esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Suppression of Self-Excited Oscillations in Pressure Servo Valve System
## 2 Dynamic Model of the PSVS
### Simulation Analysis and Experiment  
#### 2.3.1 Test Platform  
In order to verify the accuracy of PSVS dynamic model, an experimental platform is built in this paper. The hydraulic schematic diagram of the experimental platform is shown in Figure 5.  
Figure 4: The structure schematic diagram of the pressure servo valve.  
Figure 5: Hydraulic principle of experimental platform.  
The output pressure \(p_{b}\) can be measured by the pressure sensor 11.1. The load unit is used to simulate the dynamic load of the hydraulic cylinder. The backpressure unit consists of a pressure sensor and a relief valve.  
The experimental platform parameters are shown in Table 2.  
The pressure sensor used in this paper is shown below which is provided by measurement SPECIALTIES. The range of the pressure sensor is 40 MPa, and the accuracy is 0.25% of F.S. (Figure 7).  
The comparison between Simulation and Experiment  
When the step current signal of 40 mA is given, and the output pressure measured experimentally is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Oil Supply** & **System Flow** & **Output Pipe** & **Oil Return** & **Backpressure** & **Load Stiffness** & **Load Damping** \\
**Pressure** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** \\ \hline
15 MPa & 6.25 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s & \(\Phi\)0.006 \(\times\) 1.50 & \(\Phi\)0.006 \(\times\) 1.01 & 0.6 MPa & 1 \(\times\) 10\({}^{6}\) N/m & 50 Ns/m \\ \hline \end{tabular}
\end{table}
Table 2: The parameters of experimental platform.  
Figure 6: Pressure servo valve experimental platform.  
Figure 7: The pressure sensor.  
According to Figure 8, the following conclusions can be drawn:  
Under the action of step signaling, the output pressure oscillates stably with constant amplitude, and its peak-to-peak value is about 0.40 MPa. Under the condition of a frequency range of 0-100 Hz and a sampling frequency of 60 Hz, the FFT spectrum of stable oscillation pressure can be obtained as shown in Figure 8b. It can be seen that the main frequency of pressure oscillation is about 25 Hz.  
Table 3 It can be seen that the system dynamic model has high simulation accuracy.  
After the ramp current signal of 40 mA is input to PSVS, similar pressure oscillations still exist, as shown in Figure 9.  
Figure 8: Experimental results of output pressure under step signal: **(a)** Time domain curve, **(b)** FFT spectrum of output pressure.  
Figure 9: Experimental results of output pressure under ramp signal: **(a)** Time domain curve, **(b)** FFT spectrum of output pressure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Peak-Peak Value (Mpa)** & **Main Frequency (Hz)** \\ \hline Experiment & 0.319 & 24.9584 \\ Simulation & 0.347 & 24.9764 \\ Error & 8.8\% & 0.072\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of output pressure oscillation.
# RT-GAN: GAN Based Architecture for Precise Segmentation of Railway Tracks
## 3 Method
### Track Discriminator  
The structure of the track discriminator, shown in Figure 5, is a fully convolutional Patch-GAN discriminator [29], consisting of 5 convolutional layers. The input of the track discriminator is a 6-channel combined feature, consisting of the input image (3 channels), the segmentation result (1 channel) and the gradient feature (2 channels), and the output is a single-channel scoring map \(F\) instead of a single score after 4 downsamplings.  
In contrast to image-specific discriminators, if only the semantic segmentation results of the tracks are fed to the discriminator, the adversarial loss is difficult to optimise, leading to training failure. This may be due to the sparsity of the track regions in the image. Natural images have gradient features on each local region that can be learned, but the sparse pixel distribution of the track-only region contains less gradient information, which makes the training of the discriminator difficult. To solve this problem, the gradients of the input image and the classification result are treated as additional conditions. By concatenating the input images \(I\), the segmented image \(C_{cls}\) and the gradient of \(C_{cls}\) as inputs, the track discriminator gains enough knowledge to distinguish the difference between the generated distribution and the true distribution, and the training becomes stable. Therefore, for two discriminator inferences with generated image \(C_{cls}\) and ground truth image \(C_{gt}\), the corresponding score maps are as follows:  
\[F_{cls}= \mathcal{D}(\{I,X,X_{x},X_{y}\}), \tag{14}\] \[F_{gt}= \mathcal{D}(\{I,Y,Y_{x},Y_{y}\}), \tag{15}\]  
where \(X\) is the predicted result \(C_{cls}\), \(Y\) is the ground truth \(C_{gt}\), \(X_{x}=C_{cls}\odot S_{x}\), \(X_{y}=C_{cls}\odot S_{y}\), \(Y_{x}=C_{gt}\odot S_{x}\), \(Y_{y}=C_{gt}\odot S_{y}\), \(S_{x}\), \(S_{y}\), and \(\odot\) is the same as Equation (11).  
The task of the track discriminator is to try to distinguish the RT-seg output result \(C_{cls}\) from the ground truth \(C_{gt}\) by discriminating \(C_{cls}\) as false and \(C_{gt}\) as true. Therefore, the loss function of the track discriminator is mainly divided into two parts: \(L_{D}\) for updating the weights of the track discriminator and \(L_{G}\) for updating the weights of the RT-seg. The hinge loss function [30] is used to determine whether the input is true or false. The adversarial losses for training RT-GAN are as follows  
\[L_{D}=\mathbb{E}\big{[}\sigma\big{(}1-F_{gt}\big{)}\big{]}+\mathbb{E}[\sigma (1+F_{cls})], \tag{16}\]  
\[L_{G}=-\mathbb{E}[F_{cls}], \tag{17}\]  
where \(\sigma(\bullet)\) denotes the ReLU activation function, and \(f_{gt}\) and \(F_{cls}\) are two score maps of the track discriminator output.
## 5 Developing Construction Data Hub (CDH)  
After analyzing the articles mentioned earlier in the literature and employing the twelve dimensions that impact planning and control in these studies, an evaluation of the addressed dimensions, associated phases, and the detailed work level from each study is shown in Table 2. All the studies that utilized data acquisition models were custom-tailored to their respective research objectives. While these models were effectively utilized and yielded satisfactory results in knowledge discovery, they lacked generality. Implementing these models in different scenarios might be challenging, either due to their lack of adaptability or the need for extensive modifications. The results shown in Table 2 indicate that none of the previous studies addressed the twelve dimensions while focusing on collecting project data at the work package level, which is the most efficient detailed work level. The proposed CDH model supports structuring a comprehensive data warehouse at the work package level and seeks to collect construction project data from twelve dimensions that impact the project's planning and control. An appropriate structure for the CDH requires the proper identification of all the entities that are involved in generating construction project data and the precise relationships between them. The authors divided the entities into two groups: organization-related entities and project-related entities. Organization-related entities refer to portfolios, programs, projects, and resources, whereas project-related entities are phases, disciplines, and work package types (also referred to as \"MDWPs\"). The proposed CDH adopts a relational database structure. Figure 7 presents the entity-relationship diagram (ERD) that illustrates the various entities within the database and their interconnections. Each of the entities included in the ERD has several attributes that describe that entity. The MDWP entity is the main entity upon which the rest of the ERD is based, since the MDWP is the product of the integration between the OBS, WBS, RBS, and PBS. The integration is shown through the relation between the significant entities, such as the organization and the project. The authors used SQLite to build the client/server application, which demonstrates the conceptual model of the CDH (Figure 8). Figure 8a shows the main page of the CDH that presents the entities such as companies, portfolios/programs, projects, etc. Figure 8b shows an example of an organization's portfolio that includes one program--namely, program 1- and Figure 8c shows an example of a project's WBS and its related WPs (showing attributes such as unit of measure (UOM), phases, resource details, etc.). Through the developed CDH, users can input new data manually or import it from Primavera P6, Microsoft Project, Microsoft Access, or Excel files. The CDH can be customized to meet the needs of its users since several entities and attributes can be added as required.
# Habitat Loss and Other Threats to the Survival of _Parnasius apollo_ ([PERSON], 1758) in Serbia
## 3 Results
### Temporal Trends in Vegetation Types and Their Association with Butterfly Presence
apollo_ are statistically significant, we conducted Mann-Whitney U tests on percentage cover values for three key vegetation categories across all sites and years (2015-2024). The results revealed highly significant differences across all variables:  
* Exposed soil was substantially lower at butterfly sites (mean: 3.72%) compared to non-butterfly sites (mean: 16.34%) (\(U=58\), \(z=-5.36\), \(p<0.000001\), \(r=-0.69\));
* Grass coverage was also significantly lower in butterfly habitats (mean: 9.00%) than in non-butterfly locations (mean: 39.89%) (\(U=42.5\), \(z=-5.61\), \(p<0.000001\), \(r=-0.72\));
* Shrub and tree cover was markedly higher in butterfly-inhabited sites (mean: 87.29%) relative to non-butterfly sites (mean: 44.03%) (\(U=779\), \(z=+5.94\), \(p<0.000001\), \(r=+0.77\)). These findings provide robust statistical support for the structural distinctiveness of butterfly habitats, particularly in terms of reduced bare ground and grass coverage and increased woody vegetation density. Boxplot visualizations of these differences are presented in Figure 8. Each panel displays the distribution of percentage cover for (a) Exposed Soil, (b) Grass, and (c) Shrubs and Trees. Butterfly habitats show consistently lower values for exposed soil and grass, and higher values for shrubs and trees. These patterns correspond to the statistically significant differences revealed by Mann-Whitney U tests. The median lines and interquartile ranges further emphasize the structural contrast between suitable and unsuitable habitats. Additionally, we compared vegetation structure between the two butterfly-inhabited locations (LOC1 and LOC3) to assess habitat heterogeneity within suitable sites. Mann-Whitney U tests indicated significant differences in exposed soil (\(U=91.5\), \(z=3.14\), \(p=0.0019\), \(r=0.70\)) and grass coverage (\(U=4\), \(z=-3.48\), \(p=0.0006\), \(r=-0.78\)), with LOC1 exhibiting lower values in both categories. Shrub and tree coverage did not differ significantly between the two locations (\(U=75.5\), \(z=1.93\), \(p=0.0586\), \(r=0.43\)). These results suggest that, while both sites support _P. apollo_, structural differences may reflect alternative configurations of suitable habitat conditions within the species' ecological niche. Figure 7: Temporal trends in shrub and tree cover (%) across six study locations (2015–2024), grouped by butterfly presence.
# Study on the Control of Torque Distribution of 4 WD Corn Harvester Operation Drive
## 2 Design and Modeling
### Torque Distribution Scheme and Control Principle  
The torque distribution scheme of the corn harvester is shown in Figure 1. The power is output from the engine and transmitted to the automatic transmission through the V-belt, then the automatic transmission inputs the power to the inter-axle torque distribution device, finally the torque distribution device to actively distribute the torque between the front and rear axles. The torque on the rear-drive axle is freely distributed to the left and right drive wheels through the bevel gear differential, and the torque on the front drive axle is actively distributed to both drive wheels through the front drive axle inter-wheel torque distribution control device.  
The drive torque control hardware mainly includes a torque controller, electronically controlled disc brakes, electronically controlled center differential, electronically controlled inter-axle differential, diesel throttle controller, and various sensors. Among them, the torque controller is the core. During operation, the torque controller managed the brakes, the electronically controlled center differential, and the diesel throttle controller to resolve torque distribution. When the drive wheel slip or a large speed difference between the front and rear drive axles, the torque controller will issue a corresponding control command after judging the status of the whole machine, control the brakes or inter-axle torque control device to prevent the drive wheels from slipping, and distribute the torque to other well-attached drive wheels, improve the traction of the whole machine. If the driving wheelsstill slip after control, it indicates that the engine output torque is too large, and the engine output torque needs to be adjusted by throttle control.
## 2 Wireless Multimedia Sensor Networks
### Large Volume of Multimedia Data  
The volume of multimedia sensory data in nature exceeds that of classical scalar sensory data on a scale of magnitudes. For WMSNs with which the scarcity of bandwidth is norm, transmitting large volume of multimedia data is quite an obstacle to overcome. First of all, methods to reduce the amount of data to be transmitted are necessary in order to cut down bandwidth requirement. Then mechanisms to maximize the utilization of the available bandwidth has to be explored. If possible, WMSNs have to adopt technologies that can potentially provide higher available bandwidths.
## 7 Conclusions  
This study considered the requirements of agile attitude maneuvers for multi-target observations using microsatellites. It developed a novel segmented power reaching law, and an attitude maneuver scheme was designed, effectively addressing the attitude maneuver control problem for multi-target observations by low-Earth-orbit microsatellites during short arc segments. The following conclusions were drawn from the research:  
1. The proposed piecewise power-law convergence law can converge to the sliding mode surface in finite time, and there exists a maximum value independent of the initial state at that time.
2. Increasing \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\) will increase the convergence rate of the system, but \(k_{3}\) and \(k_{4}\) have a greater impact on the convergence rate.
3. Compared to conventional power-law attitude control algorithms, attitude control algorithms based on the segmented power-law approach have a faster convergence time and higher control accuracy. Under the simulation conditions in this study, the convergence time was reduced by about 3 s, and the control accuracy was improved by about 70%.  
Formal analysis, L.L.; Investigation, X.Y.; Software, Y.L.; Validation, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study is available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 5 Computational Experiment
### Computational Results of the Panel Block Assembly Line
From Table 6, it can be observed that the LPT algorithm in the heuristic methods almost instantly yields scheduling results. However, compared to our model, the reinforcement learning algorithm, metaheuristic algorithm, and NEH algorithm require slightly longer computation times. Based on the results obtained from Tables 4 and 6, our algorithm achieves superior scheduling solutions with reduced computational time. Taking the case of a panel block size of 125 as an example, when evaluating the _makespan_ criterion, the ordering of algorithms based on time, from longest to shortest, is as follows: LPT, GA, NEH, TS, DDQN, PPO, and our algorithm. On the other hand, when considering the computational time criterion, the ordering from longest to shortest is as follows: TS, GA, NEH, PPO, DDQN, our algorithm, and LPT. It is evident that although LPT exhibits the shortest computational time, it results in the longest _makespan_. Conversely, our method achieves the shortest _makespan_ while also maintaining a computational time that is second only to LPT. Therefore, considering the comprehensive performance, our approach emerges as the optimal choice. Similarly, we calculated the differences in computation times between the baseline methods and our model, as presented in Table 7. These differences were then plotted in a line graph, shown in Figure 7. Based on Table 7 and Figure 7, it is evident that, apart from the LPT algorithm, all other baseline methods have longer computation times than our approach, and as the instance scale increases, the gap in computation times widens. However, although the LPT algorithm can yield instant results, its processing time for _makespan_ significantly exceeds that of our algorithm. Therefore, considering the overall \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Blocks**} & \multicolumn{2}{c}{**Heuristic**} & \multicolumn{2}{c}{**Metaheuristic**} & \multicolumn{2}{c}{**Reinforcement Learning**} & \multirow{2}{*}{**Ours**} \\ \cline{2-2} \cline{5-7}  & **LPT** & **NEH** & **GA** & **TS** & **DDQN** & **PPO** \\ \hline
25 & 0.00 & 2.12 & 4.36 & 7.51 & 1.45 & 1.53 & 1.21 \\
50 & 0.00 & 2.87 & 6.32 & 10.12 & 1.78 & 1.85 & 1.54 \\
75 & 0.00 & 4.35 & 7.94 & 13.10 & 2.77 & 3.02 & 2.32 \\
100 & 0.00 & 7.43 & 10.22 & 15.62 & 4.34 & 4.67 & 3.83 \\
125 & 0.00 & 10.52 & 12.53 & 18.33 & 5.68 & 6.04 & 4.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computation time of each algorithm on the panel block assembly line (s). Figure 6: Line graph depicting the differences in _makespan_ among the algorithms. performance, our model surpasses the other baseline methods and offers a more effective solution to the scheduling problem in ship panel block assembly lines.
## 5 Results
### Decoding Algorithm Improvement Experiment  
For optimizing the decoding algorithm, we applied the proposed dynamic adaptive decoding algorithm and conducted experiments on the three models (after weighted fusion). The test results are presented in Table 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Decoding Algorithm**} & \multicolumn{4}{c}{**WER(\%)**} \\ \cline{3-6}  & & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline \multirow{3}{*}{Mixformer} & attention & 4.60\% & 10.12\% & 4.91\% & 10.22\% \\  & ctc\_greedy\_search & 4.58\% & 11.85\% & 4.85\% & 12.06\% \\  & ctc\_prefix\_beam\_search & 4.54\% & 11.83\% & 4.83\% & 11.97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multi-model fusion test results. Table 9 presents the recognition performance of three models using the proposed decoding algorithm, while Table 10 compares it against traditional decoding methods, including attention, CTC greedy search, and CTC prefix beam search, highlighting the significant advantages of the adaptive attention fusion rescoring algorithm. This proposed algorithm dynamically integrates the efficiency of CTC with the global modeling capabilities of attention, effectively overcoming the limitations of traditional methods. Although attention decoding excels in capturing global dependencies, it struggles with complex alignments or noisy environments. Conversely, CTC decoding is computationally efficient but neglects contextual relationships, and both greedy search and prefix beam search often suffer from suboptimal accuracy in challenging scenarios. In contrast, the proposed decoding algorithm leverages a dynamic weighting mechanism to adaptively blend CTC's efficiency with attention's contextual understanding. This synergy enhances robustness in noisy conditions, delivering notable performance improvements. For instance, on noisy datasets such as Dev-Other and Test-Other, the proposed algorithm achieves significant reductions in WER. Specifically, on the Test-Other dataset with the U2++_Conformer model, WER decreased from 12.47% with CTC prefix beam search to 10.08%, a reduction of 2.39%. This demonstrates the algorithm's ability to accurately capture complex, nonlinear features in speech signals. In summary, the novel dynamic adaptive decoding method proposed in this paper effectively combines the strengths of both CTC and attention mechanisms, merging the advantages of both decoding algorithms. This approach improves decoding efficiency while ensuring recognition accuracy. By replacing linear functions with nonlinear ones, the method more effectively captures the complex nonlinear features in audio data. Additionally, by introducing a penalty function mechanism, it limits the model's reliance on a single type of decoding output. Ultimately, this method significantly enhances the accuracy and robustness of speech recognition, providing new insights for the advance
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Impact of Varying Number of Nodes and Varying Network Radius on Throughput  
Figure 12a-e shows the number of received packets at sink while implementing UWDAR, BTM, EBET and EEBET, respectively. These results are obtained at different network radii (ranging from 1 km to 5 km) while varying the number of nodes between 80 and 160 for each network radius. In these results, initially, the packet reception rate shows an increasing behaviour and then a decreasing behaviour. The increasing behaviour is due the added number of nodes in the network field as we go from 80 to 100 nodes. From 100 to 160 nodes, the packet reception rate decreases because the added nodes contend for channel to deliver their packets to the sink. This scenario results in increased congestion at the sink which increases the chances of packets' collision at the sink and high packet collision rate means decreased number successfully received packets at the  
Figure 11: Impact of number of nodes and network radius on network lifetime.  
sink. It is evident from Figure 12a-e that our proposed protocols (EBET and EEBET) show better performance than the existing ones (BTM and UWDAR) due balanced transmission mechanism and linear programming based optimization. The EEBET is more scalable than the rest of the protocols due to global balanced transmissions.  
Figure 12: Impact of number of nodes and network radius on throughput.