esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Temperature Sensitivity of \(R_{\rm s}\)  
Previous studies have mostly focused on the relationship between \(R_{\rm s}\) and its related environmental factors, and Q\({}_{10}\) has been studied as an index to evaluate the temperature sensitivity of \(R_{\rm s}\). Moreover, some researchers have studied the environmental factors affecting Q\({}_{10}\)[2]. In the study site, the seasonal variations in the soil respiration sensitivity index, Q\({}_{10}\), showed a curve opposite to that of \(R_{\rm s}\) and \(T_{\rm s}\) with hysteresis. Despite considering the fact that Q\({}_{10}\) was controlled by the soil temperature, soil moisture [40], organic matter decomposition [41], soil physical and chemical properties and biotic factors such as vegetation cover and fine root mass [19], also varied depending on the location [13], the differences among monthly Q\({}_{10}\) values may mainly be controlled by the combination of soil moisture and soil temperature because of the relatively constant soil physical and chemical properties in study site.  
The Q\({}_{10}\) values for different temperature intervals indicated a negative response of Q\({}_{10}\) to \(R_{\rm s}\) and \(T_{\rm s}\) in accordance with previously published studies [1; 2; 40; 42]. The physical and bio-chemical process consisting of \(R_{\rm s}\) was constrained by lower temperatures; thus, an increase of temperature could significantly improve the soil respiration rate, resulting in a higher Q\({}_{10}\) at lower temperatures. However, under higher temperatures that had met the need of \(R_{\rm s}\), further increases in temperature did not improve \(R_{\rm s}\), resulting in a lower Q\({}_{10}\)[43]. The Q\({}_{10}\) acclimated to the soil temperature because of the sufficient soil moisture in the study site. The annual Q\({}_{10}\) values ranged from 1.84 to 3.78 with a mean value of 2.90 \(\pm\) 0.59, which were slightly higher than the global mean of 2.4 and range of 1.3 to 3.3 [44]. The temperature in the study site was much lower because of the high altitude, and the actual Q\({}_{10}\) value may have been slightly higher than the calculated mean value because of the missing data for the coldest days when the measurements could not be taken. Within the soil temperature range of \(-\)0.1 \({}^{\circ}\)C to 20.68 \({}^{\circ}\)C of the nine-year measured data, the Q\({}_{10}\) value based on all measured data was approximately 3.08, which was almost equal to the mean Q\({}_{10}\) value (2.90 \(\pm\) 0.59) of the annual Q\({}_{10}\). It should be noted that the Q\({}_{10}\) value was quite low under water stress in 2009, indicating that the response of \(R_{\rm s}\) to temperature was obviously inhibited by water stress when there was a low water supply. The R\({}_{10}\) value (3.01 \(\pm\) 0.27 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) in the study site was comparable to that of other studies, such as 1.27-2.90 for 11 sites with four types of land cover in a mountain area investigated by [PERSON] et al. [19] and 2.30-3.60 in a temperate deciduous forest studied by [PERSON] et al. [45].
## 3 Concepts and Models
### Graph Partitioning  
Graph partitioning (GP) provides a method for the recursive or parallel processing of graph-structured data, reducing the size of the data while ensuring that the partitioning has minimal impact. This has great significance for many distributed systems. In recent years, with the development of networks and IoT systems, graph partitioning has been playing a role in many fields [32].  
The goal of graph partitioning is divided into edge-cut and vertex-cut, which means that the vertexes or edges in a graph are divided into several balanced sets so that the edges or points between different sets are minimized. The graph partitioning mentioned in this thesis refers specifically to edge-cut, in which all vertices in a graph are divided into k disjoint subsets. An illustration of edge-cut graph partitioning is shown in Figure 4. The different colors of a node indicate which partition it belongs to. Gray edges indicate edges in different partitions and colored edges indicate edges in the same partition. Since nodes are randomly assigned to different partitions, there are many interpartition edges. When no graph partitioning is performed, Figure 3(a) shows a very terrible partition. After graph partitioning, Figure 3(b) shows that nodes are assigned to highly clustered partitions.  
We now describe the model in mathematical language. Suppose there is an input undirected graph with \(\mathcal{N}\) vertexes represented by an \(\mathcal{N}\times\mathcal{N}\) adjacency matrix \(\mathcal{A}\) composed of 0 and 1 to indicate connectivity and a given constant \(\mathcal{K}\) indicating the number of segmentations to find a color vector \(C\) of length \(\mathcal{N}\), which meets the following requirements:  
\[\mathcal{C}_{i}\in[1,\mathcal{K}],\forall i\in[1,\mathcal{N}] \tag{6}\]  
\[\max\left(\sum_{i=1}^{\mathcal{N}}[\mathcal{C}_{i}=x]\right)-\min\left(\sum_{ i=1}^{\mathcal{N}}[\mathcal{C}_{i}=y]\right)\leq\mathit{tolerance},\forall x,y\in[1, \mathcal{K}] \tag{7}\]  
Figure 4: An illustration of edge-cut. The color of a vertex represents which partition it belongs to. The gray links are interpartition edges and colored links are edges inside a partition: (**a**) shows a poor partitioning while (**b**) is good, as the vertexes are assigned into highly connected groups.  
where tolerance is the maximum acceptable difference between two different sets. Under the conditions above,  
\[min\sum_{i=1}^{N}\sum_{j=1}^{i}\mathcal{A}_{ij}\big{[}C_{i}\
eq C_{j}\big{]} \tag{8}\]  
Graph partitioning is a classic NP-complete problem, so it is usually difficult to find the optimal solution in a finite time. The current graph partitioning methods mainly include spectral methods, geometric methods, and heuristic methods. Considering the research problem in this thesis, that is, the number of nodes in satellite-UAV-integrated networks is far fewer than that in large-scale networks, it is a good heuristic solution to use the simulated annealing described above.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 4 Measurement Results
### Feasibility Test and Power Delay Profile
Although many studies assume Rayleigh fading channel environment, LOS channel effect has to be taken into account if we consider WAIC system where  
Figure 8: Feasibility test. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Locations** \\ \hline Region 1 & P0, P1 \\ Region 2 & P0, P2, P3 \\ Region 3 & P4, P5 \\ Region 4 & P6, P7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of communication regions. Figure 9: APDP results at various locations (20 MHz at 4.3 GHz). short-distance wireless transmission is preferred, as in our experiment. As we discussed, communication regions of intra-avionic sensors are limited based on their maximum transmit power. Accordingly, relay nodes have to be carefully installed at the intersection of communication regions (Table 3) in order to guarantee reliable wireless communications in the entire aircraft area. For example, intersection points between the main body and right/left wings are a proper place for relay nodes, as shown in Figure 10. (Note that the relay installation would mainly be determined by the targeted aircraft structure.) Additionally, we can utilize one of the existing interference mitigation/spatial diversity techniques at relay nodes to guarantee QoS (quality of service) [17; 18; 19].
# Advancing in RGB-D Salient Object Detection: A Survey
## 3 Attention Mechanisms in RGB-D SOD
### Attention Models in SOD  
In RGB-D SOD, attention mechanisms are crucial for enhancing the integration and processing of RGB and depth data. Recent studies have demonstrated the successful application of various attention models in the RGB-D SOD domain.  
**Unified unsupervised salient target detection via knowledge transfer**. [PERSON] et al. [43] discussed the role of attention networks in improving RGB-D SOD by enhancing cross-modal feature integration, highlighting the impact of self-attention and mutual attention models on performance. However, the computational complexity is high due to the feature fusion at different levels.  
**RGB-D salient target detection based on cross-modal and cross-level feature fusion**. [PERSON] et al. [11] employed an attention mechanism to enhance the different types of features that are derived from RGB and depth modalities, which helps to mitigate the problems caused by differences in modal quality and improved the accuracy of salient target detection. However, it overly relies on the performance of pre-trained models, making it difficult to transfer to different tasks. [PERSON] et al. [44] showed how different types of attention mechanism can be used effectively to synchronize feature representations between RGB and depth data to improve the overall detection process. [PERSON] et al. [45] used a context-based learning attention scheme to dynamically adjust the contributions of RGB and depth modalities, used an attention model to assess the quality and relevance of each modality, and optimized the fusion process to improve the robustness of cross-scene detection.  
**Deep perception attention networks distinguish salient objects**. [PERSON] et al. [46] introduced a depth-aware attention model, DSAM, which prioritizes depth information to augment RGB features for background suppression, and achieved superior results in distinguishing salient objects from complex backgrounds by using attention to selectively emphasize the depth features that are most reliably and consistently associated with RGB features.  
**Progressive attention network**. [PERSON] et al. [23] used an incremental learning strategy to progressively refine the integration of RGB and depth data using a cross-modal attention fusion module and an incremental decoder, with each stage focusing on increasingly fine-grained features. Progressive decoding strategies make feature fusion more detailed, enhancing detection accuracy, but they also increase model complexity and computation time.
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Review from 2015 to 2024
Diagnostics of engines and gearboxes were presented in [81; 82; 83; 84; 85], while the following publications [86; 87; 88; 89; 90] described pipelines and cutting tools. Works [91; 92; 93; 94; 95] addressed the topics of biometrics and wind turbine diagnostics, while [96; 97] focused on vibration analysis of induction motors and gear transmissions. The comprehensive review of studies from 2015 to 2024 unequivocally demonstrates the profound impact of machine learning on vibroacoustic diagnostics, marking a significant maturation of the field. Across a diverse spectrum of applications--ranging from the intricate fault diagnosis of ball bearings and industrial fans to the large-scale structural health monitoring of bridges and wind turbines--ML techniques consistently achieve remarkably high diagnostic accuracies, frequently exceeding 90% and, in several instances, reaching near-perfect classification rates. This widespread success, particularly with the increasing adoption of deep learning architectures such as Convolutional Neural Networks (CNNs) for image-based spectrogram analysis and Recurrent Neural Networks (RNNs) for time-series data, signifies a transition from theoretical exploration to the development of robust, practical solutions capable of addressing complex, real-world engineering challenges. A critical underlying trend in the field's evolution is the increasing sophistication of data handling, particularly through multi-sensor data fusion and automatic feature extraction. Studies consistently highlight that integrating information from diverse sources--such as vibration, acoustic, current, and optical signals--significantly enhances the reliability and accuracy of machine learning classifiers, especially in complex fault scenarios. This approach provides a more comprehensive diagnostic picture, mitigating the limitations of single-sensor systems. Concurrently, the proliferation of deep learning methods, notably CNNs and LSTMs, has enabled the automatic extraction of salient features directly from raw data, thereby streamlining the diagnostic pipeline and reducing the reliance on laborious manual feature engineering. These advancements collectively align with the principles of Industry 4.0, facilitating the development of highly reliable, automated, and real-time condition monitoring systems capable of integrating seamlessly with broader industrial digital ecosystems.
## 3 Results
### Relation of VQ with VOT, fo, and SPL
Voice SPL demonstrated weakly positive and highly significant correlations with all perceptual VQ parameters: OS (\(r=0.24\), \(p\leq 0.001\)); roughness (\(r=0.26\), \(p\leq 0.001\)); breathiness (\(r=0.25\), \(p\leq 0.001\)); and strain (\(r=0.26\), \(p\leq 0.001\)). SPL had higher values for more deviant VQ.
# Reactive Balance Control for Legged Robots under Visco-Elastic Contacts
## 1 Introduction
### State of the Art  
The most common approach is to neglect elasticity in the balance controller and simply assume that contacts are rigid. This method works fine for contacts that are not too soft and that are sufficiently well damped, as was demonstrated with a passivity-based controller on the Toro humanoid robot [11]. However, in general, making this hypothesis results in destabilizing oscillations [1]. Force feedback can then be used for damping these oscillations. This is usually done by using force feedback to modify the position references given to a high-gain joint position controller, leading to admittance-control schemes [5; 7; 9]. Although this approach proved to be successful in practice, its lack of theoretical foundations makes it challenging to tune and analyze in a robust and repeatable way.  
A crucial issue of admittance-control schemes is the resulting delay in the force tracking. To deal with it, two approaches have been proposed so far. The first one [3] is to prove that the controller is robust to these delays. The second one is to account for the delay by introducing a 1-st order low-pass filter in the dynamic model used by balance controller. Our analysis reveals that the force tracking delay is actually not an issue, so long as the admittance-control gains are properly set.  
Instead of relying on admittance control, another approach is to try to change the contact forces at the next time step [12] since they cannot be changed instantaneously. This approach exploits a model of the visco-elastic contacts for the controller design. However, as it mainly relies on contact damping, it cannot be applied if the damping is too low (see Section 2.1).  
Recently, the state-of-the-art Whole-Body Controller [13] has been extended to ensure consistency to terrain compliance [14]. In this approach the controller incorporated a soft-contact model and was connected with an online learning algorithm that estimates the terrain compliance. Though that work focused on the case of well-damped contacts, we focus here on the underdamped case.
# Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties
## 3 Results and Discussion  
### Model Validation  
Computational modeling helps to overcome the limitations of analytical methods such as simple geometries or non-realistic hypothetical problems. In this study, a finite element model (FEM) [31] is used to calculate the sound pressure field at each interface point. The accuracy of the FEM depends on a computationally feasible and sufficiently fine mesh selection. In COMSOL, the number of degrees of freedom (DOF) per wavelength must meet specific guidelines in order to ensure model accuracy and convergence. For second-order Lagrangian elements, a minimum of six cells per wavelength is required to accurately capture the wave properties. In this study, the following formula was used to calculate the cell size for meshing:  
\[h\leq\frac{\lambda}{n}=\frac{c}{nf_{max}}\]  
h represents the element size, n denotes the number of elements, and \(\lambda\) denotes the wavelength, \(f_{max}\) is the maximum frequency of bowel sounds and \(c\) is the speed of sound in different tissues. COMSOL automatically calculates the number of degrees of freedom required based on the element size.  
\[\Delta t\leq\frac{1}{20f_{max}}\]  
\(\Delta t\) denotes the time-step requirement, and \(f_{max}\) denotes the maximum frequency.  
In this study, we validated the accuracy of the proposed model by comparing the IWC values obtained from simulating bowel sound propagation with actual IWC data collected from clinical measurements [32]. The comparison results shown in Figure 3 indicate that the amplitude values of the two waveforms are nearly identical at most time points, especially at the peaks and valleys. The real IWC signals may exhibit minor noise-induced variations. This high degree of overlap indicates that the two waveforms are very similar in the time domain, i.e., the signals are strongly coherent. This comparison reinforces the validity of the mathematical model proposed in the literature for the generation of bowel sounds and confirms the robustness and accuracy of our propagation model in simulating the propagation of sounds in the abdominal cavity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tissue Type** & **Speed of Sound [29]** & **Density [30]** & **Acoustic Impedance** \\  & **(m/s)** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{2}\)/s)** \\ \hline Skin & 1613 & 1120 & 1,804,560 \\ \hline Fat & 1478 & 950 & 1,404,100 \\ \hline Muscle & 1547 & 1050 & 1,624,350 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The acoustic properties of different tissues.
## Key Rheological Models
### Damage rheological models  
In multi-field coupling conditions, the rheological behavior of rocks is often influenced by damage mechanics, particularly in scenarios where rock deformation leads to the development of fractures or microcracks. Damage rheology models aim to describe the evolution of damage and its effects on rock properties over time. These models are particularly useful for predicting long-term rock behavior in underground settings, where cumulative damage can significantly alter rock properties.  
Advances in rheological testing equipment and the integration of nonlinear components into traditional models have significantly improved our ability to describe the rheological properties of rocks. By continuing to combine experimental research, theoretical modeling, and numerical simulation, scholars are working towards developing multi-field coupled rheological models that can accurately guide engineering practices in deep underground environments. These efforts are critical for ensuring the safety and stability of geothermal projects, nuclear waste repositories, and other infrastructures that rely on the integrity of deep rock formations.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 4 Methodology
This means the proposed scheme incorporates symmetry in its design architecture, offering fair and equal opportunities for all nodes to perform consensus and block validation. Considering the adoption of Ethereum 2.0, this scheme offers a fair deal of symmetry in its consensus operations. In a real-world scenario, the selection of validators in Ethereum 2.0 to perform block validation is carried out based on their stake (i.e., the amount of cryptocurrency). From the perspective of novelty, it can be seen that the proposed study model introduces three types of nodes in the consensus block (i.e., slave, auxiliary, and master) that contribute to effective trust computation. The security design involved in the proposed system is also controlled by cost modeling, which includes multiple constraints on the specific design states of the network model. In this process, a better form of symmetry is maintained to ensure their impact is directly proportional to their proposed stake. At the same time, this scheme prevents the participation of any single node from gaining uneven control of the network. Apart from this, the triple role of nodes in the consensus mechanism presented in the proposed system with mobile sensors also ensures that there is always a functional network for supporting seamless communication with mobile sensors. The prime notion of this phenomenon is to offer a sustainable network that can withstand significant failures and attacks. In short, a better form of data integrity and consensus is maintained by the combined efforts of multiple nodes. Hence, better symmetry towards resiliency and network stability is presented in the proposed methodology.
## 3 Equalization Schemes
As explained in Section 2, the VCSEL shows different relaxation oscillation frequencies for all 12 possible pulse responses, so linear scaling of the coefficients is expected to improve the BER but leave significant performance boost unused. The digital sample-based topology [20] shown in Figure 7 uses its own set of coefficients for every possible set of pulse responses. Each of the four possible combinations of MSB and LSB are split into four lanes of UI-based delay chains. For every tap, only one state within the chains (i.e., Pre1_11,..., Pre1_00) is logically true. When the state of the main tap is different from one of the other taps, an edge is detected and the corresponding FFE coefficient is added. This topology offers several advantages. First, each of the 12 pulse response combinations can be optimized (almost) independently. They are not totally independent because the height of the peak is dependent on the Pre1 and Post1 tap coefficients of the level of the peak. Second, the proposed architecture avoids multipliers which are expensive in terms of hardware and delay. Since only one of the tap states is valid, all additions of one tap can be replaced by 16 entries of a LUT. Alternatively, the proposed filter is fully realized by a LUT using 24 inputs (4 states times 6 taps) resulting in 4\({}^{6}\) (i.e., 4096) LUT entries. Because of the complexity of the filter, a digital implementation is more energy-efficient than the analog counterpart. Using a CML approach, the adders would yield a large number of differential pairs in parallel creating an unrealistically high load. Finally, due to the digital nature of the proposed filter, this topology can also easily be extended to a larger amount of taps (at the added cost of complexity in the digital domain and hence larger power draw) as well as to handling PAM-6, PAM-8 or higher modulation formats.