esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 4 Experiment and Results
### Comparison of Mask R-CNN and BlendMask  
For a learning rate (LR) of 0.01, the distribution of loss values for Mask R-CNN is shown in Figure 5.  
The experimental results show that the loss values are highly unstable, ranging from 0 to 0.0002. In the context of deep learning models, training processes utilize loss values  
\begin{table}
\begin{tabular}{l} \hline \hline
**Common Parameters** \\ \hline MODEL: \\ BACKBONE: \\ NAME: “build\_resnet\_fpn\_backbone” \\ RESNETS: \\ DEPTH: 50 \\ OUT\_FEATURES: [\"res2\", \"res3\", \"res4\", \"res5\"] \\ FFN: \\ IN\_FEATURES: [\"res2\", \"res3\", \"res4\", \"res5\"] \\ ANCHOR\_GENERATOR: \\ SIZES: [[32], [64], [128], [256], [512]] \# One size for each in feature map \\ ASPECT\_RATIOIS: [[0.5, 1.0, 2.0]] \# Three aspect ratios (same for all in feature maps) \\ ROI\_HEADS: \\ NAME: “StandardROIHeads” \\ IN\_FEATURES: [\"p2\", \"p3\", \"p4\", \"p5\"] \\ SOLVER: \\ IMS\_PER\_BATCH: 2 \\ STEPS: (210000, 250000) \\ MAX\_ITER: 270000 \\ WEIGHT\_DECAY: 0.05 \\ BASE\_LR: 0.001 \\ INPUT: \\ MIN\_SIZE\_TRAIN: (640, 672, 704, 736, 768, 800) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of Mask R-CNN, BlendMask, and SWIN.  
Figure 5: Loss values for Mask R-CNN (learning rate: 0.01).  
as an indicator of performance, with lower values being better. High loss values could indicate inaccurate model predictions or discordance between the input data and the neural network's architecture. Conversely, low loss values correspond to good predictive performance, indicating a model that performs with greater accuracy on given tasks.  
For LR: 0.01, Figure 6 presents the loss value distribution for BlendMask.  
These experimental results show that after 350,000 iterations, the loss values gradually converged to 0.0001 or lower. Moreover, these results not only exhibit more consistency but are also generally smaller than the prior findings from Mask R-CNN. The rapid change in loss value depending on the number of repetitions in the graph occurs due to the learning rate. The learning rate determines how much the model adjusts the weights at each iteration. If the learning rate is too large, the model may miss the optimal point, and if the learning rate is too small, it may take a long time to reach the optimal point. In these cases, the learning rate can be adjusted to stabilize the change in loss values.
# Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications
## 5 Conclusions  
This manuscript introduces the concept of an articulated soft robot with two inflatable links and three degrees of freedom. Its lightness and capability to be contained in a relatively small package when deflated, makes the robot suitable for space applications.  
The prototype of the inflatable link, made of PVC-coated fabric, was tested and statically characterized, using a linear pseudo-rigid body model. The model proposed is considered valid until the pre-stress point, when due to the pressure, the fabric walls are stiffened, making it possible to thereafter consider the link as a beam. Results evidence that the validity limits of the model depend on the inflation pressure, while this value does not influence the stiffness of the link until it remains in the 10-60 kPa range.  
Recurrent algorithms for the pose and load estimation and to build the inverse kinematics of the manipulator were developed. Data from the characterization tests of the link prototype were considered and implemented in the robot model to evaluate the deformations due to the inflatable nature of the links. The presented methodology, based on an elastostatic approach, can calculate the sets for motor joint rotations, compensating the link deformations, for precise trajectory planning.  
This work represents a preliminary analysis to be further developed. A dynamic model and more advanced control system of the robot is under development, as well as the design of the prototype of the full robot.  
Conceptualization, M.T., M.G., P.P. and S.M.; methodology, M.T., M.G., P.P. and S.M.; software, M.T., M.G. and P.P.; validation, M.T., M.G., P.P. and S.M.; formal analysis, M.T., M.G., P.P. and S.M.; investigation, M.T., M.G. and P.P.; resources, S.M.; data curation, M.T., M.G. and P.P.; writing--original draft preparation, M.T., M.G. and P.P.; writing--review and editing, M.T., M.G., P.P. and S.M.; visualization, M.T., M.G., P.P. and S.M.; supervision, S.M.; project administration, S.M.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
## 3 Results
### Holographic Monitoring of Cell Behavior in Response to GSH  
Label-free imaging cytometry using the HoloMonitor microscope was used for real-time monitoring of 13s24 cells. Among the 30 physical and behavioral parameters analyzed, the 4 most representative parameters significantly modulated by GSH are presented and discussed (Figure 2). Cell surface occupancy represented by cell area increased under control conditions during the 24 h recording (Figure 2A). This increase was affected in the presence of GSH 2.5 and 5 mM in a dose-dependent manner with a most significant difference after 18 h (Figure 2A). Inversely, the average optical volume markedly increased for cells exposed to GSH compared to control (Figure 2B). Optical volume increased during the first 12 h and then remained stable. Interestingly optical volume of cell changed according to the GSH concentrations. Concentrations of 1 and 2.5 mM GSH transiently increased optical cell volume from 10 to 15 h of recording. No significant difference was observed between 1 and 2.5 mM GSH. The increased optical volume may be explained by the increase in maximal optical path length of cells (i.e., the distance that light travels through cells, Figure 2C). Regarding cell behavior, only cell motility was significantly affected by 2.5 and 5 mM GSH, with a progressive effect observed over 24 h (Figure 2D). In conclusion, holographic analyses show significant effects of GSH 2.5 and 5 mM on cell phenotype (size) and motility.  
Figure 1: Dose-dependent effects of GSH on 13s24 olfactory neuroblasts. (**A**) Real-time evolution of cell index in response to increasing concentrations of GSH. Cell index was normalized at the time of treatment. Data are presented as mean delta cell index \(\pm\) SEM during time (n = 6). (**B**) Linear regression curve of mean delta cell index \(\pm\) SEM, 8 h after exposure to GSH (n = 6). (**C**) Phase contrast images of 13s24 cells 8 h after treatment at objective \(\times\)4 and \(\times\)20.
## 4 Numerical and Experimental Tests
### Numerical Simulation  
For the pitch axis of the MAV, the simulation is conducted to evaluate the step response performance using the data-based dynamics model in [27]. It is illustrated from Figure 3a that the proposed strategy can achieve good dynamic performance within a short period of time even without any prior information about the behavior of the MAV. As shown in Figure 3b,c, the response of the model can match rapidly and approximately with the real response even within the first period of the square wave signal; the tracking error can remain in a bounded range for the aggressive maneuvers, which demonstrates the improved tracking accuracy in the simulation. The outputs of the roll axis caused by the coupling among axes, shown in Figure 3d,e, also remain bounded. Therefore, the proposed strategy can improve the control qualities of the MAV. Meanwhile, the parameter estimation algorithm has a good convergence property and achieves a guaranteed model reference tracking performance.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 2 The Navigation Filter
### Observation Model
#### 2.2.2 Magnetic Field Measurement Model  
The earth's magnetic field in the local frame is obtained from the magnetometer measurement \(\mathbf{\bar{B}}^{b}\) as follows  
\[\mathbf{\bar{B}}^{l}=\mathbf{R}_{b}^{l}\mathbf{\bar{B}}^{b} \tag{17}\]  
The magnetometer is supposed to be calibrated so that the measurement error is dominated by the sensor noise \(\mathbf{\eta}_{B}\) as  
\[\mathbf{\bar{B}}^{b}=\mathbf{B}^{b}+\mathbf{\eta}_{B} \tag{18}\]We chose this model for the sake of simplicity. Note that the Earth's magnetic field measured by a magnetometer is distorted, in addition to the sensor errors and their misalignment with the body frame axes, by the perturbations due to the ferromagnetic material composing the vehicle and by the current induced by the rotation of the metallic vehicle. In practice, those errors can be calibrated [21; 22; 23; 24] and the residuals will add to the sensors bias and scale factor errors. A model that includes the magnetometer bias and scale factor in the filter can be found in [25].  
For small attitude errors \(\delta\epsilon\), \(\hat{\mathbf{R}}_{b}^{l}\) can be written as:  
\[\hat{\mathbf{R}}_{b}^{l}=(I_{3}-(\delta\epsilon\times))\mathbf{R}_{b}^{l} \tag{19}\]  
Inserting (18) and (19) into (17) yields  
\[\widetilde{\mathbf{B}}^{l}=\mathbf{B}^{l}+\left(\widetilde{\mathbf{B}}^{l}\times\right) \delta\epsilon+\mathbf{R}_{b}^{l}\mathbf{\eta}_{\mathbf{B}} \tag{20}\]  
The earth's magnetic field error in the local frame \(\Delta\mathbf{B}^{l}=\widetilde{\mathbf{B}}^{l}-\mathbf{B}^{l}\) is therefore  
\[\Delta\mathbf{B}^{l}=\left(\widetilde{\mathbf{B}}^{l}\times\right)\delta\epsilon+\mathbf{ R}_{b}^{l}\mathbf{\eta}_{\mathbf{B}} \tag{21}\]  
The reference Earth's magnetic field in the local frame, \(\mathbf{B}^{l}\), can be obtained for instance from the World Magnetic Model [26].
# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
## 3 Particle Swarm Algorithm  
Particle swarm optimization (PSO) is a popular intelligent optimization algorithm [24] whose origins originate from the research on the predatory behaviors of birds. The PSO algorithm is similar to the genetic algorithm in that the solution of the objective function needs to be initialized randomly and optimized by iteration, without the crossover and mutation of chromosomes. Each particle is abstracted as a bird in the search space, which represents the solution of the optimization problem. The particle has two attributes: speed and position. The former represents the speed of movement and the latter represents the direction of movement. Substituting particle position information and velocity information into the fitness function to be optimized, the fitness value can be obtained. In the process of optimization, the particle determines its next motion through its own flight experience (individual extreme value) and group experience (group extreme value).  
In a \(D\)-dimensional search space, the particle population size m, particle position \(x_{i}=(x_{i1},x_{i2},\cdots,x_{iD})\), and velocity \(v_{i}=(v_{i1},v_{i2},\cdots,v_{iD})\) are initialized, where \(D=1\), \(m=50\), range of position \(X\) is [0, 255], and the range of velocity \(V\) is \([-10,10]\). Particle \(i\) individual extremes \(p_{i}=(p_{i1},p_{i2},\cdots,p_{id},\cdots,p_{iD})\) and Global Extremums \(p_{g}=(p_{g1},p_{g2},\cdots,p_{gd},\cdots,p_{gD})\)\(p_{i}\) is the optimal position currently searched by the particle, and \(p_{g}\) is the optimal position currently searched by all of the particles. Over the course of each iteration, particles update their velocity and position according to the following equation:  
\[v_{id}^{k+1}=\omega v_{id}^{k}+c_{1}r_{1}(p_{id}-z_{id}^{k})+c_{2}r_{2}(p_{gd }-z_{id}^{k}) \tag{7}\]  
\[x_{id}^{k+1}=x_{id}^{k}+v_{id}^{k+1} \tag{8}\]  
where \(i=1,2,\cdots,m\), \(d=1,2,\cdots,D\), \(k\) is the number of iterations, and \(r_{1}\) and \(r_{2}\) are random numbers between \([0,1]\); these two parameters are used to maintain the diversity of the population. Furthermore, \(c_{1}\) and \(c_{2}\) are learning factors. Take \(c_{1}=c_{2}=2\), inertia weight \(\omega\) reflects the ability of particles to inherit the previous velocity, with a linear decrement method, as follows:  
\[\omega=\omega_{max}-\frac{\omega_{max}-\omega_{min}}{k_{max}}\times k \tag{9}\]where \(\omega_{max}=0.9\), \(\omega_{min}=0.4\), \(k\) is the number of iterations, \(k_{max}\) is the total number of iterations, and \(k_{max}=100\).
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
## 4 Numerical Simulations
### Nominal Simulation  
In the nominal simulation, the guidance system is tested without the navigation solution, i.e., assuming perfect information in Equation (8). Figure 2 shows that the displacement along \(x\) and \(y\) go to zero at the end of the simulation. The obtained miss distance is indeed very small at 5 cm. It is worth noting that a distance smaller than 1 m is deemed adequate to start a docking maneuver between two small satellites [29].  
The difference between the velocity of the two spacecraft is shown in Figure 3. It can be seen that the two velocities are aligned in both directions after the application of the \(\delta\mathbf{v_{2}}\) command of Equation (8) at the end of the simulation. Having nulled the difference in position and velocity between the two spacecraft in the orbital plane, the nominal simulation can be, therefore, considered successful. The out-of-plane motion, in fact, is null in this case, as the considered orbits are Keplerian and no perturbations have been taken into account.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Quantity** & **Value** & **Quantity** & **Value** & **Quantity** & **Value** \\ \hline \(\sigma_{r}\) & 500 m & \(\sigma_{\xi}\) & \(1^{\circ}\) & \(\sigma_{\phi}\) & \(1^{\circ}\) \\ \hline \(\sigma_{\varphi_{r}}\) & \(1\,\mathrm{m/s}\) & \(\sigma_{\varphi_{b}}\) & \(1\,\mathrm{m/s}\) & \(\sigma_{\varphi_{b}}\) & \(1\,\mathrm{m/s}\) \\ \hline \(\sigma_{\mathrm{ther}}\) & \(1^{\circ}\) & \(q_{0}\) & \(5\times 10^{-7}\) & \(\Delta t_{tot}\) & 600 s \\ \hline \(\Delta t_{F}\) & 0.25 s & \(\Delta t_{S}\) & 0.25 s & & \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 2: Position errors in the \(x\) and \(y\) directions.
## 3 The Feature Selection Approach of Composite Faults
### Extraction of Composite Fault Features  
Feature extraction is a critical link in fault diagnosis. Through transformation, low-dimensional feature space is used to express the original high-dimensional feature space. In this case, the most representative and effective features are obtained. When constructing composite fault feature vectors, to achieve more comprehensive information for the extracted composite fault features, we extract 20 statistical features from three perspectives of time domain statistics, frequency domain statistics and entropy value. The calculation formulas and meanings are depicted in Tables A1-A4 of Appendix A.  
Time domain statistics are the statistical value of vibration signals' time domain parameters and describe how vibration signals change with time. Such statistics include dimensioned and dimensionless parameters. We choose seven dimensioned parameters, including the peak value, peak-to-peak value, average amplitude, root mean square, square root amplitude, variance and standard deviation and record them as fea1-7, respectively. We also select six dimensionless parameters of crest factor, impulse factor, margin factor, shape factor, kurtosis factor and skewness and record them as fea8-13, respectively [30,31].  
Frequency domain statistics are the statistical value obtained by performing various operations on signals within the frequency domain after the Fourier transform. Such statistics show the changes in the frequency components of vibration signals. When rolling bearings fail, the frequency components of collected vibration signals change accordingly. Therefore, to obtain features in the frequency domain representative of the faults, we choose five frequency domain statistics of centroid frequency, mean square frequency, root mean square frequency, frequency variance and frequency standard deviation and record them as fea14-18, respectively [32].  
Entropy is an indicator to measure the complexity of time series. For rolling bearings, when an unknown fault occurs, the periodic shock signal would lead to an increase in the ordered component of the vibration signal, resulting in a corresponding change in the entropy value. We choose two entropy indicators of power spectral entropy and singular spectral entropy and record them as fea19 and fea20 [33].
## 5 Discussion  
The multimodal design of the dataset, integrating self-reported responses with physiological signals, enabled a nuanced analysis of emotional states within VR environments. By assembling a diverse cohort, we achieved a balance between experimental control and representativeness, enhancing the generalizability of our findings.  
The incorporation of biosignal analysis methodologies, particularly electrocardiography (ECG) and galvanic skin response (GSR), was crucial in understanding the physiological underpinnings of emotional states. The application of the discrete wavelet transform (DWT) on ECG signals revealed critical time-frequency attributes, while the GSR readings provided insights into aspects of physiological arousal and emotional reactions.  
The deployment of advanced ensemble machine learning techniques, particularly boosted trees, enabled us to achieve notable precision in discerning emotions within VR contexts. The robust performance metrics of the model, including accuracy, precision, recall, and the F1 score, underscored its efficacy in accurate emotional categorization, while minimizing erroneous classifications. The adoption of 5-fold cross-validation further reinforced the model's dependability and extrapolative power.  
These findings have significant implications for the evolution of emotionally responsive VR technologies. The capacity for real-time emotional adjustment has the potential to dramatically enrich immersive quality and user engagement in VR experiences, with potential utility in entertainment, healthcare, education, and therapeutic domains. Future investigations could explore the refinement of the synergy between biosignal analysis and machine learning to enhance the emotional acuity of VR systems. The comparison of our findings with existing literature, outlined in Table 3, highlights the superior accuracy of our model, marking a significant advancement in the field.  
The classification errors observed in our model point to a few critical challenges in emotion recognition using physiological signals. Emotions that exhibit similar physiological markers, such as those with similar arousal levels, are more likely to be misclassified. This highlights the need for more discriminative features or additional biosignals to improve classification accuracy.  
The incorporation of EEG data, for instance, could offer a substantial improvement in the model's ability to distinguish between closely related emotional states. Similarly, the introduction of advanced signal processing techniques, such as frequency-domain analysis and entropy measures, could yield further improvements by capturing more subtle distinctions in physiological signals.  
Data augmentation techniques, aimed at addressing the imbalance in emotional categories, could also bolster performance, particularly for underrepresented emotions. Furthermore, deep learning models, such as LSTM networks, offer a promising avenue for improving real-time emotion classification in VR by effectively handling the temporal dynamics of physiological signals.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
###### Abstract  
Due to the increasing popularity of communication devices and vehicles, the channel environment becomes more and more complex, which makes conventional channel estimation methods further increase the pilot overhead to maintain estimation performance. However, it declines the throughput of communication networks. In this paper, we provide a novel two-stage based channel estimation method by using generative adversarial networks (GANs) to handle this problem in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the first stage aims to learn the mapping from a low-dimensional latent variable to the real channel sample. During the second stage, an iterative algorithm method is designed to find the optimal latent variable by matching the pilot channels of a real channel and generated channel. Then, the data channels are recovered based on the learned mapping relationship between the latent variable and the real channel sample. The simulation results show that our proposed method can achieve a performance gain of more than 2 dB with a pilot reduction by 75% when SNR is 10 dB, by comparing with the widely used Wiener filter interpolation method. In addition, as the low-dimensional latent variable can be obtained simultaneously, it can also be used for reducing the feedback overhead.  
channel estimation; generative adversarial networks +
Footnote †: journal: Applied sciences
## 4 The Trust Evaluation Process
### Trust Calculation for One-Hop Neighbor Nodes  
In this subsection, the trust relationship between one-hop neighbor nodes is established by using the habit of human psychological cognition for reference, in which direct experience is the priority for evaluation and judgement. For example, in social network, if two people are friends or they are very familiar with each other, they can directly get assessment on each other's trust level based on their daily communications. Otherwise, trust relationship between strangers needs recommendations from the third parties. Therefore, in the process of secure localization in UWSNs, if two neighbor anchor nodes have enough information for each other, that is there are enough trust evidences for evaluating trust relationship, we only need to calculate direct trust as final trust. Otherwise, only when the trust evidence is not sufficient for direct trust evaluation, neighbor nodes' recommendations are required. This not only limits the number of information exchange between neighbor nodes, reduces the communication overhead and the complexity of the algorithm, but also greatly improves the accuracy of trust evaluation, and avoids the impact caused by malicious nodes in the process of trust recommendation, so that the trust evaluation between one-hop neighbor nodes is more true and reliable.  
In the process of trust calculation for one-hop neighbor nodes, there are two main types of trust evaluation: direct trust evaluation and recommendation trust evaluation, as shown in Figure 4. If an anchor node **a** wants to obtain the trust value of another anchor node **b**, the evaluated anchor node **b** is named as a target node. The evaluating anchor node **a** which is responsible for trust evidence collection and trust evaluation is named as a sponsor node. Based on communication behaviors between anchor nodes, a sponsor anchor node can assign a target anchor node with different trust values. If there are direct communications between anchor nodes, direct trust is calculated. Otherwise, recommendation trust can be computed based on other anchor nodes' recommendations. The anchor node which is selected as a trusted third party to provide recommendations is named as a recommender, just like {m\({}_{1}\),m\({}_{2}\),m\({}_{3}\), \(\cdots\)} in the Figure 4.