esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation
### ALE Fluid-Structure Interaction Algorithm
#### 3.1.1 Lagrangian Description  
The Lagrangian Description is applicable to the stress-strain analysis of solid structures. Meshes are overlapped with material nodes, and in computation, all meshes move along with the material nodes. While the deformation of structures is consistent with the deformation of meshes, other substances will not flow among the meshes. The main contribution of the description is to accurately describe the movement at the boundary of the structures and track the interfaces between the free surface and different materials. However, the weakness of the description is the severe distortion of the meshes in the case of significant deformation of the structures, which will lead to difficulties and interruption of the numeric computation. Thus, meshes must be reorganized or hourglass control is required to address the issue and continue the computation. Regarding the transient nonlinear dynamic issue of a blast, the discussion of the damage type caused by the movement of materials and stress in the instant of a blast is recommended.  
In the numerical simulation analysis of a blast, the use of the Lagrangian Description will contribute to more accurate analysis. If the change in the shape of the analyzed object is exactly consistent with the change in the finite element mesh (element mesh nodes are material points), the materials will not flow among the elements, but they will result in a negative volume of the meshes, which is caused by severe distortion, and computation will be terminated. Computation can be continued if the element erosion condition is incorporated.
## 6 Real Jamming Results Analysis
### Low Jamming Scenario  
Firstly, the performance of the GNSS receiver was assessed under low actual jamming conditions for both the first and second trajectory.
# Hydrogen-Rock Interactions in Carbonate and Siliceous Reservoirs: A Petrophysical Perspective
## 3 Results and Discussion
### Porosity, Permeability, and Textural Characteristics
The stable pore population within the 3-10 nm range confirms the absence of collapse, obstruction, or secondary-phase precipitation, in contrast to the behavior observed in dolomite and calcite. Quartz, as a silica-dominated mineral phase, demonstrates outstanding stability under high-pressure hydrogen conditions, maintaining both its structural and petrophysical integrity. Unlike dolomite, which undergoes pronounced pore enlargement, or calcite, which shows susceptibility to partial network collapse, quartz exhibits negligible microstructural degradation, thereby reinforcing its candidacy as a robust matrix component in subsurface hydrogen storage environments [15; 20; 37; 39]. This stability is quantitatively reflected in the minimal variations recorded across key textural descriptors, the BET surface area decreased only slightly (from 19.048 to 18.87 m\({}^{2}\)/g), total pore volume showed a marginal reduction (0.0291 to 0.0284 cm\({}^{3}\)/g), and the average pore diameter increased modestly (3.107 to 3.305 nm). Additionally, total porosity declined from 24.5% to 24.13%, and permeability from 581.96 to 567.43 mD. These minor shifts, detailed in Table 4, underscore the resilience of the quartz framework and further validate its functional neutrality in the face of hydrogen-induced chemical and physical stress.
# Development and Application of Unesaled borehole Leakage Detection Device Based on Flow Method
## 2 Research and Development of Unsealed Borehole Leakage Detection Technology Based on Flow Method
### Selection of the Main Components of the Air Leakage Detection Device  
The air leakage detection device developed is mainly to determine the leakage position around the borehole by detecting the difference of extraction flow before and after the leakage position. This section focuses on the selection process of the main components of the device on the ground, including the selection of expandable capsules, pressure limiting valve, and flow gauge.
## 4 Conclusions  
This study systematically reveals the synergistic control mechanisms of linearly polarized laser intensity (\(\mathbf{a_{0}}\)) and beam waist radius (\(\mathbf{\omega_{0}}\)) on radiation power, electron dynamics, and temporal spectra in nonlinear Thomson scattering. The sensitivity of radiation power symmetry to \(\mathbf{\omega_{0}}\) increases significantly with its increase: under a fixed \(\mathbf{a_{0}}\), increasing \(\mathbf{\omega_{0}}\) not only causes the radiation power to approximately follow a quadratic growth but also transforms the angular distribution from a saddle-shaped valley structure to a dual-peak distribution with superior collimation. Further analysis of electron trajectories demonstrates that highly symmetric \"zigzag\" motion induces periodic energy oscillations due to the dynamic balance between forward/backward accelerations. Additionally, optimizing \(\mathbf{\omega_{0}}\) effectively suppresses secondary peaks in temporal spectra, evolving the main peak profile from an asymmetric tailed shape to a quasi-Gaussian structure, offering new strategies for ultrashort pulse shaping and noise suppression. These findings establish quantitative mappings between laser spatiotemporal parameters and radiation properties, providing theoretical guidance for designing high-brightness X-ray sources through parameter synergy.  
**Author Contributions:** Conceptualization, J.X. and Y.T.; Methodology, J.X. and J.S.; Validation, J.X.; Formal analysis, J.X. and J.S.; Investigation, J.X., J.S., Y.C. and Y.Z.; Resources, Y.T.; Data curation, J.X. and J.S.; Writing--original draft, J.X.; Writing--review & editing, J.X., Y.Z., J.S. and Y.T.; Visualization, J.X.; Project administration, Y.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 2 Wind Turbine Parameter Description and Feature Selection  
### Description of the Wind Turbine Parameters  
This study selected a 2 MW wind turbine located on a wind farm in Dabancheng, Xinjiang province from 1 January to 31 December 2020. The rated power of the wind turbine is 2 MW. Its cut-in wind speed is set at 4 m/s, and the cut-out wind speed is 25 m/s, while the rated wind speed is 15 m/s. The SCADA system records the operating parameters of the wind turbine every 10 min, including but not limited to key indicators such as wind speed, generating power, rotation speed, and temperature. At the same time, the system will also record the start of wind turbines, shutdown and safety chain disconnection, and other key state information. These exhaustive data are crucial to assess and analyze the performance of wind turbines. In view of the environmental and structural uncertainties, the parameter values of wind turbines are usually set to a wide range of changes. To improve the accuracy of the model, the necessary preprocessing of the data recorded by the SCADA system is an indispensable step. The following are the main methods for data preprocessing.
# Advances, Progress, and Future Directions of Renewable Wind Energy in Brazil (2000-2025-2050)
## 3 Wind Farm Projects: Experiences in Brazil
### Onshore Wind Energy Projects--In Operation (2000-2025)
#### 3.1.8 Rio Do Vento Wind Farm  
Rio Grande do Norte State  
Located in Brazil's northeastern region within Rio Grande do Norte state, the Rio do Vento Wind Farm is showcased in Figure 16 through panoramic imagery, while Table 9 outlines its critical technical parameters [32]:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Location & Bahia State & - \\ Concession Owner & Omega Geracão & - \\ Type of Wind Farm & Onshore & - \\ Number of Turbines & 202 & - \\ Turbines Manufacturer & Siemens-Gamesa & - \\ Diameter of Turbines & 145 & m \\ Hub Height & 100–120 & m \\ Installed Capacity per Turbine & 4.0 & MW \\ Total Installed Capacity & 808.1 & MW \\ Capacity Factor & 0.41 & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: Assuruá wind farm main specifications.  
Figure 15: Panoramic view of wind turbines in the Assuruá wind farm.  
Figure 16 shows that the wind farm is situated in an area with no adjacent human settlements.  
As detailed in Table 9, the project incorporates 240 Vestas wind turbines, each featuring a 150 m rotor diameter. The towers are installed at heights ranging from 90 to 110 m. Each turbine delivers 4.2 MW of power, culminating in a substantial total installed capacity of 1038 MW. The estimated capacity factor of 0.37 indicates consistent energy generation, accounting for regional wind patterns and operational efficiency.
## 1 Introduction  
Wireless sensor networks (WSNs) technology has gained significance due to its potential for supporting a wide range of applications for military operations, industrial, surveillance, target tracking system, health needs, monitoring disaster areas and many other applications.  
WSN consists of a large number of very small, low cost sensor nodes that are deployed randomly. The sensor nodes are typically equipped with power-constrained batteries. Unlike other wireless networks, it is generally not practical to charge or replace the battery. Since prolonging the life span of the sensor nodes is extremely vital, energy efficiency becomes the most important aspect of the design of MAC protocol of sensor networks. The idle listening can be considered as the main source of energy wastage for the case of wireless sensor networks [1]. Therefore, in the sensor network, nodes do not wakeup all the time rather they prefer energy preservation by operating in a low duty cycle (sometimes in active mode and sometimes in sleeping mode) as mentioned in Figure 1. With this sleep scheduling technique, nodes can operate in a low duty cycle and thus save energy and extend the network lifetime. But on the other hand this also causes increased communication latency and synchronization overhead as well. Different sleep scheduling schemes have been analyzed in [2] with the proposal of a scheduling methods that can decrease the end to end delay. However, interference free scheduling cannot be ensured with this method. If each communication link can use a separate slot then interference free scheduling can be achieved. In this case the required number of slots will be the same as the number of communication links in the network. But, this method requires much more slots than are actually necessary, and this will definitely augment delay and reduce the channel utilization. On the other hand, in terms of energy preservation, performance of broadcast scheduling is worse than the performance of link scheduling in WSN. We herein offer a new Medium Access Control protocol for WSN, named EP-MAC, which combines the idea of parallel transmission and the link scheduling together where the power of CSMA is used in order to minimize the fundamental problems of using link scheduling alone (pair wise TDMA) with the aim of achieving high energy efficiency as well as high throughput.  
The rest of the paper is organized as follows: Section 2 reviews related works. In Section 3 we elaborate on the design of the Efficient MAC with Parallel Transmission (EP-MAC) protocol. In Section 4 we compare our proposed MAC protocol with other existing protocol and describe the performance in detail. And finally in Section 5 we conclude the paper with mentioning some scope of future works.  
Figure 1: Scenario of periodic listen and sleep of a sensor node.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 3 Materials and Methods  
### Credit Card Dataset  
The European cardholders dataset [45] is used in this study. It is publicly available and comprises 284807 transactions made by European cardholders in September 2013. The dataset has been widely used in different credit card fraud-detection studies [1; 6; 46]. It contains 492 fraudulent transactions, and the rest are legitimate transactions, i.e., only 0.17% of the dataset belongs to the minority class, and 99.83% belongs to the majority class; hence, the dataset is highly skewed and it is challenging for conventional ML algorithms to learn from the dataset.  
Due to privacy concerns, the features in the dataset were anonymized as \(V1,V2,\ldots,\) and \(V28\), except for the \"Time\" and \"Amount\" features. The \"Time\" attribute indicates the seconds elapsed between a transaction and the first transaction in the dataset, whereas the \"Amount\" indicates the value of the transaction. Meanwhile, the \"Class\" attribute is the response variable, representing legitimate and fraudulent transactions and having values 0 and 1, respectively.
## 2 Materials and Methods
### Deep Learning Model for Evaluating Weather Disaster Emergency Response Capacity
#### 2.2.3 Network Parameter Update
```
Input: Sample set S\({}^{*}\) comprising meteorological disaster emergency response capability features Error limit E Maximum number of iterations K Learning rate (lr) Batch size (B) Output: Optimized weights and biases for the capability feature extraction model
```  
Initialize: epoch = 0  While epoch < K:  for batch in S\({}^{*}\): # Extract a batch of weather emergency subject sample data from S\({}^{*}\)  Loss = 0 # Initialize the total loss of each batch  for i in range(batch_size):  for l in range(1, Layers): # Stepwise forward calculation to obtain hidden layers based on Equations (9)-(11)  z_l = dot (W_l, r_(l-1))  r_l = g_l(_z_l)  p_i = g_l(_z_l) # Forward calculation of the output layer according to Equation (12)  # Calculate the loss according to Equation (8)  \(\L_{(\alpha\text{-}A\text{-}\text{Adaptive-}\text{Focal})(\text{p\_{i}\_{y}\_{i}})}\) = -\(\alpha\)_i s _i y _i^T log(p_i)  Loss += \(\L_{(\alpha\text{-}A\text{-}\text{Adaptive-}\text{Focal})(\text{p\_{i}\_{y}\_{i}})}\) # Calculate the average loss of a batch of samples \(\L_{(\alpha\text{-}A\text{-}\text{Adaptive-}\text{Focal})(\text{p\_{batch}\_{y}\_ {\text{batch}}})}\) = Loss/batch_size if L_(\(\alpha\text{-}A\text{-}\text{Adaptive-}\text{Focal})(\text{p\_{batch}\_{y}\_ {\text{batch}}})\leq\text{E}\):# If the error is less than E, terminate the training  break # Calculate the loss partial derivative of z_Layers according to Equation (15)  \(\delta\_\text{Layers}=\alpha\_\text{batch s\_batch | p\_batch-y\_batch|}\)  for l in range(Layers-1, 0, -1): # Backward propagate the gradient according to Equations (14)-(16)  # Calculate the loss partial derivative of z_l according to Equation (14)  \(\delta\_\text{l}=\alpha\_\text{batch s\_batch W\_(l+1)T\_\delta(l+1)^{*}\text{g\_l}\)(z_l) # Calculate the gradient of the loss to the lth layer parameter according to Equation (16)  (\(\theta\text{L\_{(}\alpha\text{-}A\text{-}\text{Adaptive-}\text{Focal})(\text{p\_{ batch}\_{y}\_{\text{batch}}}))}/\partial\text{W\_l}=\alpha\_\text{batch s\_batch \&l\_r_(l-1)^T}\) for l in range(Layers-1, 0, -1): # Update the weights of each layer according to Equation (17)  \(\text{W\_l}=\text{W\_l}-\text{lr}\times\alpha\_\text{batch s\_batch \&l\_r\_(l-1)^T}\)  epoch += 1 ```  
**Algorithm 1**Training algorithm of deep neural network model based on weighted adaptive focus loss function. To further enhance the transparency and reproducibility of our study, Appendix A includes a segment of the code used in our experiments. This code provides insights into the specific implementation details of our models and algorithms. It serves as a valuable resource for readers interested in the technical intricacies of our approach or those who wish to replicate or build upon our research. We encourage readers to refer to Appendix A to gain a more comprehensive understanding of the coding practices and methodologies employed in this study.
## 3 Materials and Methods
### GC\(\times\)GC-TOFMS Analysis  
Analysis was performed using a Pegasus 4D GC\(\times\)GC instrument (LECO, St. Joseph, MI, USA), including an Agilent 6890A GC (Agilent Technologies, Palo Alto, CA, USA) coupled with Pegasus IV time-of-flight mass spectrometer (LECO Corp., St. Joseph, MI, USA) and Gerstel MPS2 auto-sampler (Gerstel, Mulheim, Germany). The column set consisted of a 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m primary column (1D) with Equity 1 stationary phase (Supelco, Bellefonte, PA, USA) and a 2.0 m \(\times\) 0.10 mm \(\times\) 0.10 \(\upmu\)m secondary column (2D) with Sol-Gel-Wax stationary phase (SGE Analytical Science, Austin, TX, USA). A modulation period of 5.0 s was used with the cryogenic trap cooled to \(-\)196 \({}^{\circ}\)C by liquid nitrogen.  
The volatile compounds were separated using the following temperature gradient program for the primary GC oven: Initial temperature of 40 \({}^{\circ}\)C maintained for 1 min, then ramped at 8 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, and finally kept for 10 min. The temperature program for the secondary GC oven was with the shift of +40 \({}^{\circ}\)C according to the program of primary GC oven. The total analysis time was 37 min. The injector was carried out in splitless mode at 250 \({}^{\circ}\)C. Helium was used as the carrier gas at a constant flow of 1.0 mL/min. The temperatures for the transfer line and ion source were maintained at 250 \({}^{\circ}\)C. The detector voltage was set to 1600 V. Ions in the \(m\)/z 40-500 range were analysed with a data acquisition rate of 125 spectra/s.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Optimized Link State Routing (OLSR)  
OLSR, which stands for \"optimized link state routing,\" is a dynamic routing technology that is frequently employed. Transmissions of routing information at regular intervals from each node in the network ensure that all of the nodes in the network share the same comprehensive view. The protocol's periodic nature results in a large amount of additional overhead being incurred. This problem is solved by OLSR, which reduces the amount of traffic that may be forwarded. In order to accomplish this, multi-point relays, also known as MPRs, are utilized; these devices are in charge of relaying and routing messages. Each node selects MPRs manually from among the nodes that are immediately adjacent to it. It is preferable for a node to choose MPRs that will enable it to communicate with at least one of its neighbors that is accessible over a hop-and-a-half path. It is the job of the MPRs to transmit the control traffic that is produced by those nodes.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 4 Experimental Results and Analysis
### Results and Analysis  
It is preliminarily verified by experiments that if all groups of LLE fingerprint extraction is carried out at the same time, too much features are lost and the retrieval performance is poor. So as to ensure the efficiency of audio retrieval is not seriously affected by the feature lost in the dimensionality reduction process, the algorithm does not reduce the dimension of energy vectors in all groups. The rest of the energy vectors that have not been reduced by LLE are calculated as shown in Table 1.  
As can be seen in Table 1, 1-18 dimensions are computed in a single group and represent the energy distribution of horizontal, vertical, central, and marginal sub-regions. The extended sub-region is composed of marginal sub-region in pairs and then generates the fingerprint by the comparison of energy pairs corresponding to the 19-22 dimensions.  
Once the fingerprint has extracted by the reduced energy vector, the computation has uncertain effect on the efficiency of retrieval. The maximum reduced dimensions in a single group and the group numbers can be reduced at the same time are presented in the rest of paper by giving the experimental results. The performance is evaluated by the metrics mentioned in Section 4.1.
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 3 Experiments
### Discussion of Results  
This part discusses the results mentioned in the first three parts of this section. In the first part, we gave a brief introduction to the experimental platform. In the second part, we described the collection of the latitude and longitude coordinates of the four vertices of the straight quadrilateral PCA boundary, the analysis of the single-point positioning accuracy and the determination of the GPS-RTK measurement error distribution model. In the third part, we described how we took points at equal intervals across one straight line segment and two curved line segments with curvature radii of 70 to 300 m for simulation experiments, explored the effect of window width and determination coefficient on the experiments, and finally achieved good experimental results by the proposed BSW algorithm in both straight and curved polygons.
The simulation results for the frequency response model are examined and evaluated in Section 5, including a comparison of the performance of the different controllers and optimization algorithms. Section 6 provides a further analysis of the robustness of the suggested controller for three different examined cases. Finally, the conclusions of this research are presented in Section 7.
# Fingerprint Based Codebook for RIS Passive Beamforming Training
## 5 Performance Evaluation
For instance, using one candidate RP can provide RX with a rate of nearly 10.3 bps/Hz on average if the provided localization comes with a 2 m error, while the rate is decreased to 8.5 bps/Hz if the error reaches 10 m. Moreover, to mitigate the impact of the high localization error and obtain better performance, searching using more RPs from FP-CB is recommended. For example, if the positioning error reaches 10 m, using three RPs guarantees only a 9.42 bps/Hz achievable rate on average, whereas using 10 and 15 candidate RPs can obtain 10.16 and 10.54 bps/Hz rates, respectively. Thus, we will consider using between five and 10 candidate RPs for the searching stage, as a balance between the obtained performance and immunity against positioning error can be achieved. Figure 4: The FP-PBF scheme’s achievable rate versus the number of candidate RPs is comparable to other PBF schemes. Figure 5: The achievable rate of the proposed scheme versus localization accuracy using different numbers of candidate RPs. Figure 6 shows the impact of the time-variant channel on the performance of the proposed FP-CB scheme and the CE and AO schemes. In which we study the obtained effective achievable rate, \(R_{e}=(1-\tau/T)\times R\), where \(\tau\) and \(T\) are the searching overhead and the channel coherence time. The proposed scheme outperforms the CE and AO schemes in rapidly changing channels because it needs a lower overhead for searching, \(\mathcal{O}(C)\), comparable to the CE-based scheme, which needs, \(\mathcal{O}(N)\) overhead. For example, assuming \(T=100\), using five and 10 candidate RPs, the proposed scheme obtains 10.38 and 9.98 bps/Hz effective rates, respectively, while the CE-based scheme achieves 9.66 bps/Hz effective rates. Furthermore, when considering the system's required computational complexity (CC), we can see a high reduction in CC when the system uses the proposed scheme over the CE and AO schemes. For instance, assuming FP-CB with \(C=16\), the proposed scheme CC will be \(16\times 16\), while the CC of the CE and AO schemes will be \(16^{4.5}\), considering \(N_{i}=1\). Although a random CB-based scheme has the same CC as our proposed scheme in this case, its achievable rate is lower, as presented in Figure 4.