esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
Energy conservation has emerged as a crucial undertaking in WSNs, owing to the limited-capacity batteries installed in the network nodes. Clustering is the most efficient technique for conserving energy by retrieving data from all participants and relaying them to the sink in a cluster. This paper introduced EACH-COA, an energy-aware CH selection-based COA designed to increase the longevity of WSNs and decrease their energy consumption. The proposed EACH-COA approach first performs cluster formation using Euclidean distance, and then the CH is selected by adopting the COA. The performance of the EACH-COA protocol is verified for different network sizes. Therefore, the network longevity and throughput are enhanced by 8-15% and 5-10% compared with the EECHS-ARO, EECHS-ISSADE, and PDU-SLno approaches. As a result, energy conservation is maximized.  
In future work, the performance of the proposed EACH-COA approach will be evaluated in real-time settings. A hybrid approach will be developed to select the optimal CH by reducing the convergence time in the network. To enhance the overall performance of the network, several other network parameters will be considered when determining the fitness function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**Network Longevity**} \\ \cline{2-5}  & **EECHS-ARO** & **EECHS-ISSADE** & **PDU-SLno** & **EACH-COA** \\ \hline
100 & 800 & 100 & 800 & 100 \\ \hline
200 & 850 & 200 & 850 & 200 \\ \hline
300 & 900 & 300 & 900 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Network stabilization period vs. number of nodes.  
Figure 7: Network stabilization vs. number of nodes.  
**Author Contributions:** Conceptualization, R.S.; methodology, R.S.; validation, R.S.; Y.C. and B.K.M.; formal analysis, R.S.; investigation, R.S.; writing--original draft preparation, R.S.; writing--review and editing, R.S., Y.C. and B.K.M.; supervision, Y.C.; project administration, Y.C. and B.K.M.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01489) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation). This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (RS-2023-00259703) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). This research was supported by Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), 21 Sunchon National University, 255, Jungang-ro, Suncheon-si, Jeellanam-do 57922, Republic of Korea.  
The dataset used for this work was randomly generated in MATLAB.  
The authors declare that they have no conflict of interest.
## 5 Conclusions  
The experiment presented in this article testifies that the proposed method guarantees a level of accuracy for results on a parallel level with commercial PU probes and, with some limitations, has a number of advantages. It eliminates the need for an expensive PP or PU probe. A single microphone is sufficient. Therefore, the complicated intensity calibration is eliminated. The problem of compatibility of microphone phase characteristics known from the PP probe disappears. Another advantage is that the smaller dimensions of the microphone compared to the standard PP probe result in less impact of the sensor on the object to be measured. When scanning specific planes or volumes, the proposed method guarantees a reduction in the amount of measurement data (for example, when scanning a plane, the gain over the pp probe is four times and over the PU probe three times). In addition, in order to increase the accuracy of measurement in the lower frequency range, the intensity values at a given point can be determined from the data corresponding to the use of a PP probe spacer of larger size.  
As an example application, one can point out, for example, the optimization of loudspeaker design, where vector visualization of the energy distribution can provide valuable information on the performance of the drivers themselves, the enclosure, and the crossover. Other applications include, for example, analysis of the behavior of an acoustic wave over various types of obstacles introduced in the vicinity of the sound source. As such tests have already been carried out with commercial intensity probes, performing them with the proposed method would verify its usefulness in these applications.  
Another application of the proposed method, already under development, is the measurement of the vector impulse response of a room, so that the measurement of acoustic properties of rooms can be more easily enriched with parameters that take into account the directional propagation of acoustic waves in a room. An outline of this issue was also provided in [54].  
The presented method can contribute to a greater industrial spread of research using sound intensities and make intensity methods more inclusive.  
Conceptualization, W.M. and M.R.; methodology, W.M. and M.R.; software, W.M. and M.R.; validation, W.M. and M.R.; resources, W.M. and M.R.; data curation, W.M. and M.R.; writing--original draft preparation, W.M. and M.R.; writing--review and editing, W.M. and M.R.; visualization, W.M. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Discussion
### Limitations  
This study analyzed the impact of six driving factors on WUE using the XGBoost and SHAP models, revealing the nonlinear relationships between these factors. However, there are some limitations to the research. First, the study considered a relatively small set of driving factors. Future research could incorporate additional variables, such as soil moisture and land use changes, to enhance the model's comprehensiveness. Second, the resolution of the driving factor data used in this study is relatively low. Future research should utilize higher-resolution meteorological and remote sensing data to more accurately capture changes at local scales. In addition, the analysis in this study is basedon interannual data. In the future, subdividing the time span into quarterly or monthly intervals could indeed provide a deeper insight into the seasonal variations in WUE and the dynamic changes in its driving factors. These improvements would enhance the understanding of WUE variation mechanisms and provide more precise support for ecological and environmental management.
# Forming Difficulty Evaluation for Curved Hull Plates Based on Grey Relational Analysis
## 2 Analyze the Influence Law of Forming Difficulty of Complex Curved Hull Plates
### The Influence of Plate Thickness on the Forming Difficulty of Complex Curved Hull Plates  
Except for the thickness of the curved plate, other influencing factors keep constant. The plate length is 3 m, the plate width is 2 m, the radius of curvature is 3 m, the heating line length is 0.3 m, the heating line position \(l_{p}\) is 0.5. Set 4 groups of different plate thickness, which are 0.012 m, 0.016 m, 0.02 m, 0.024 m separately, get the plate thickness on the deflection of the law as shown in Figure 7b. The thicker the plate, the smaller the change in deflection. Therefore, with other conditions keeping constant, the thicker the plate, the greater the energy required to deform the curved plate, the smaller the resulting deflection deformation, the greater the forming difficulty. The plate thickness is positively correlated with the forming difficulty.  
Figure 6: Line heating parameters of curved hull plate.  
Figure 7: Influence of different plate geometry parameters on deflection: (a) plate length; (b) plate thickness.
# Pediatric Endoscopy, Update 2020
## 6 Ercp
### Indications  
The main indication for ERCP in childhood is biliary disease which is not sufficiently explained by advanced non-invasive imaging such as MRCP. ERCP procedures in children are far less frequently performed than in adults. Malignant diseases of the biliopancreatic region are extremely rare. The main indications for ERCP in pediatrics are: Choledocholithiasis, primary sclerosing cholangitis, biliary strictures, cholangiocarcinoma diseases, leakage of the bile duct after surgery (transplantation or cholecystectomy), bile plug syndrome [34; 97; 101], and parasitosis (fascioliasis, ascariasis). Pancreatic indications are: Recurrent acute pancreatitis, chronic pancreatitis, pancreas aulware, and pancreatic duct leaks after surgery or traumatic rupture of the pancreatic duct [14; 16; 33; 34; 43; 97; 101]. Genetic (CFTR, SPINK1, PRSS1, CTRC) and obstructive risk factors are the most common causes for the development of chronic pancreatitis in children. According to the INSPPIRE study, therapeutic ERCP was performed in 67% of children with chronic pancreatitis [102]. Patients with pancreas dtivism (PD) may present with acute recurrent pancreatitis (ARP) or chronic pancreatitis (CP) [103].
## 2 Signal Processing Principles
### Pulse Frequency Slicing and Counting  
This scheme employs a 4 FSK signal as the carrier to transmit symbols with a bit width of 2, thereby sending signals 00, 01, 11, and 10. In UV communication systems, due to the discrete nature of photon signals, it is difficult to use conventional waveform detection or pulse threshold decoding. To improve the recognition and processing of weak signals, a photonic pulse counting strategy is employed in the front-end of the algorithm to ensure efficient signal recovery. A PMT + ADC sampling scheme combined with threshold optimization is used to achieve an efficient photon counting mechanism.  
The counting of photon pulses is based on the ADC sampling the voltage signal, which is binarized by setting the voltage threshold \(V_{\text{th}}\)  
\[N(T)=\sum_{t=0}^{T}I(V(t)>V_{th}) \tag{4}\]  
\(N(T)\) denotes the number of detected pulses within time interval \(T\). The voltage signal sampled by the analog-to-digital converter (ADC) is represented by \(V(t)\). The indicator function \(I(x)\) is defined such that \(I(V(t)>V_{th})=1\) if \(V(t)>V_{th}\), and 0 otherwise. A slicing approach is employed to partition the single symbol duration, improving synchronization precision from symbol-level to slice time \(T\). For signals of different frequencies, eight samples per symbol period are acquired. Given the variable frequency characteristics, the slice time \(T\) differs for each frequency component. During decoding, pulse signals are processed through four-channel frequency slicing counting, with distinct slice counts allocated to each of the four frequencies, resulting in four-channel frequency slicing count data output.
## 5 Experimental Results
### A Short Discussion  
In Section 5.1, we first verified the feasibility of the MILP model by testing a small test instance containing five pending jobs. Then, in Sections 5.2 and 5.3, we defined a series of test instances and analyzed the size complexity and computational complexity of the MILP model. Finally, in Section 5.4, the impact of some key parameter settings (i.e., the number of jobs, the number of AGVs and the parallel machine types) on the makespan was further explored, and the marginal effect between the makespan and AGV count was found. However, as the number of jobs increased, the computational complexity of the model grew exponentially, and when dealing with large-scale problems (number of jobs greater than 20), a pure MILP model may not be feasible for rendering results in a reasonable amount of time. To bridge this gap and maintain the quality of the solution, the following approaches can be considered: (1) Adopting a hybrid MILP-heuristic framework utilizing the decomposition approach (Benders' decomposition, column generation) and a hierarchical framework (decomposition of a problem into sub-problems) to deal with large-scale scheduling problems to improve the solution efficiency and the quality of the solution. This approach combines the accuracy of MILP with the flexibility of heuristics and can find a near-optimal solution in a reasonable amount of time; (2) Approximation algorithms for real-time scheduling are algorithms for solving complex scheduling problems, and common approximation algorithms include reinforcement learning (RL) agents and graph neural network (GNN) scheduling. They can significantly improve the solution efficiency while ensuring certain solution quality and are suitable for large-scale and dynamically changing scheduling scenarios.
## 3 System Description
### Reference Control  
Usually, the control system of wind turbines, as indicated in [19], consists of two sub-controllers: a generator torque controller and a controller for the pitch of all the blades based on traditional control techniques. The simulation model also has a control system based on those two subsystems. Since the first one acts when the generator speed is below the nominal value and the second one acts when it is above, those two controllers are designed to work practically in an independent yet complementary manner. Thus, the objective of the first one is to capture as much wind power as possible using an MPPT algorithm, implemented by a torque control law (Figure 1), while the second one regulates the generator speed when the operation is above the nominal generator speed with changes in the collective pitch angles, which often happens when the wind speed is higher than its nominal value.  
Figure 1: Theoretical law of torque control based on the MPPT algorithm. A constant wind speed with the value of the nominal wind speed is considered.  
It is in the second control system, the traditional pitch controller (Figure 2), that there is room for improving the system performance with the integration of intelligent control techniques, e.g., fuzzy logic control. It is worth noting that the reference control system is based on a Proportional-Integral controller, developed by the NREL, that has dynamic gains that depend on the actual value of the pitch blades. This architecture is usually known as \"gain scheduling\" and it is based on expert knowledge of the NREL.  
Nevertheless, it is mandatory to develop, in advance, a simulation tool and comparison indexes to set a framework that allows for controller comparison, testing, and improvement using common, yet representative, performance metrics.
# Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material
## 1 Introduction  
In order to reduce the cost of cement in concrete and to manufacture special cement for various special purposes, many worldwide scholars [1; 2; 3; 4; 5] have conducted extensive work on cement substitute materials and concrete additives. Recently, limestone cement has become a recent subject of research, owing to the promising properties of limestone for cost saving and cementing [6; 7]. Siliceous limestone powder waste has cementitious activity as it can be reused to replace a part of the cement, leading to an improvement in the properties of concrete and thereby reducing contamination in soil as well as the air pollution [8; 9]. Cemented paste backfill (CPB) is an emerging material, which is similar to concrete, that is increasingly being used in mining industries. [PERSON] et al. [10] assessed stone powder as a replacement of cement in CPB, and they studied strength characteristics and reaction mechanism. The results showed that strength of the backfill was greatly reduced at an early stage, and was slightly reduced in the final stages. The effects of adding waste limestone powder into solid waste CPB material still need to be studied.  
As a relatively new mine waste management technology, CPB has been extensively applied in underground mine operations around the world because of its significant environmental, technical, and economic benefits [11; 12; 13; 14]. Besides, as the mining industry moves deeper into the ground, thecost of CPB has increased because of unfavorable conditions such as increased pressure in the stope and high environmental standards [15]. Currently, CPB accounts for about 40% or more cement content, although it has only 3-9 wt % of CPB [16]. Therefore, there is urgent need to find another cheap cementation material to replace part of the cement that can reduce the cost and minimize the environmental problems caused by solid waste.  
Effects of pore structure on the strength and various properties of CPB are important to know [17]. In fact, researchers need to focus attention on how the pore structure changes in limestone powder. In general, total porosity (n) and pore size distribution (PSD) are the main parameters that are related to the pore structure and can be quantified by using various approaches [18; 19; 20]. Among them, scanning electron microscopy (SEM) combined with image analysis (IA) technology is one of the most common methods, which can be directly obtained from the characteristic parameters of pores. Simultaneously, analysis of nuclear magnetic resonance (NMR) spectra can be proposed for identifying material porosity, pore geometry, connectivity, and to characterize PSD material.  
The present study focuses on the performance of solid waste cemented paste backfill (SWCPB) containing limestone powder as an additive (0-20%) to cement and tailings as aggregates in SWCPB. SEM-IA and NMR are used to determine relevant pore structure parameters to assess mechanical and hydraulic behaviors. Furthermore, the feasibility of reusing limestone powder in CPB will be evaluated.
# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 5 Conclusions  
In this paper, the NWP and CCMP wind products are used as the ground truth wind to establish 2D GMF for spaceborne GNSS-R ocean surface wind speed remote sensing, and the buoy data are included to validate the results of the two groups' inversion results. In order to improve the accuracy  
Figure 14: Wind speed inversion accuracy from the NWP-based reference wind (**a**) and CCMP-based reference wind (**b**) corresponding to different GPS PRN satellite types.  
Figure 13: CCMP reference wind speed probability density distribution in the training samples.  
of the retrieval algorithm, new constraints are added in the process of smoothing the discrete empirical GMF to eliminate fluctuations caused by the measurement noise and insufficient training samples to ensure that the model is consistent with the trend of the actual physical process. The established 2D GMF is sensitive to the variation of DDM observables under medium-strong wind speed conditions. The results show that the inversion accuracy could reach 1.84 m/s if the surface reference wind speed is given by the NWP when the wind speed was less than 20 m/s, while the inversion accuracy of the CCMP-based retrievals was 1.68 m/s. There are no large deviations between the derived wind based on different reference wind sources, and this result further proves the reliability of GNSS-R derived wind speed. From the distribution of the winds in the training dataset, it shows that the training samples are mainly concentrated below 15 m/s, and therefore, further expanding the size of the training dataset at a high wind range can improve the accuracy of the wind speed retrieval algorithm.  
[PERSON] and [PERSON] conceived and designed the experiments and [PERSON] performed the experiments and analyzed the data. Both authors contributed to the writing of the paper.  
This work was supported by the National Key Research and Development Program of China Sub-Project (grant no. 2017 YFB0502802), the Strategic Priority Research Program Project of the Chinese Academy of Sciences (grant no. XDA23040100), the Jiangsu Province Distinguished Professor Project (grant no. R2018T20), and the Startup Foundation for Introducing Talent of NUIST (grant no. 2243141801036).  
The authors would like to thank ECMWF and GDAS for providing the reanalysis data, and the NASA CYGNSS team. The CCMP wind products were provided by the National Center for Atmospheric Research's Research Data Archive, and we also appreciate the work for this dataset.  
The authors declare no conflicts of interest.
## 4 The Line Extraction Pipeline
Color contrast: Experimental results showed that dark lines and light colored samples were high contrast samples, and they usually helped obtain better extraction results. Therefore, we needed to add samples with lower contrast and increase the weight of these samples; otherwise, it would be difficult to extract the lines we required in low contrast images. For example, lines and the color of black hair are easily mixed together. Similarly, there are cases where the dark areas are edged with light colored lines. In order to adapt to more situations, we needed to adjust the training data content and weights based on incorrect or unsatisfactory outputs, as many as possible. Training direction: If there was an error in the training direction, it was better to select the backup file and continue the training from the good model because the correction of the training direction may take a long time or even could not be corrected. Simplified network: For quick testing, we designed a simplified version of the progressive full data convolutional neural network by appropriately reducing the channel amount of convolutional layers. It could do a good job of rough line extraction, but there were many noises. For practical use, there is still no good way to simplify the standard model provided in this paper. Batch normalization: The neural network structure proposed in this paper did not use batch normalization because the convergence rate slowed down after using batch normalization, and the results after the reference were not significantly improved. Without pre-training: The model in this paper was based on new training without any pre-trained models. Even if we started training from scratch, we found that we could usually get preliminary results in a few hours. With the current computational power on a typical PC, it is usually possible to train a neural network model with the most appropriate loss and output effects in about three days. If the training data were changed and retrained, unexpected situations would often occur. Batch size: The batch training size value was fixed at 16 in our test. Too small batch training size values, such as one, could lead to problems such that the loss of model could not converge because each training direction could not be fixed. Excessive values could lead to memory overflow because our model was complex, and the progressive whole-data convolution process was also performed at the level of the original image size, so the memory consumption was very large. To sum up, we obtained a value of 16 based on trial-and-error. Loss function: We use the mean squared error (MSE) to calculate the loss of our model. The following equation expressed the loss function:  
\[\Sigma=\frac{1}{n}\sum_{i=1}^{n}(Y_{i}-\hat{Y}_{i})^{2} \tag{2}\]