy and long distance due to the FWM effect. Therefore, to support user demands, a new set-up is required, where FWM is managed and high data rate, long range transmission, low latency and less complexity goals are provided to the users. Henceforth, to address these issues an analytical and experimental mechanism is developed in this work to maximize the system performance and handle FWM effect. The major handouts of this model are mentioned as follows.  
1. [label=()]
2. We present a new model coined as LHOCS, where the FWM are compensated and high capacity information is communicated over a 500 km distance.
3. An analytical model is presented with an improved digital signal processing (IDSP) receiver to mitigate FWM effect and support 8, 16, and 32 WDM channels.
4. We also show that transmission of 100 Gbps data rate over 32 WDM channels is achieved for 500 km length of optical fiber.
5. The duo-binary (DB) advanced modulation scheme is applied to narrow the pulse spectrum, which leads to manage FWM successfully over longer distances.
6. Through extensive simulation we demonstrate that our proposed technique work better compare to current optical communication system in terms of BER, length and capacity.  
The rest of paper is organized as follows. The network layout is studied in Section 2. Section 3 presents the theoretical background. The results and discussion is analyzed in Section 4. Finally, Section 5 concludes the paper.
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Fuel Cell Powered UAVs
#### 2.3.1 Fuel Cell Efficiency Issues  
The process of electricity generation from fuel cells involves certain elements. Fuel cells can achieve an efficiency level of up to 60% [89]. However, this efficiency is lower compared to lithium batteries, which can exceed 90%. One of the reasons for the lower efficiency of fuel cells is the presence of auxiliary equipment required for the operation of the fuel cell stack. These auxiliary components add complexity to the system and can contribute to energy losses, thus reducing overall efficiency [80]. Additionally, the onboard hydrogen generation system, which is necessary for fuel cell operation, can also introduce complexity and potentially decrease efficiency. While fuel cells may have lower efficiency compared to lithium batteries, they offer advantages in terms of specific energy and longer operational duration. While proton exchange Membrane fuel cells (PEMFCs) hold promises for enhancing the endurance of drones and hydrogen-lithium composite energy storage systems prove adaptable to specialized working conditions, certain challenges must be addressed for their large-scale commercialization. Notably, PEMFCs require higher power and current densities to meet the demands of commercial applications. However, at elevated operating current densities, issues such as the accumulation of liquid water in the Gas Diffusion Layer (GDL) can lead to flooding and impede gas diffusion, resulting in a rapid degradation of cell performance [92]. Consequently, the improvement of water management capabilities is imperative to unlock the full potential of PEMFCs and achieve better cell output performance. The choice between fuel cells and batteries depends on specific application requirements, taking into account factors such as flight endurance, weight limitations, cost considerations, and the availability of infrastructure. The cost of implementing fuel cells in drones varies based on factors such as fuel cell type, size, and specific application requirements. While fuel cells offer advantages in terms of extended endurance and reduced environmental impact, their cost is a significant consideration in  
Figure 6: The fuel cell system auxiliaries.  
the adoption of this technology. Cost reduction efforts, advancements in research and development, and economies of scale are expected to make fuel cells more cost-effective in the future. The decision to use fuel cells as the primary means of power generation and energy supply in drones will depend on the specific drone applications, regulatory support, competition with evolving battery technology, and a comprehensive evaluation of the total cost of ownership, taking into account initial purchase, maintenance, and operational costs.
# Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
## 2 Background
### Sequential Models  
Deep learning methodologies consist of representative methods for processing time-series data, namely recurrent neural network (RNN) and long short-term memory (LSTM) methodologies. They are utilized to process time-series data and improve the prediction performance by reducing the likelihood of information loss by inputting data according to the time window in neural network operations. RNN is a deep learning algorithm in which the output values are fed back as inputs resulting in a repetition of the loops. This ensures consistent use of information. RNN is a network that considers past data and is mainly used for time-correlated data provided that all inputs and outputs are independent. As shown in Figure 1, the input to the network appears as an output value. \(h_{t}\) is a hidden state of the current state and is renewed after receiving a hidden state from the previous state. This has a deep learning network structure that emphasizes the flexibility of the model to accept inputs and outputs regardless of the length of the sequence in data.  
However, the drawback of RNN is that the slope diminishes with an increase in time. This is known as the vanishing gradient problem in which various weights within a given neural network reduce the data update resulting in the discontinuation of learning in the neural network. Figure 2 depicts the gradient vanishing problem.  
LSTM is an algorithm that improves the performance of the model by replacing the hidden layer of the RNN with memory blocks; it exhibits intensified performance in long-term memory through repeated information selection and removal of hidden networks. This addresses the vanishing gradient problem caused by distance when information in RNN is updated because it has the advantage of flexibly accepting input and output values of the network regardless of the length of the sequence.  
The structure of LSTM comprises a series of \"gates\" unlike that of RNN (Figure 3). Gates are included in the memory blocks linked through each layer. They are identified as input, forget, and output gates, which can selectively allow the inputs to transfer information to output gates. The gates control the reading and writing of data, such as switches, and incorporate long-term memory into the model.  
Figure 1: Recurrent neural network.  
Figure 3: Memory block and long short-term memory structure.
# Data-Driven Analyses of Low Salinity Waterflooding in Carbonates
## 4 Conclusions  
* Different single parameters (such as salinity, contrast in salinity change, PDI concentration, oil acidity, base number of crude oil, permeability, and temperature) were individually analyzed using linear regression to study their correlation with the incremental oil recovery by LSW flooding. Negligible and weak relationships indicate that a single parameter is not sufficient to explain the performance of LSW injection.
* Among groups of parameters, a set of oil/brine parameters that include AN, alteration in salinity, SO4\({}^{2-}\) and cation concentrations, showed the best, but still weak, correlation. So, linear correlations are insufficient to forecast LSW potential.
* A nonlinear relationship between parameters and RF was observed using ML models. Among the ML models, DT produced the best correlation for brine only parameters; the correlation coefficients for training and testing data were 0.57 and 0.35, respectively. For oil/brine parameters, all models showed strong and very strong relationships. However, ANN and SVM showed unsatisfactory results for testing data due to overfitting. In contrast, less overfitting was achieved by DT, where the correlation coefficients for training and testing data were 0.68 and 0.63, respectively.
* Several mechanisms involved in the LSW process and the LSW effect cannot be explained by a single mechanism. MIE and rock dissolution are the most widely accepted mechanisms in the literature. These mechanisms result in wettability alteration in coreflooding tests in carbonates. Our studies showed that, by analyzing oil/brine parameters, a better understanding of the active mechanisms during LSW can be achieved, and it is possible to predict the mechanism by analyzing parameters such as salinity, ion concentrations, pH, and IFT.
* Future research should be further conducted to confirm these findings by increasing the data set size. In addition, with more experimental data, other parameters should be added to the model to show fluid/fluid interactions.  
Conceptualization, methodology, investigation, writing-original draft preparation, R.S.; writing-review and editing, supervision, P.P. and L.W. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge Nazarbayev University for aiding this research through the NU Faculty Development Competitive Research Grants program (Award number: 110119 FD4541).  
The authors would like to thank respectable reviewers for their valuable feedback and comments to enhance the quality of manuscript.  
The authors declare no conflict of interest.