esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 3 Present Technology Scenario
### Actual Sensing Devices  
In order to perform automatic steam leaks detection, there are several commercial technologies offering different sensors and wired/wireless technologies. We summarize the following options:  
* **Wireless** using conventional Wi-Fi, Bluetooth, or WirelessHart. These devices detect inlet and outlet temperatures for monitoring. They are powered by lithium batteries with an overall expected lifetime of 2 years. Due to the high battery consumption, data are only sent once per day. We can include an Emerson Rosemount acoustic transmitter (only ultrasound) [12] or Loctite Pulse (only temperature) [13] in this system, both of which are compared in Section 5.4.
* **Wired** using a conventional R5485 data communication bus. This device monitors inlet and outlet temperatures and also pressures. It needs an overall power consumption of 100 mW, and data are sent in real time with a costly four-wired infrastructure as used by Purgasa Bitherm [14].  
In contrast, this paper introduces a new approach that combines the benefits of both wireless and wired technologies. The proposed solution is autonomous, powered by the heat of the steam trap or pipeline through thermoelectric devices. It features a plug-and-play installation that is non-intrusive and maintenance-free, eliminating the need for replacing lithium batteries or their equivalents. Data are transmitted every five minutes, limited by the LoRaWAN spread factor. The system integrates three sensors: two measuring inlet and outlet temperatures, along with an ultrasound detector for enhanced performance monitoring.
# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
## 4 Experimental Results and Discussion
### Comparisons and Discussions  
In order to further validate the effectiveness of the data preprocessing method on improving accuracy, the raw cutting spindle power and vibration signals are utilized in the TCF model for comparison. The raw signals of the same each eight holes are used as the inputs of the TCM model, including the measured cutting spindle power, \(X\) cutting vibration amplitude, \(Y\) cutting vibration amplitude and \(Z\) cutting vibration amplitude signals. The outputs of the TCM model are changed to \(n\) time series values, as the inputs of the LSTM network, and the \(m\) time series tool-wear values are acquired. The ResNet-based TCM model with 20 residual blocks and the LSTM network with \(n=3\), \(m=2\) are also implemented. The mean-based data correction method is used to calculate the final outputs.  
Figure 10: Tool condition monitoring results.  
The results using raw measured signals and preprocessed data are compared, as shown in Figure 12.  
The MAE of the TCM model results using raw signals are 44.65 \(\upmu\)m, which is 2.09 times larger than that using preprocessed signals. The MAE of the LSTM network results using raw signals are 45.01 \(\upmu\)m, which is 2.20 times bigger than that using preprocessed signals. It is because there are many noises in the raw signals due to various factors, such as those of the machine center, process system and signal acquisition systems. The data preprocessing method in this paper can eliminate the noise interference and extract features more relevant to the tool condition. It improves the tool-wear-forecasting accuracy to meet the demand of online TCF for drilling CFRP/Ti stacks.
# Co-Simulation of Multiple Vehicle Routing Problem Models
## 4 Implementation and Results
### Implementing VRP Models  
In the definition and implementation of the VRP graphs using Netlogo, we use the turtles agent to represent nodes with their turtle variables. The coordinate of each client and the links agent are used to define the edge between two nodes. We use the link variables for determining for each edge the link cost of these two nodes and the value of pheromone trail.  
Table 1 represents the result obtained by executing single model VRP: model \(M_{1}\) with BKS is the best-known solutions which were given by [29], Perc-Dev is the percentage deviation of the result reached by the model \(M_{1}\) and Processing time (s) is the processing time for the execution of the model \(M_{1}\).  
Table 2 represents the result obtained by executing single model VRP, i.e., model \(M_{2}\). Both models \(M_{1}\) and \(M_{2}\) are based on an ant colony system in their implementation, and they can interchange the following data: the cost of solution is calculated by the total traveled distance, edges belonging in the best solution found and the values of pheromone trail of these edges.  
Other data can be exchanged between these models such as the route of the best solution or the route of each vehicle, etc. However, we skip these exchanged data to avoid more machine resources occupation because we must reserve more memory space to store the solution (routes) which takes a considerable time during the exchange.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inst.** & **BKS** & **Model 1** & **Per-Dev \%** & **Processing Time (s)** \\ \hline A-n32-k5 & 784 & 794 & 1.25 & 16.261 \\ A-n33-k5 & 661 & 661 & 0 & 26.46 \\ A-n33-k6 & 742 & 759 & 2.23 & 11.98 \\ A-n60-k9 & 1408 & 1442 & 2.35 & 91.18 \\ B-n78-k10 & 1266 & 1332 & 4.95 & 112.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: VRP results of \(M_{1}\).
## 5 Results and Discussion
### Hyperparameter Experiments
#### 5.4.2 Effect of Node Embedding Dimension  
The node embedding dimension is a key parameter in adaptive graph generation and affects the quality of the learned graph structure. Figure 13 shows the effect of different node embedding dimensions, and the STAGCN achieves its best performance when the node embedding dimension is set to two. A large node embedding dimension contains more information but increases the possibility of gradient vanishing or explosion, which affects the stability of training.  
Figure 12: Impact of the number of subsequences on metrics: (**a**) MAE; (**b**) RMSE.  
Figure 13: Impact of node embedding dimension on metrics: (**a**) MAE; (**b**) RMSE.
# Developing a Universal Mirror-mirror Laser Mapping System for Single Event Effect Research
## 2 Materials and Methods
### Verification of the Beam Scan Module (BSM)
#### 2.2.1 Spatial Movement Accuracy  
In traditional systems that keep the laser beam still and move the supporting stage, it just needs to record the moving distance of the stage, so that the movement of the laser beam can be determined. However, in the mirror-mirror BSM, the change of scanning location depends on the reflection angle of the mirrors. As such, it was critical to verify whether the system can precisely reflect the laser beam into the desired ROI.  
An experiment to verify the accuracy of scanning was designed using the following process. A photodiode (\(1.1\times 1.2\) mm\({}^{2}\)) was used in the testing, consisting of three parts: the circular main photodiode area (PDA), the surrounding metal area (MA) and the outside area (OA). Each of these areas has a unique waveform. All the information is shown in Figure 3.  
The yellow curve is the signal from the photodiode, while the green signal is the scanning open window. The red color in this figure and the following Figures 4, 12a,b, 15a and 17a show  
Figure 3: Waveform for each area in a photodiode.  
Figure 2: Schematic of mirror–mirror module.  
high-reflective areas by imaging laser. This effect can be alleviated by decreasing the imaging laser energy and it does not affect the testing results. As shown in Figure 3, each area of photodiode gives out different waveform in the same open window towards the same input laser beam. Then, a small ROI (\(4.3\times 4.2\) um\({}^{2}\)) was drawn at the edge of each area in order to monitor the waveform at the edge. If the waveform at the edge is consistent with the waveform in that area, it can be said that the BSM precisely injected the laser beam into the desired ROI. However, if the waveform contains the shape of the neighboring area, it means that there exists some offset in this BSM. Figure 4 shows the location of each ROI.  
The waveforms below that correspond to these four locations are shown in Figure 5.  
Figure 4: The location of each region of interest (ROI) in the photodiode.  
Figure 5: Waveform at each location in the photodiode.  
As shown in Figure 5, the waveform at each location does not contain any shape from the neighboring area. Because the ROI is drawn at the very edge of each area (see Figure 4), it can be confirmed that the BSM has the ability to project the laser beam into desired region with high spatial accuracy.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 2 Materials and Methods
### Machine Learning
#### 2.5.3 XGBoost  
XGBoost (eXtreme Gradient Boosting) is an optimized gradient-boosting decision tree algorithm that has undergone a number of improvements, including second-order Taylor expansion of loss functions, the addition of regular terms, parallel computation, and missing value processing, which improves training speed and model generalization ability [23; 24]. The XGBoost model structure is shown in Figure 1. XGBoost builds the model step by step by optimizing the objective function, which is the weighted sum of the loss function that measures the error between the predicted and true values as well as the regularization term, which controls the complexity of the model and prevents overfitting.  
The TOC content prediction model based on XGBoost can be expressed as Equation (11).  
\[y_{pre}=\sum_{k=1}^{K}f_{k}(x) \tag{11}\]  
where, \(y_{pre}\) is the predicted value of TOC content; \(x\) is the principal component of the log after processing; \(K\) is the number of trees; \(f_{k}(x)\) is the \(k\)th tree.
## 4 Results and Analysis
### Temporal and Spatial Trend Variations of WUE and Various Factors
Different land cover types in the basin were superimposed on the WUE data of the past years, and the results are shown in Figure 4. The WUE of different land cover types had a decreasing trend. Among them, the decreasing trend in WUE in shrubland was the most significant at 0.008 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), the rate of change in cropland was smaller compared with other land cover types at 0.0049 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), and the overall WUE of the basin continued to decrease at a rate of 0.0072 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). The very low WUE values for each land cover type all occurred in 2020; the peak WUE values all occurred in 2007 except for forest WUE in 2006.
## 3 Results and Discussion
### MM-GBSA Free Energy Studies
It is well known that PPAR receptors-\(\alpha\) and \(\gamma\) have hydrophobic binding pockets, and the peptides and the polyphenol-peptide conjugates have hydrophobic regions. Additionally, the polyphenic components increase H-bonding interactions due to the phenolic -OH groups. The binding energies for the polyphenol-peptide conjugates GLAMut1, RMA-Pep, RMAMut1, and GLAMut2 were found to be higher for the PPAR-\(\alpha\) compared to the ER\(\alpha\) while CSAMut4 was lower despite the fact that it contains the diterpene moiety of the carnosic acid component. Since PPAR-\(\alpha\) normally binds to fatty acid ligands, it is likely that the polyphenol groups may not interact strongly due to relatively lower hydrophobicity compared to long chain fatty acids. Nevertheless, most of the ligands showed binding  
Figure 15: Comparison of \(\Delta\)G binding interactions based on MMGBSA calculations. H-bonding, coulomb interactions, van der Waals and lipophilic contributions are shown. (**a,c,e**) show interactions involving the peptides (Pep, and mutated peptides) while (**b,d,f**) show the interactions with the polyphenol-conjugates of the peptides with ER\(\alpha\), PPAR\(\alpha\) and EGFR respectively. energies that are close to other commercially available medicinal polyphenolic compounds, as mentioned above and may be probed as drug candidates. Figure 15 further confirms that the most negative binding energies were found to be for van der Waals interactions, illustrating their role in the binding of ligands to each of the receptors with EGFR being showing the highest (most negative values) followed by ER-\(\alpha\) and PPAR-\(\alpha\). Coulombic forces also played a significant role, though their values were fairly similar for each of the receptor's ligands, with the exception of Mut1 which was more negative for EGFR. Furthermore, each of the ligands demonstrated by far the highest binding energies for EGFR compared to the ER-\(\alpha\) and PPAR-\(\alpha\). It was also demonstrated from docking studies as well as MD simulations, that these ligands attach firmly into the binding pocket of EGFR, encompassing a second sub pocket region as well [75; 76]. The highest \(\Delta\)G bind was seen for Mut1 with EGFR at \(-194.2\) kcal/mol, while the lowest value was observed for CSAMut4 at \(-146.6\) kcal/mol. These results corroborate with the MD studies as well as docking studies. The \(\Delta\)G bind values for RMAMut1 and RMA-original peptide were relatively lower at \(-49.7\) kcal/mol and at \(-63.0\) kcal/mol compared to most of the other ligands which showed \(\Delta\)G bind values \(>-150\) kcal/mol. These high average binding energies indicate the stability of the ligands within the binding pockets of EGFR. Given that their values are close to or higher than the MMGBSA value obtained for the well-known EGFR inhibitor TAK-285 [77], we can propose that all the peptides as well as GLAMut1 and GLAMut2 may be probed further for biological studies for inhibition of EGFR.
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## 2 Materials and Methods  
### High-Volume-Rate Imaging  
The imaging method used in this work is based on the transmission (TX) of unsteered plane waves from a 2-D probe and on parallel beamforming in reception (RX). Since no compounding is applied, full volumes can be reconstructed at the maximum rate (i.e., equal to the pulse repetition frequency (PRF)). Then, radiofrequency (RF) volumetric data must be clutter-filtered to remove contributions due to stable or slowly moving tissues. Specifically, hereinafter, since only vessels with rigid walls are considered, clutter removal is obtained by a high-pass filter with a cutoff frequency of 5 Hz. Furthermore, RF echoes are sampled at frequency fs equal to 39 MHz; hence, the distance between pixels in the z direction is \(\delta_{z}=c/(2f_{\rm s})=20~{}\upmu\)m. The lateral distance between pixels is the distance between beamformed lines (300 \(\upmu\)m) both along the x direction (\(\delta_{x}\)) and y direction (\(\delta_{y}\)) (see Figure 1).
###### Abstract  
The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach.  
low-energy adaptive clustering hierarchy; wireless sensor network; scalability; mean field games; energy efficiency +
Footnote †: journal:
## 1 Introduction
### Goal of the Work  
In our experimental study, volunteers performed various exercises while the optical system's capability to detect and predict joint depth was meticulously evaluated, focusing on the conditions affecting joint detection, and pinpointed exercises that introduced significant depth prediction errors. We investigated the effects of synchronization of the datasets on temporal and spatial alignment, which was essential for accurate depth coordinate prediction and joint detection assessment. We aimed to assess the impacts of markers on the accuracy of depth predictions, track concurrent movements using the optical system, identify exercises where specific joints were difficult to detect, and determine which exercises and joints exhibited the highest depth prediction errors.
## 3 Results and Discussion
### Impact of Network Configuration on Positioning Performance
#### 3.3.2 Configuration with More Seafloor Base Stations  
Generally, four seafloor base stations are used in traditional networks [3; 28], and the basic \"3+1+1\" network configuration can be expanded to the \"4 seafloor base stations + 1 subsurface buoy + 1 sea surface buoy\" (\"4+1+1\") network configuration. Figure 5 illustrates this expanded configuration. We analyzed two kinds of seafloor base station array geometries, the triangular-center-form array geometry and the square-form array geometry.  
Figure 5a,b show that, for the \"4+1+1\" network configurations, the \(\text{EPG}_{\text{ASN}\geq 3}\) values fluctuated slightly when the base station distance was less than 19 km. Compared with the \"3+1+1\" network configuration (Figure 2a), when the distance was greater than 19 km, the \(\text{EPG}_{\text{ASN}\geq 3}\) of the \"4+1+1\" network configuration (triangular-center-form array geometry) showed an obvious increase, and the percentage of \(\text{EPG}_{\text{ASN}\geq 3}\) changed from 21.62% to 46.51%. In contrast, the \(\text{EPG}_{\text{ASN}\geq 3}\) values of the \"4+1+1\" network configuration (square-form array geometry) decreased significantly, and the minimum percentage of \(\text{EPG}_{\text{ASN}\geq 3}\) was 6.72%.  
Figure 5c,d show that, when the distance was less than 13 km, the PDOP values of the triangular-center-form array geometry were slightly greater than those of the square-form array geometry. When the distance was greater than 13 km, the PDOP values of the square-form array geometry increased significantly and were greater than those of the triangular-center-form array. Moreover, compared with the \"3+1+1\" network configuration (Figure 3b), the PDOP values of the expanded network configuration (square-form array geometry) showed similar trends, but decreased slightly.  
In general, the performance of positioning range and positioning accuracy can be improved by increasing the number of seafloor base stations. The improvement of kinematic positioning performance is also closely related to the array geometry. However, more seafloor base stations always mean more costs in equipment, construction, and maintenance.  
To optimize the cost and efficiency of the UGN, the basic \"3+1+1\" network configuration is more appropriate.
## 5 Comparison of Numerical Simulation Results to Measured Data
(**a**) Comparison of vertical displacement between numerical simulation results and measured data of typical section. (**b**) Comparison of horizontal displacement between numerical simulation results and measured data of typical section. from the numerical simulation illustrate that the foundation pit excavation leads to the existing tunnel shifting toward the foundation pit. Additionally, the excavation of the new tunnel results in the horizontal displacement of the existing tunnel away from the foundation pit. The numerical simulation findings align accurately with the measured results. However, the slight difference is that the existing tunnel is subjected to multiple disturbances caused by the shield tunneling of the new tunnel, in which the displacement away from the foundation pit continues to increase. The final horizontal displacement was in line with the measured value, which was 1.03 times the final state of the measured value.
## 4 Preliminary Imaging Tests for Art Conservation  
In order to test the ability of the system to perform measurements on artworks, experiments were carried out within the framework of the bilateral pilot project THz-ARTE between ENEA-Frascati and NICT-Tokyo [36] on a sample that was already used at NICT for THz transmission imaging. The sample was a painting on wood mock-up made with natural pigments and gold and partially covered with a gypsum layer (Figure 6a). THz transmission imaging was performed at NICT at frequencies ranging from 0.6 to 2.6 THz, and the results confirmed the ability of the THz radiation to penetrate the gypsum layer and to show the underlying painting (Figure 6b). A similar image was obtained with the phase sensitive technique described above (Figure 6c) in a reflection configuration, by using THz radiation at a lower frequency (0.15 THz). It is interesting to note that the sharpness of the image was better in the area covered by gypsum. This was probably because the thickness of the gypsum layer was about one quarter of the radiation wavelength and the gypsum acted as an anti-reflection coating over the painted surface.  
By exploiting the peculiar characteristic of the FEL radiation, it was possible to overcome the diffraction limit using the technique described in paragraph 2, so as to obtain a satisfactory lateral resolution for the image: FEL radiation is linearly polarized and only the fundamental mode was excited in the waveguide probe. This made possible a sub-wavelength lateral resolution if the sample was scanned along the short dimension of the waveguide cross-section. By using this technique, a lateral resolution of about 0.2 mm was measured when a wavelength of 2.6 mm radiation was used.  
Since different materials produce different phase shifts, it is theoretically possible to distinguish the different pigments of a painting if an optimal scanning distance is chosen. The typical phase response of four different pigments (Indian yellow, giallorino, vermilion, rose lake) and of the gypsum that is used to make the preparation or ground layer is shown in Figure 7. If we consider the graph that illustrates the curves of the five materials (four pigments and gypsum), it is possible to select an optimal distance between the probe and the surface of the mock-up, where the greatest difference in response among the investigated materials is to be found. At a distance of 850 \(\upmu\)m, due to the different phase shift induced by the different materials, all pigments produce different signal intensities, thus making it possible to obtain a THz image in false colors. High-intensity signals can be associated  
Figure 6: (**a**) Visible image of the mock-up; (**b**) transmission image at 0.6–2.6 THz (yellow dotted area); (**c**) reflection image at 0.15 THz (red dashed area: \(50\times 40\) mm\({}^{2}\)).  
with giallorino and low-intensity signals are associated with rose lake, while medium-intensity signals can be associated with vermillion or Indian yellow (or gypsum). We can then distinguish three out of four pigments in a false color image in which color is associated with signal intensity.