esa-sceva/satcom-chunk-collection huggingface dataset:
# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 4 Discussion
in deep reservoirs is generally lower, the fracture network tends to be more developed. After hydraulic fracturing, efficient flow channels can be established, improving reservoir productivity. In engineering applications, TVD is a core parameter in fracturing design, influencing fracture propagation range, orientation, and network coverage. Deep reservoirs require higher fracturing pressures and specially formulated fracturing fluids to accommodate complex geological conditions and achieve effective resource recovery. FBR330 (330-day flowback rate) is a critical parameter for evaluating fracturing effectiveness and reservoir utilization, significantly impacting the accuracy of EUR predictions. The flowback rate reflects the proportion of fracturing fluid that returns from the reservoir; a high flowback rate typically indicates good fracture conductivity, minimal fluid retention within fractures, and unobstructed flow channels, supporting sustained gas production. Conversely, a low flowback rate may suggest fluid retention and fracture blockage, reducing gas flow efficiency. The value of FBR330 also indirectly characterizes pore and fracture connectivity, where higher flowback rates are often associated with reservoirs possessing better porosity connectivity and enhanced fracture conductivity. Additionally, FBR330 serves as a reference for production management optimization. Controlling the flowback rate appropriately can prevent premature fracture closure while avoiding rapid pressure depletion that could induce reservoir damage, ultimately improving EUR recovery efficiency. Despite the promising results, this study has certain limitations. The research data primarily originated from the CN block, which introduces some regional specificity to the findings. Further validation is required under different geological conditions and operational environments to confirm the model's generalizability. Future studies could expand the dataset to include more regions and diverse geological conditions, enhancing the model's applicability across broader scenarios. Figure 16 shows that the model's training loss decreases consistently as the number of iterations increases, while the validation loss initially decreases rapidly and then gradually stabilizes. Furthermore, the reasonable gap between the training and validation losses indicates that the model converges progressively during training without exhibiting signs of overfitting or underfitting. Additionally, the stability of the validation loss indirectly demonstrates the rationality of the training and validation dataset split. Figure 16: Learning curve. Additionally, improving the interpretability of machine learning models remains an important challenge. While the attention mechanism partially improved the transparency of the model, integrating more explainable artificial intelligence techniques could provide deeper insights into the interactions between features and their specific impacts on EUR prediction.
# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 5 Conclusions and Future Work  
This paper proposes a dual-link data transmission method based on the Internet of Vessels, which significantly improves the reliability and transmission speed of underwater communication by utilizing vessels as relay nodes. The proposed spatial crowdsourcing task allocation algorithm, combined with a comprehensive evaluation function, selects the optimal relay nodes. The HDTA method demonstrates clear advantages in reducing transmission delay, increasing throughput, and improving network reliability, with simulation results validating its superiority. Future research will focus on the following aspects: further optimization of algorithm performance and computational efficiency, and strengthening the system's robustness in complex marine environments, particularly in adapting to dynamic conditions such as weather changes and vessel density variations. Additionally, experimental validation in more real-world scenarios will be conducted, along with the development of new energy consumption models and energy-saving strategies, the integration of satellite communication and UAV-assisted communication technologies, and the realization of a land-sea-air integrated communication system. These efforts will  
Figure 15: Comparison of energy loss.  
ultimately provide more reliable and efficient solutions for data transmission in the marine domain in the future.  
Conceptualization, F.L. and Y.G.; methodology, F.L.; software, F.L.; validation, F.L., Y.G. and Z.W.; formal analysis, F.L.; investigation, Y.G.; resources, F.L.; data curation, F.L. and Z.W.; writing--original draft preparation, F.L. and Y.G.; writing--review and editing, F.L., Y.G., Z.W., Y.C. and J.G.; visualization, F.L.; supervision, F.L.; project administration, Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Key Research and Development Program of China, grant number 2024 YFB3311800.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers  
[PERSON]\({}^{\star}\)  
++  
links [11]. Previous work related to single-switch-based shaping includes the article by [PERSON] and [PERSON] [12], which discussed the control of a servomechanism using a phase-plane approach for rest-to-rest control.  
Most articles on input shaping are based on the system starting from a resting state. A smaller number of articles have used input shaping methods with non-zero initial conditions. [PERSON] et al. demonstrated an input shaper designed to cancel initial oscillations in cases with known, finite disturbances [13]. [PERSON] et al. also considered input shaping control for systems with non-zero initial conditions; they proposed a parametric formulation to compute the switching times [14]. These methods are effective enough to reduce vibration compared to unshaped control, but they are not always capable of ensuring the final \"rest\" condition when performing a slew maneuver.  
In this article, we introduce an input shaping control method for both rest-to-rest and non-rest-to-rest rotational maneuvers of a structure with one flexible mode. The approach is based on a single-switch control with a numerically optimized Zero Vibration Derivative-Derivative (ZVDD) input shaper. The main objective of the proposed method is to ensure the final \"rest\" condition regardless of the initial conditions and the minimum number of switches. Compared to the available literature on input shaping control, this article aims to solve the non-rest-to-rest control problem with an unknown minimum number of bang-bang switches when performing a slew maneuver. The proposed method can be adapted to several problem variants, such as actuator constraints and additional boundary conditions.  
The article is organized as follows. First, the dynamic model is introduced. Second, the vibration introduced by a single-switch bang-bang control is analyzed. Third, the conventional ZVDD shaper is discussed, and it is shown that it can leave a moderate to large amount of vibration for non-rest-to-rest problems. Finally, the optimized ZVDD shaper is introduced and applied to two examples.  
This work proposes the use of optimized input shaping in the design of an open-loop control system, useful for guidance and trajectory planning. For the non-rest-to-rest control problem, it is assumed that the initial non-rest conditions are known. Of course, given the complexity of the problem, if the initial non-rest conditions are partially/completely unknown, then this work will require an initial state-estimation step. However, with the development of various advanced sensor-based measurement methods and sensor fusion algorithms, it is expected that the initial conditions will be known with a high level of precision. Thus, in this article, we are only interested in cases where the initial non-rest conditions are known during slewing and we explore the use of input shaping to solve such problems.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 5 Conclusions  
In this paper, various deep learning models were selected for comparison, including recurrent neural networks (BP, LSTM, GRU), bidirectional recurrent neural networks (BiLSTM, BiGRU), and hybrid neural network models (CNN-LSTM, CNN-GRU, GRU-LSTM, CNN-LSTM-Attention), as well as models combining different optimization algorithms such as ABC-CNN-RGRU-Attention, SSA-CNN-RGRU-Attention, and WOA-CNN-RGRU-Attention. By comparing the prediction errors of these models in both longitude and latitude dimensions, the PSO-CNN-RGRU-Attention model, which integrates PSO, CNN, residual networks, GRU, and attention mechanisms, demonstrated the best performance, with the lowest MAE and RMSE. The PSO-CNN-RGRU-Attention model achieved better prediction results in both longitude and latitude compared to the other models, effectively addressing navigation safety issues in this maritime area. The model's improved performance enhances confidence in decision-making and is expected to play a significant role in fields such as traffic management and maritime safety.  
Future research can further verify the performance of this model in a wider range of application scenarios by considering additional natural and human factors, thereby improving the model's adaptability and prediction accuracy in complex environments. In addition, the use of more advanced deep learning architectures and training techniques, such as reinforcement learning or multi-task learning paradigms, should also be explored to further enhance the performance of geographic information prediction tasks.  
Conceptualization, W.L. and Y.C.; data curation, W.L.; formal analysis, W.L.; funding acquisition, Y.C.; investigation, W.L.; methodology, W.L.; project administration, Y.C.; resources, Y.C.; software, W.L.; supervision, W.L. and Y.C.; validation, Y.C.; visualization, W.L.; writing--original draft, W.L. and Y.C.; writing--review and editing, W.L. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the LIAONING PROVINCE APPLIED BASIC RESEARCH PROGRAM, grant number 2022 JH2/101300272; the LIAONING PROVINCIAL EDUCATION DE-PARTMENT BASIC RESEARCH PROJECT, grant number LJ212410148031; and the LIAONING PROVINCE APPLIED BASIC RESEARCH PROGRAM, grant number 2023 JH26/10300013.  
Not applicable.  
The data are available upon request from the corresponding author. The data are not publicly available due to confidentiality.  
The authors declare no conflicts of interest.
## 3 Our Dedicated and Scalable Multiplier Architectures  
As introduced in Section 1.3, we have used the digit-parallel multiplication approach for multiplying two \(m\)-bit polynomials. A digit-parallel method can be employed by dividing one input polynomial into smaller digits with different digit lengths, depending on the designer to choose. Then, multiplication over each created digit with an \(m\)-bit polynomial is performed either by selecting the least significant or most significant polynomial digits. In our dedicated and flexible architectures (presented in Sections 3.1 and 3.2), we have created digits of an \(m\)-bit polynomial \(B(x)\) with a size of 32 bits each. Moreover, for simplicity, we have used the least-significant digit multiplication approach to acquire the resultant polynomials. The flowchart of our polynomial multiplication architectures is illustrated in Figure 1. Similarly, the pseudocode is given in Algorithm 1.  
Figure 1 describes the proposed methodology for multiplying polynomial coefficients. It starts with the register transfer level (RTL) implementations of our dedicated and flexible architectures. Finally, it ends after reporting and comparing the implementation results with the state of the art. The pseudocode of the proposed architectures (shown in Algorithm 1) takes two \(m\)-bit polynomials \(A(x)\) and \(B(x)\) as input. It results in a resultant polynomial \(C(x)\) with \(m\)-bit length as output. The line one in Algorithm 1 performs the multiplication (using Equation (2)) and concatenation operations in an iterative way (or under loop). The _for loop_ in statement one of Algorithm 1 starts from 1 and continues until \(d=\frac{m}{n}\), where \(d\) is the total number of digits, \(m\) is the operand length and \(n\) shows the digit size (32 in our architectures). Statement two, i.e., the \(RED()\) function, in Algorithm 1 performs the polynomial reduction using Algorithms A1-A5 for targeted input operands of 163, 233, 283, 409 and 571.  
```
Input: Polynomial, \(A(x)\) and \(B(x)\) with \(m\)-bit length Output: Polynomial, \(C(x)\) with \(m\)-bit length \(1\) for\((i\) from \(1\) down to \(d=\frac{m}{n})\)do \(M_{i}\longleftarrow D_{i}(x)\) computation of polynomial multiplications using Equation (2) \(T_{i}\longleftarrow M_{i}\oplus(T_{i}\ll n)\) concatenation to produce polynomial \(T(x)\) with \(2\times m-1\) bit length \(2\) \(C\longleftarrow RED(T_{i})\) \(Return\ (C(x))\)
```  
**Algorithm 1**Pseudocode of our dedicated and scalable multiplier architectures
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.9 EEDBR [32]  
In [32], the authors proposed a routing protocol that increases the network's lifespan by reducing the number of transmissions. This routing protocol is a modified version of the DBR, and the authors named the protocol energy-efficient depth-based routing protocol (EEDBR). It has two phases, firstly, the knowledge acquisition phase and the data transferring phase. During the knowledge acquisition phase, the nodes broadcast a Hello packet to the adjacent nodes with the message information of the depth of the node and residual energy. The node above the sender receives the message for the data forwarding phase, ensuring the data is closer to the sink. The nodes then choose a forwarder with lesser depth than the sender, and this helps in eliminating unnecessary transmission. The forwarding nodes store the information of the Hello packet. The storing time depends on the residual energy of the node. For nodes with similar residual energy, the author uses priority values to determine which node will keep the data more than the other and avoid multiple data forwarding. According to the authors, eliminating extra messages helps expand the lifetime of the whole network. The authors compared the protocol with DBR, and the simulation shows it has a higher lifetime than DBR, a lesser end-to-end delay, and less energy consumption. The choice of the simulator is NSZ for comparison.
## 3 Data and Methods
### Method
#### 3.2.2 Geometric Registration  
Although the acquired aerial images were subjected to geometric and orthorectification corrections, there was still position deviation to a certain extent because of the complex terrain. After comprehensive consideration of the image quality and position error compared with the Landsat TM image, we selected airborne images from 2009 as our reference images, and selected 100 to 150 control points at each hazard site to complete the geometric registration of the images from 2008, 2010, and 2011. In addition, the China geological map was projected in UTM coordinates (WGS84 datum), the same as Landsat image.
# Wind Turbines Around Cut-In Speed: Startup Optimization and Behavior Analysis Reported to MPP
## 6 Discussion  
At low wind speeds, specifically above the connection threshold of 3 m/s, the duration required to attain optimal speed solely through wind input is significantly prolonged, resulting in considerable wastage of wind energy. In power converters that facilitate bidirectional power flow, the electric generator can be transitioned into engine mode, thereby reducing the time needed to achieve optimal speed.  
Analyzing the operation of these wind turbines, it was possible to:  
1. Develop a mathematical model for the wind turbine (_WT_) and the double-fed asynchronous/synchronous motor, derived from the induction generator with a wound rotor, utilizing experimental data as the foundation;
2. Follow the transition of the asynchronous generator to a double-powered synchronous motor, with the stator and rotor connected to the same frequency network, so that the turbine operates at the point of maximum power in the shortest possible time interval;
3. Visualize the process of bringing it to the optimal speed and interpreted the oscillations of the load angle and power at the double powered synchronous motor;
4. Determine the oscillation of the load angle, the power when switching to generator mode as well as the operation at the maximum power point. The results obtained reveal several key aspects that can be emphasized:
5. When switching the asynchronous generator to the mode of a double-powered synchronous motor, at the same frequency, oscillations of the load angle and power occur;
6. The most pronounced oscillations are in the stator and rotor power supply at variable frequency.  
Consequently, the power supply of the motor obtained from the induction generator with the wound rotor, having the same frequency in stator and rotor, is new and of interest in high-performance electric drives, with direct applicability in starting wind turbines at cut-in wind speeds. The work demonstrated the importance of modeling the double synchronous motor powered from the same variable frequency source.
# Characterization of Invar Syntactic Foams Obtained by Spark Plasma Sintering
## 2 Materials and Methods
The density was measured using the following process. SPS cylindrical samples (Figure 3) were weighed, the volume of sample was computed and the density (\(\rho_{\mathrm{s}}\) in g/cm\({}^{3}\)) was determined as the ratio between the mass and volume of the samples using the following relationship:  
\[\rho_{\mathrm{s}}\ =\ \frac{\mathrm{m}}{\mathrm{V}}\ =\ \frac{4\cdot\mathrm{m}}{ \pi\cdot\mathrm{d}^{2}\cdot\mathrm{h}}\ \ \left[\frac{\mathrm{g}}{\mathrm{cm}^{3}}\right] \tag{1}\]  
where m is the sintered sample mass (g), V is the sintered sample volume (cm\({}^{3}\)), d is the sample diameter (cm) and h is the sample height (cm). The value of the Invar/20%NiCrSiB composite's sintered density was calculated to be 3.13 g/cm\({}^{3}\). Figure 3: Photo images of spark plasma sintered Invar/20%NiCrSiB syntactic foam sample. Figure 2: Optical images of the wall thickness (g) of some NiCrSiB superalloy particles; (**a**) g \(\cong\) 6 \(\upmu\)m, (**b**) g \(\cong\) 26 \(\upmu\)m, (**c**) g \(\cong\) 54 \(\upmu\)m.  
The relative density of the spark plasma sintered foams (\(\rho_{\rm r}\)) was computed using the following relationship:  
\[\rho_{\rm r}\ =\ \frac{\rho_{\rm s}}{\rho_{\rm t}} \tag{2}\]  
where \(\rho_{\rm s}\) is the sintered density, and \(\rho_{\rm t}\) is the theoretical density of the composite foam (g/cm\({}^{3}\)). The theoretical density of the composite foam (\(\rho_{\rm t}\) in g/cm\({}^{3}\)) was determined using the mixture relationship rule, as shown in Equation (3). The theoretical density of the NiCrSiB superalloy is 6.72 g/cm\({}^{3}\) and that of the Invar alloy is 8.22 g/cm\({}^{3}\). \[\rho_{\rm t}\ =\ \sum\rm f_{i}\cdot\rho_{i} \tag{3}\]  
where \(\rm f_{i}\) is the volume fraction and \(\rho_{\rm t}\) is the theoretical density of both components of the Invar syntactic foam. The porosity values (P) were computed with the following relationship:  
\[\rm P\ =\ (1-\rho_{\rm r})\cdot 100 \tag{4}\]  
The porosity values of the sintered Invar/20%NiCrSiB foam were found to be 40%.
## 2 Materials and Methods
### Statistical Analysis  
For all continuous parameters, changes over time for separate patient groups were analyzed using paired t-tests or, where more than 2 time points were available, repeated measures ANOVA. The influence of available predefined patient characteristics (age, gender, BMI, and Kellgren-Lawrence grade) on osteophyte formation was tested with linear regression. For comparisons with the control groups, linear regression was used, correcting for baseline values. For comparisons where in both groups more than two time points were available, mixed ANOVA was used instead.  
In SF patients, for the categorical Altman score per region, the Wilcoxon Signed Rank test was used to test changes over time The changes in whole-joint osteophyte Altman score and in synovial fluid biomarkers were analyzed with paired t-tests, and the Pearson correlations between total joint osteophyte Altman score and biomarker baseline values and changes over time were calculated. Finally, SF patients were divided into three groups (trichotomized) based on an increase, no change, or decrease in total osteophyte Altman score over time. The change in TGF\(\beta\)-1 and in IL-6 during the distraction period was compared between these three groups using a Kruskal-Wallis test because of the limited number of patients per group.  
Normal distribution was verified for all outcome parameters; in case outcomes were not normally distributed, log transformation was performed. For all tests, a \(p\)-value < 0.05 was considered statistically significant. Absolute values are presented with mean \(\pm\) standard deviation (SD), while changes over time are presented as mean change and 95% confidence interval (95%CI).