esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Sound and Image Features in ASMR on Emotional and Physiological Responses
## 4 Discussion
### Induced Emotions
#### 4.1.3 Physiological Perspective: ECG  
Heart rate variability (HRV) analysis revealed that the LVHR group generally induced a higher level of relaxation compared to the HVLR group. The mean values of SDNN (a time-domain measure) and total power (a frequency-domain measure), both indicators of autonomic nervous system balance, were higher when participants watched LVHR videos. These higher values indicate greater HRV variability, which is associated with a healthier, less stressed physiological state [24]. This suggests that LVHR videos are more effective at promoting relaxation and reducing stress.  
HRV data across frequency bands showed that both low-frequency (LF) and high-frequency (HF) components increased after viewing ASMR videos in both groups. However, the increase was significantly more pronounced in the LVHR group, consistent with the higher total power observed. Additionally, the value of ln HF, reflecting parasympathetic nervous system activity, was higher in the LVHR group, suggesting a greater state of rest and stress reduction [24]. RSA, another indicator of parasympathetic activity, also showeda significant increase in the LVHR group compared to the HVLR group, further indicating enhanced parasympathetic activation.  
Both groups exhibited increased HRV variability following ASMR stimulation, which is consistent with previous research demonstrating that ASMR enhances HRV and leads to relaxation and stress reduction [13; 14; 38]. However, the degree of this effect varied based on the type of ASMR stimulus, supporting the study's hypothesis.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Experimental Results and Analysis
#### 4.4.2 Experiments on Multi-Object Dataset  
In order to evaluate the effectiveness of ODGNet for grasp detection in the clutter scenes, it was tested on a multi-object dataset [13]. The multi-object dataset consists of 97 RGB-D images, each containing at least three objects, each labelled using multiple rectangular boxes, and each object is previously unseen. The GR-ConvNet algorithm is also used as a comparison group, and the specific detection results are shown in Figure 10.  
In Figure 10, there are three scenes. For the GR-ConvNet, in the first scene, although it generates some better grasping rectangles, its grasping area is too large in the grasping quality map; in the second and third scenes, the GR-ConvNet does not generate suitable grasping rectangles for some objects, the grasping quality scores are too low in the grasping quality maps, and the grasping regions of the objects are not extracted.  
For ODGNet, on the one hand, since ODGNet utilizes multi-scale features, in the first scene, the generated grasping regions are accurate and suitable in size; on the other hand, since ODGNet uses ODConv and the attention mechanism, which enhance the feature extraction capability and the localization of the graspable regions, the graspable regions of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Splitting**} & \multicolumn{4}{c}{**Jaccard Index**} \\ \cline{3-6}  & & **0.25** & **0.30** & **0.35** & **0.40** \\ \hline GR-ConvNet [20] & IW (\%) & 97.7 & 96.8 & 94.1 & 88.7 \\ Ours & & 98.4 & 97.2 & 94.9 & 91.5 \\ \hline GR-ConvNet [20] & \multirow{2}{*}{OW (\%)} & 96.6 & 93.2 & 90.5 & 84.8 \\ Ours & & 97.8 & 95.5 & 94.4 & 91.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy at different Jaccard indices.  
Figure 10: Comparison of grasp detection results on multi-object datasets.  
all the objects are predicted well and good grasping configurations are generated in the second and third scenes.
## 4 Optimal Control Problem
### Constraints  
As we want to obtain feasible motions for an existing humanoid robot, boundary constraints need to be set according to the physical limits of the robot on:  
* Joint angles range,
* Joint velocities,
* Torques.  
Ground reaction forces and contact positions are checked according to the contact points of each phase by means of the contact points of each foot as described in Figure 3, modeled as equality and inequality constraints. Depending on the phase, the ground reaction forces have to be positive when the contact is established, and the positions have to be zero on \(z\), e.g., during RSS, the right foot reaction forces have to be positive and the foot flat on the floor, while the left foot positions have to be above the floor level to avoid penetration issues.  
Collision avoidance also represents a key constraint that needs to be enforced in motion generation for robots. In this work, it is modeled as a set of constraints based on geometric capsules, i.e., cylinders with rounded caps [36], as shown in Figure 4. The cylinders approximate the limits of each of the links of the legs, such as the upper leg, the lower leg and the foot, for both legs. The distance \(d(i,j)\) between the capsules \(i\) and \(j\) is used as a constraint to avoid collision between links, i.e., \(d(i,j)>0\), where \(i\) and \(j\) are couples of different links of the two legs.  
Orientation constraints are enforced on the feet and torso of the robot only at the start and end of the phases as equality constraints, such that after touchdown, the feet are straight aligned and the torso upright with respect to the world reference frame.  
As the stability criterion, we use the ZMP, which we ensure lies inside the support polygon, which is approximated by a polygon that approximates the shape of the foot during single-support phases and an enlarged polygon including both feet and the area between the feet during double-support phases, as depicted in Figure 5. The ZMP is computed from the ground reaction forces using the whole-body model of the robot.  
Periodicity constraints are enforced on the intermediate periodic step as depicted in Figure 2. We impose a one-step periodicity, i.e., at the end of the step, the state of the robot is mirrored with respect to the beginning, meaning that left and right legs have a mirrored configuration, as well as the torso, such that by mirroring the obtained periodic step, we can obtain a full sequence of multiple steps. The periodicity is imposed on states \(\mathbf{x}\) and controls \(\mathbf{u}\).  
The joint velocities are constrained to be zero at the beginning of the first phase and at the end of the last phase as equality constraints, to ensure that the motion starts from a complete stop and ends with a complete stop, i.e., \(\dot{\mathbf{q}}(0)=\mathbf{0},\dot{\mathbf{q}}(T)=\mathbf{0}\).
# Optimal Disintegration Strategy in Multiplex Networks under Layer Node-Based Attack
## 3 Experimental Analysis
### The Existing Disintegration Strategies
#### 3.1.2 Layer Node-Based Strategies  
In layer node-based strategies, we calculate the scores of layer nodes in each layer respectively and order all the layer nodes in all layers. Similarly, high layer degree (HLD) is a strategy where we remove the layer nodes according to their layer degree. The high layer degree adaptive (HLDA) strategy is also defined by the adaptive computation of the layer degree.  
Meanwhile, we introduce collective influence (CI) strategy to the layer node based attack [13]. The CI strength of a layer node \(v_{i}^{\alpha}\) is defined as:  
\[CI_{l}(i)=[k_{i}^{\alpha}-1]\sum_{j\in\partial Ball(i,l)}[k_{j}^{\alpha}-1] \tag{8}\]  
where \(\partial Ball(i,l)\) denotes the set formed by all the layer nodes in layer \(\alpha\) that are at distance \(l\) to node \(v_{i}^{\alpha}\). The integer \(l\) is an adjustable parameter and we set \(l=2\) in this paper. In each step, we also need to iteratively calculate the CI length of all the layer nodes and remove the one with the highest CI length.
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 6 Conclusions  
In this study, we introduced two essential modules: the correction compensation module and the adaptive cost aggregation module. The former learns and compensates for image correction errors, while the latter performs differentiated aggregation of costs based on the values of cost volume elements. Building on these modules, we proposed a reconstructed self-supervised stereo matching model. It surpasses state-of-the-art unsupervised methods and well-generalized methods, and demonstrates competitive performance compared to supervised models on the SCARED dataset. The visualization results show that our model avoids significant errors and noise more effectively than the baseline model. It achieves smoother disparities in textureless regions and reduces errors in areas with specular highlights. Our model exhibits robust capabilities in overcoming correction errors and addressing ill-posed features in images.  
Conceptualization, J.Z. and Y.S.; methodology, J.Z.; software, J.Z., B.Y. and X.Z.; supervision, Y.S.; validation, B.Y., X.Z. and Y.S.; investigation, J.Z., B.Y. and X.Z.; data curation, J.Z.; resources, Y.S.; project administration, Y.S.; writing--original draft, J.Z.; writing--review and editing, Y.S. 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 are available in article.  
Conflicts of Interest: Author [PERSON] was employed by the company China United Network Communications Group Co., Ltd. Shaanxi Branch. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Case Study--Goldeneye CCS Site
### Sensitivity Analysis
#### 3.6.2 Adding Gaussian Noise the Test Data Set  
We briefly assess the classifiers sensitivity by adding Gaussian Noise, \(\mathcal{N}(0,\sigma)\), to the test data set, before we predict by using the same trained model as above. The noise was added to the pre-processed time series of the test data set. We tested two cases, where we chose \(\sigma=0.01\) and \(\sigma=0.1\).  
The general observation for a relatively low level of noise (\(\sigma=0.01\)), is that time series with true label no-leak had a higher uncertainty related to its prediction and the predicted value of it being labeled as leak increased. That is the AUC has dropped from values near 1, see Figure 4, to values around 0.8, see Figure 11. The prediction of true leaks were improved, however at the cost of more false positives. With a noise level of 0.1, the majority of the time series were classified as leaks. This tells us that this classifier is sensitive to small and rapid changes in the data, for data where the true label is no-leak, which can lead us to two potential conclusions. Either rapid and relatively small changes is a potential indicator of a leakage, or the classification is just comprised due to the additional noise. There are studies using rapid changes as indicators of leaks, as described by [PERSON] et al. in [39]. This also highlights major drawbacks of deep-learning methods, i.e., it is difficult to interpret the model and to explain classification criteria. It is worth mentioning that if the noise also were added to the training data, the classifier might cater for this and be more robust against such changes.  
Figure 10: Mean detection probability plotted vs. the total area covered at specific threshold.
## 2 Proposed Topology and Principle of Operation
### Common-Ground Flying Capacitor  
The flying capacitor concept was first introduced in DC-DC converters in a type of inductor-less converter called the charge pump (switched capacitor) DC-DC converter [26]. The basic idea behind using a flying capacitor in single-phase transformerless inverters was illustrated in [27] and is shown in Figure 5, where the capacitor is charged from the input voltage supply during a specific time by connecting the \(C_{\text{FC}}\) positive terminal to point 1 and the negative terminal to point 3. Afterwards, the flying capacitor positive terminal is connected to point 2 and negative terminal to point 4 to release the flying capacitor from the input supply positive terminal and generate a negative output voltage source to supply the load.  
Compared with the previously mentioned flying capacitor transformerless inverter topologies, the proposed topology presents a flying capacitor that can be charged at all times from the PV panel input, despite the inverter operation state, due to introduction of a buck-boost converter, as illustrated in Figure 6. Therefore, it requires only one switch to connect or disconnect the flying capacitor negative terminal, whereas the positive terminal is connected directly. This scheme allows the flying capacitor to charge continuously at a high frequency rate. Consequently, this leads to a smaller capacitor compared with other topologies that charge \(C_{\text{FC}}\) during the positive power cycle or freewheeling state (zero voltage state), but not during the negative power cycle (active). Moreover, this limits the charging inrush current flow into the flying capacitor due to the existence of the buck-boost inductor.  
Figure 5: Illustration of flying capacitor operation.  
Figure 6: The proposed flying capacitor circuit.
## 3 Results  
TMF elevation was technically successful in all specimens of the adult human cadaveric heads and there were no procedure-related complications. The placement of both types of SER in the left cadaveric ears was technically successful. The proximal ends of the SERs were located 2-3 mm from the tympanic membrane. The mean (\(\pm\)SD) procedure time for TMF elevation with SERs was significantly shorter than that for TMF elevation without SERs (\(175.6\pm 10.3\) s vs. \(312.2\pm 23.4\) s; \(p<0.001\)) (mean difference, 136.5 s; 95% CI, 111.7-161.3 s). The mean (\(\pm\)SD) number of endoscopy lens cleanings during the procedure was significantly less in the SER group compared with the non-SER group (\(0.83\pm 0.75\) vs. \(5.16\pm 1.17\); \(p<0.001\)) (mean difference, 4.33; 95% CI, 3.07-5.59) (Figure 2).  
In the SER group, insertion of the endoscope into the EAC was relatively easy without any resistance or tissue contact by the lens during the TMF elevation compared with the non-SER group because of the expanded and straightened EAC (Figure 3).  
Figure 3: Endoscopic images showing the view at the pinna. (**a**) Endoscopic image showing hairs and irregular external auditory canal (EAC) anatomy. (**b**) Endoscopic image taken immediately after fully covered self-expandable retainer placement showing the expanded and straightened EAC.  
Figure 2: The number of endoscopy lens cleaning interventions (**a**, **c**) and procedure time (**b**, **d**) in the non-SER and SER groups. Data are mean difference with a 95% confidence interval.  
However, with the bare type of SER, hairs and foreign bodies in the EAC were visible through the wire mesh of the straight part of the SEMS, (Figure 4).  
The mean number of times the endoscopy lens was cleaned during the procedure was less in the fully covered SERs group (\(0.3\pm 0.56\)) compared with the bare SERs group (\(1.3\pm 0.57\)) without a statistical difference (\(p=0.101\)). There were no mucosal injuries after the procedure in any specimen.
# Behaviour of Steel Tubular Knee Joint in Aluminium Frames with Tension-Tie Element
## 2 Experimental Program
### Experimental Results  
#### 2.5.1 Load-Displacement Relationship  
The overall behaviour of all specimens is characterised by the load-displacement curves given in Figure 7, where the steel joints are marked with S1-S3. The displacements were recorded in the piston. Small drops can be spotted in the load-displacement curves due to the incremental increase in the load value. During every pause in the loading process, slight relaxation in the specimens occurred since loading was displacement-controlled and not force-controlled. It can be concluded that specimens S1 and S2 behaved similarly, while specimen S3 had a softer response after reaching 53 kN. This behaviour can be explained by the premature failure of the weld between the tensile bolt nut and the steel knee joint [3].  
The curves shown in Figure 7 are characterised in Table 1, where Fpl and \(\Delta\)pl stand for plastic resistance and the corresponding deformation of the joint, while Fu and \(\Delta\)u stand for ultimate resistance and ultimate deformation, respectively. The plastic resistance of the joint and the corresponding deformation are defined as values at the intersection between regression lines of the elastic and plastic region of the load-displacement curve (see methodology explained in Section 2.5.2).  
It can be seen from Table 1 that the mean value of Fpl is equal to 59.0 kN, while the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Fpl [kN]** & \(\Delta\)pl [mm] & **Fu [kN]** & \(\Delta\)u [mm] \\ \hline S1 & 60.0 & 11.0 & 86.0 & 31.2 \\ S2 & 64.0 & 13.0 & 84.0 & 30.8 \\ S3 & 53.0 & 9.50 & 72.7 & 23.6 \\ \hline Mean value & 59.0 & 11.2 & 80.9 & 28.5 \\ Std. Dev. & 4.55 & 1.43 & 5.86 & 3.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of experimental load–piston displacement curves.  
Figure 7: Force–displacement curves of the steel specimen measured in the piston.  
mean value of F\({}_{\rm u}\) is equal to 80.9 kN. It is also visible that measured values of F\({}_{\rm pl}\) are less scattered than those of F\({}_{\rm u}\) (4.55 < 5.86). Furthermore, the mean value of \(\Delta_{\rm pl}\) is 11.2 mm, while the mean value of \(\Delta_{\rm u}\) is 28.5 mm. The values of \(\Delta_{\rm pl}\) are significantly less scattered in comparison to the values of \(\Delta_{\rm u}\) (1.43 < 3.49).
## 3 Results and Discussion
### Random Forest Models Based on Feature Extraction
While external validation was performed, further testing on larger independent datasets is necessary to confirm the robustness of the model. Finally, while RF is a robust and widely used algorithm, comparing its performance with other machine learning techniques (e.g., support vector machines, neural networks) could provide a more comprehensive evaluation of the effectiveness of the method.
## 6 Simulation Results
### Energy Consumption  
In TS, the energy consumption is influenced by two important factors: the number of search attempts and the transmission redundancy. The number of search attempts influences the energy consumption in the search phase and the transmission redundancy impacts the energy consumption in the chase phase. Therefore, we firstly evaluate the total energy consumption and then exploit these factors respectively. We choose two parameters: the threshold value of trail age \(\varepsilon\) and the waiting time of the actor \(\tau_{A}\) to evaluate the performance and compare with ERS and XYLS.
## 2 System Description and Analysis
### ACV Model
#### 2.2.3 Air Momentum  
When the ACV is sailing, air must be continuously supplied to the cushion by four cushion fans. The air is accelerated to the speed of the ship, and the forces and moments generated by its momentum change. The forces and moments vectors are:  
\[\mathbf{\tau_{m}}=[X_{m},Y_{m},K_{m},N_{m}] \tag{12}\]  
and \(X_{m},Y_{m},K_{m},andN_{m}\) denote the projection of air momentum in four DOFs. They are:  
\[\begin{split} X_{m}&=-\rho_{a}\sum\limits_{j=1}^{4} Q_{j}V_{a}cos(\beta_{a}),Y_{m}=-\rho_{a}\sum\limits_{j=1}^{4}Q_{j}V_{a}cos( \beta_{a})\\ K_{m}&=-\sum\limits_{j=1}^{4}Y_{m}z_{fj},N_{m}=\sum \limits_{j=1}^{4}(Y_{m}x_{fj}-X_{m}y_{fj})\end{split} \tag{13}\]  
where \(Q_{j},j=1,2,3,4\) denote inlet flow of pressure fans. \((x_{fj},y_{fj},z_{fj}),j=1,2,3,4\) denote the coordinate of pressure fans in body-fixed frame.  
By merging similar items, we can obtain the air momentum vector function:  
\[\mathbf{\tau_{m}}=\mathbf{D_{m}}\mathbf{v}+\mathbf{W_{m}}, \tag{14}\]  
where \(\mathbf{D_{m}}\) is the constant damping matrix of the air momentum, \(\mathbf{W_{m}}\) is the wind disturbance matrix.  
\[\mathbf{D_{m}}=\left[\begin{array}{cccc}k_{mxu}&0&0&0\\ 0&k_{mnyv}&0&0\\ k_{mku}&0&0&0\\ k_{nmuu}&k_{mmv}&0&0\end{array}\right]\]  
\[\mathbf{W_{m}}=\left[\begin{array}{cccc}k_{mxu}&0&0&0\\ 0&k_{mnyv}&0&0\\ 0&k_{mky}&0&0\\ k_{mmu}&k_{mnyv}&0&0\end{array}\right]\left[\begin{array}{cccc}u_{w}^{b}\\ v_{w}^{b}\\ 0\\ 0\end{array}\right],\]  
where \(V_{w}^{b}=[u_{w}^{b},v_{w}^{b},0,0]^{T}\) is the wind speed vector in body coordinate frame. \(k_{mxu},k_{mxy},k_{mpu},k_{mmu},k_{mmv}\) are constant coefficients, and they are:  
\[\begin{split} k_{mxu}&=k_{mnyv}=-\sum\limits_{j=1}^{4}Q_{j}\rho_{ a}\\ k_{mpu}&=\sum\limits_{j=1}^{4}Q_{j}z_{fj}\rho_{a}\\ k_{mmu}&=\sum\limits_{j=1}^{4}Q_{j}y_{fj}\rho_{a}\\ k_{mmv}&=-\sum\limits_{j=1}^{4}Q_{j}x_{fj}\rho_{a}\end{split}\]
## 3 Implementation of RUL Prediction Based on TCNMABG
### Experimental Results  
The RUL prediction results of performing RNN, GRU, LSTM, BiLSTM, and TCNMABG are shown in Figure 12. Where (a) is the prediction result of the test set, and (b), (c), (d) is the prediction result of the training set.  
In order to illustrate the effectiveness of TCNMABG for predicting the RUL of rolling bearing, the error indexes MSE, RMSE, MAE, and R\({}^{2}\) are introduced to evaluate the accuracy of the proposed method. By comparing with RNN, GRU, LSTM, and BiLSTM, the results of TCNMABG are obtained by performing all the methods five times, which is shown in Table 4. It can be seen that the RUL prediction results of TCNMABG, the MSE, RMSE, MAE, and R\({}^{2}\) in the test set are 0.006753, 0.082175, 0.056021, and 0.926302, respectively. The results show that TCNMABG has an excellent performance in RUL prediction.  
Comparing the prediction results in Figure 12, the RNN model has the largest error in prediction results; GRU, LSTM, and BiLSTM have improved the traditional recurrent neural network to some extent and have a better ability to describe time series information, but in this experiment, the prediction results still have large errors and fluctuations. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Model** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline RNN & 0.011753 & 0.108411 & 0.065320 & 0.871729 \\ GRU & 0.028311 & 0.168260 & 0.134098 & 0.691013 \\ LSTM & 0.010972 & 0.104475 & 0.089342 & 0.880250 \\ BiLSTM & 0.007165 & 0.084649 & 0.058105 & 0.921797 \\ TCNMABG & **0.006753** & **0.082175** & **0.056021** & **0.926302** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison experiments of RUL prediction for bearing1_5 in XJTU-SY dataset.  
Figure 12: Comparison of XJTU-SY training set and test set prediction results and model effects.  
prediction results of the TCNMABG model can not only better characterize the degradation trend of the remaining useful life of the equipment but also have a good fit with the real RUL value, and the prediction of every bearing in the training set and test set is the most stable, without overfitting phenomenon. The prediction results in Table 4 show that compared with the other four mainstream methods, the MSE, RMSE, and MAE of the TCNMABG model on the test set of the XJTU-SY dataset decreased by 40.72%, 24.91%, and 28.34% on average, respectively, and the R2 increased by 11.51% on average.