esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Assessment
### DL-Based Diagnostic Assessments
This may be a major concern for cases where computational resources are a major decision-making criteria; however, since most state-of-the-art computers often feature high-end processing units, computational costs may not be a major concern. On the other hand, choosing a more shallow DL model architecture offers better computational efficiencies, as shown in the low computational costs associated with the DL models (except the FCN). From this perspective, one may be inclined to find an optimal balance between computational accuracy and computational costs, which realistically offers a better decision-making rationale. In that case, the 1D-CNN models--\(CNN64\), \(CNN64\), and \(CNN64\)--offer realistic alternatives to the FCN since they offer high test accuracies (as shown in Table 2), minimal false alarm rates (as shown in Figures 10a-c), and comparatively lower computational cost than the FCN. Moreover, the cost implications of combining both vibration sources are shown in Figure 11, whereby using the raw signals from both sources (\(SP_{-}FR_{R}\)) is more computationally expensive (13.71 min) than when the other combinations were employed--13.4 min for \(SP_{-}FR_{C}\), 13.13 min for \(SP_{-}FR_{C}\), and 12.58 min for \(SP_{-}FR_{R}\). Overall, the study reveals an improved diagnostic accuracy by the 1D-CNN and DNN models of about 1.3% and 5.71% reduced computational costs  
Figure 11: Computational cost assessment of the DL models on the different input/source data channels. The green bars (Avg cost) are the mean value of the computational costs of the DL models per input/source data channel.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 4 Experiments
### Steganalysis  
To thoroughly assess the security and detectability of the proposed steganographic method, this study employs four state-of-the-art deep learning-based steganalysis networks for validation: XuNet [47], YeNet [48], SRNet [49], and Yedroudj-Net [50]. Steganalysis aims to distinguish stego images from cover images, thereby evaluating both the imperceptibility and resistance to detection of the steganographic algorithm. Based on the results obtained from these four steganalysis tools, we plotted the Receiver Operating Characteristic (ROC) curve (see Figure 7) to analyze the trade-off between the false positive rate (i.e., misclassifying cover images as stego images) and the true positive rate (i.e., correctly identifying stego images). In Figure 7, the blue diagonal line represents the baseline of random guessing, which serves as a reference for comparing actual model performance. Generally, the closer the ROC curve is to this diagonal, the more the detection results approximate random guessing, indicating a higher level of imperceptibility for the steganographic scheme.  
To further validate the security of our method, we conducted comparative experiments with two advanced steganographic methods, LISO and FNNS. We employed the above four deep learning-based steganalysis networks to evaluate the steganographic security of different methods under an embedding payload of 1 bpp. For each steganographic method, we generated 7000 pairs of cover and stego images to retrain the steganalysis networks. As  
Figure 6: Model security analysis with histograms. The horizontal axis of a pixel histogram represents pixel intensity values, while the vertical axis indicates the number of pixels at each corresponding intensity value.  
shown in the figure, our method demonstrates significantly stronger robustness against detection compared to LISO and FNNS across all four steganalysis networks.
## 5 Real-World Interference Classification  
The purpose of the proposed test standards is not to propose a fixed set of threats to cover all types of signal, but instead to develop draft standards for testing receivers against interference that has been observed in the real world through the dedicated interference monitoring network. Using real interference threats that have been detected in the field allows interested parties (e.g., certification bodies, application developers, receiver manufacturers, etc.) to better assess the risk to GNSS performance during operations and to develop appropriate counter-measures. However, with many thousands of potential threats being detected by the monitoring network, it is impractical to test receivers against all detected threats. Therefore, an initial selection process has been done and a baseline set of 5 types of threats has been selected for inclusion in the draft test standard for receiver testing [9].  
In particular, each receiver is tested against the following 5 types of interference threats, described in Table 3: Wide swept frequency with fast repeat rate, multiple narrow band signals, triangular and triangular wave swept frequency, and tick swept frequency. Each of these threats is generated by the VSG replaying raw I/Q data captured in the field during a real event, if not mentioned otherwise. In Table 3, each type of threat is explained via an example plot with two figures. The left figure denotes the estimated power spectral density at the GNSS L1/E1 carrier frequency \(\pm\)8 MHz and the right figure represents the spectrogram of the perceived interference. As can be seen in the example plots, the spectrogram color map is defined so that the 'blue' color represents the least signal power, whereas the'red' color represents the most signal power.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constellation** & GPS + Galileo \\ \hline
**Number of satellites** & 10 (GPS) + 10 (Galileo) \\ \hline
**Centre frequency (MHz)** & 1575.42 \\ \hline
**Ionosphere model** & Klobuchar (default parameters) \\ \hline
**Troposphere model** & Saastamoinen (default parameters) \\ \hline
**Start time** & 01.02.2018-12:00:00 \\ \hline
**Duration (min)** & 30 (TTRP) / 60 (sensitivity test and baseline) \\ \hline
**GNSS signal power (dBm)** & \(-\)125 \\ \hline
**Interference power level for** & \(-\)35 \\
**’TTRP test method’ (dBm)** & \\ \hline
**Interference power range for** & [\(-\)120: 5: \(-\)60] \\
**’Sensitivity test method’ (dBm)** & [5: 5: 65] \\ \hline
**I/S range for ‘Sensitivity test method’ (dB)** & [5: 5: 65] \\ \hline
**Receiver location (Lat/Long/Alt)** & 60\({}^{\circ}\)N/24\({}^{\circ}\)E/30 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated scenario settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**C/N\({}_{0}\) threshold for satellite in Navigation Computation (dB-Hz)** & 25 \\ \hline
**Minimum elevation angle** & 5\({}^{\circ}\) \\ \hline
**Start mode** & Cold start \\ \hline
**Dynamic model** & Stationary \\ \hline \hline \end{tabular}
\end{table}
Table 2: Receiver configuration.
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 4 Experiment Studies and Result Analysis
### Static Palpation Experiments  
In order to accurately estimate the YM of the soft tissue phantom (with and without hard inclusions), static experiments are conducted, in which the robot is moved to a predefined indentation depth. After the robot arrives at the indentation depth, the robot keeps the position until the force data becomes stable, then the palpation force data with different indentation depths are recorded and the respective YM is calculated. Table 1 shows the detailed experimental data and YM calculated. The interaction force (static), indentation depth and YM are presented under different setups with indentation depths ranging from 3-10 mm. The results of the static experiments can be used to accurately reflect the Young's modulus of the soft tissue under different setups, which will also be used to verify the result of the proposed framework with force compensation. In this work, we mainly focus on the force measurement uncertainties caused by the dynamic robotic palpation procedure, and the static experimental results are used to verify the proposed compensation framework. From the results presented from Table 1, we noticed that the YM of soft tissue without hard inclusion is quite small. The one with a hard inclusion at a depth of 5 mm is the biggest, since the distance between the inclusion to the surface of the soft tissue is small, which provides a bigger interaction force during the palpation. Similarly, the YM of the one with a hard inclusion embedded at 8 mm depth is smaller than the one at the depth of 5 mm. It is interesting that the YM of the setup in which the hard inclusion is embedded at 10 mm depth is bigger than the one at 8 mm depth, but always smaller than the one at 5 mm depth. It might be caused by the position of the hard inclusion at 10 mm depth, as it is arranged in the middle of the soft tissue phantom and the YM might be affected by the tensions from both sides. Besides, the distribution of the material for soft tissue phantom might not be the same at each point of the tissue.
# 3D Avatar Approach for Continuous Sign Movement Using Speech/Text
## 3 Materials and Methods
### Generation of Sign Movement  
The generation of sign movements based on the input text is accomplished with the help of an animation tool called Blender [37]. The tool is popularly used for designing games, 3D animation, etc. The game logic and game object are the key components of the Blender game engine. We developed the 3D avatar by determining its geometric shape. The whole process for creating the avatar is divided into three steps. First, the skeleton and face of the avatar are created. In the second step, we define the viewpoint or orientation of the model. In the third step, we define the movement joints and facial expressions of the avatar. Next, we provide the sequence of frames that determine the movement of the avatar over the given sequence of words over time. Finally, motion (movement like walking, showing figures, moving hands, etc.) is defined by giving solid animation. The game engine was written from scratch in C++ as a mostly independent part and includes support for features such as Python scripting and OpenAI 3D sound. In this third module, we generate sign movements for the ISL sentence (generated in the second module). The entire framework of the movement generation of the avatar from the ISL sentence is described in Figure 5. For the generation of sign movement from ISL, initially, the animation parameters are extracted from the ISL sentence. Once the animation parameters are identified, the motion-list of each sign is performed using a 3D avatar model. In the proposed 3D avatar model, each movement is associated with several motions, and all such motions are listed in the \"motionlist\" file. A counter variable is initialized for tracking current motion. Each motion has its timestamps mentioning how long the action of different gestures will be performed. Each motion is generated by a specific motion actuator when some sensor event has occurred. The controller acts as a linker between the sensor and actuator. The conditional loop checks the maximum bounds of the number of motions, and it performs the motion one by one using a specific actuator (e.g., the actuator[counter]). The next valid motion is performed by incrementing the counter variable. If the value of the counter variable exceeds the number of motions in the \"motionlist\" file, then the counter variable along with the \"motionlist\" is reset to the default value, and the next movement will be performed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**English Sentence** & **ISL Sentence** \\ \hline I have a pen. & I pen have. \\ The child is playing. & Child playing. \\ The woman is blind. & Woman blind. \\ It is cloudy outside. & Outside cloudy. \\ I see a dog. & I dog see. \\ \hline \hline \end{tabular}
\end{table}
Table 3: English sentence–ISL sentence mapping.
## 3 Results and Discussions
### Electrochemical Behavior of TiO\({}_{2}\) and Ba/TiO\({}_{2}\)  
The reaction kinetics linked with DSSCs were investigated using EIS results. EIS spectra of TiO\({}_{2}\) and Ba/TiO\({}_{2}\) photoanodes under dark conditions with 0.6 V open circuit voltage are depicted in Figure 6. The EIS of DSSC was explained by three semicircles in the high, middle, and low frequency range, corresponding to the electrochemical output in platinized counter-electrode and electrolyte-interface (R\({}_{1}\)), WE/electrolyte (R\({}_{2}\)), and diffusion resistance of electrolyte (R\({}_{d}\)), respectively. The displacement of the graph from origin represents the series resistance due to the contact resistance of the cell (R\({}_{s}\)) [55]. Associated charge transfer in the electrolyte influenced the low frequency region, which was evident from the third semicircle. From the fitted data, the Ba/TiO\({}_{2}\) photoanode charge transfer resistance value was higher than the TiO\({}_{2}\) photoanode charge transfer resistance value. Barium doping increased the band gap value (which was also confirmed by optical analysis), and this reduces electron recombination in the Ba/TiO\({}_{2}\)/electrolyte interface. This behavior is attributed to the charge transfer value increment. The increment of charge transfer resistance was helpful to reduce charge recombination, which was evident for the photocurrent increment.  
Figure 5: SEM analysis of synthesized (**a**) TiO\({}_{2}\) and (**b**) Ba/TiO\({}_{2}\), and EDS analysis of synthesized (**c**) TiO\({}_{2}\) and (**d**) Ba/TiO\({}_{2}\) nanopowders.
# Investigation of Zero Moment Point in a Partially Filled Liquid Vessel Subjected to Roll Motion
###### Abstract  
Liquid-handling robots are designed to dispense sub-microliter quantities of fluids for applications including laboratory tests. When larger amounts of liquids are involved, sloshing must be considered as a parameter affecting stability, which is of significance for autonomous vehicles. The measurement and quantification of slosh in enclosed volumes poses a challenge to researchers who have traditionally resorted to tracking the air-liquid interface for two-phase flow analysis. There is a need for a simpler method to predict rollover in these applications. In this work, a novel solution to address this problem is proposed in the form of the Zero Moment Point (ZMP) of a dynamic liquid region. Computational experiments of a partially filled, two-dimensional liquid vessel were carried out using the Volume of Fluid (VOF) method in a finite volume based open-source computational fluid dynamics solver. The movement of the air-liquid interface was tracked, while the Center of Mass and the resulting Zero Moment Point were determined from the numerical simulations at each time step. The computational model was validated by comparing the wall pressure and movement of the liquid-free surface to experimentally obtained values. It was concluded that for a dynamic liquid domain, the Zero Moment Point can be instrumental in determining the stability of partially filled containers subjected to sloshing.  
computational fluid dynamics; fluid sloshing; table cart method; zero moment point +
Footnote †: journal: Applied Sciences
## 3 Simulation Results
### Trade-Off Analysis
#### 3.3.1 Analyzing Communication Performance  
The analysis assesses the effectiveness of communication in both SSF D-JCAS and DCAS setups. This comparison helps us to understand how changes in the communication SNR affect the BER of DCAS compared to SSF D-JCAS, where communication and radar sensing occur simultaneously in the same space.  
This comparison provides insights into how communication performance varies with SNR and helps to determine the superiority of either SSF D-JCAS or the traditional DCAS. The simulation parameters remain consistent with the previous settings: four transmit antennas (\(N_{t}=4\)) and either one or two receive antennas (\(N_{r}=1,2\)).  
Figure 10 illustrates this comparison, visually representing the communication performance of SSF D-JCAS and the traditional DCAS. It is important to note that the channel is modeled as a Rayleigh fading channel, and beamforming techniques are employed to segregate the communication and radar functions, enabling a focused analysis of their individual performances within the same space.  
Figure 9: Communication and sensing performance under different velocities of a UE: (**a**) Communication performance, (**b**): Sensing performance.  
From the graphs, it is evident that SSF D-JCAS achieves a lower BER compared to the traditional DCAS for downlink transmissions. Moreover, the BER curves for SSF D-JCAS and DCAS closely align and coincide in the high SNR regime.  
This phenomenon can be attributed to the concurrent transmission of communication and radar signals in SSF D-JCAS, which utilizes simultaneous spatial function instead of sequential transmission as in the traditional DCAS. In SSF D-JCAS, the received signal at the UE comprises both communication and radar signals transmitted over the same channel. In contrast, the traditional DCAS only contains the communication signal, along with AWGN.  
To mitigate the interference between the communication and sensing signals, MIMO systems are employed. Additionally, the proposed system utilizes interference cancellation techniques to minimize unknown signals treated as noise, thereby optimizing the communication performance. Consequently, the BER performance of SSF D-JCAS surpasses that of the traditional DCAS.
## 6 Conclusions and Outlook on Future Work  
In this paper, a novel iterative LMI solution for the joint control and observer parameterization of systems with polytopic parameter uncertainty and stochastic process, actuator, and sensor noise was derived. It is, by construction, equally applicable to the design of observer-based state and output feedback approaches. Most importantly, it allows for a guaranteed stabilization of the system dynamics despite bounded (polytopic) uncertainty in the system matrices, which makes the classical separation principle of control and observer design invalid. It was shown in benchmark simulations that the approach outperforms an LQG design for which a point-valued system model was used for the underlying controller and observer parameterization and, therefore, does not guarantee stability by design.  
In future work, this approach will be further investigated for discrete-time processes. Moreover, it is desired to optimize the structure of output feedback controllers and reduced-order observers for high-dimensional systems such that the number of variables that are required for a robust parameterization is minimized. Finally, we aim at introducing and optimizing weighting factors for the diagonal entries in the matrix (47) so that, during the minimization of (41), the user can decide which of the individual states (or estimation error signals) has the highest importance. This extension will have similarities to tuning the ratio between the weighting matrix entries for state deviations in the classical LQR design.  
Conceptualization, A.R.; Investigation, A.R. and S.R.; Software, A.R.; Validation, R.D., S.L. and B.T.; Writing--original draft, A.R., R.D., S.R., S.L. and B.T.; Writing--review and editing, A.R., R.D., S.R., S.L. and B.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** We acknowledge support from the Open Access Publication Fund of the University of Wuppertal.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Reduction of the output tracking error over 10 iterations for the proposed LMI-based output feedback controller (Case 2).
## 2 Materials and Methods
### Analytical Analysis: Modular Upper Bound (MUB)  
In previous investigations, the MUB models were developed for a flat punch (FP) and an inclined punch (IP) case (Figure 2) [19; 20], showing that the new UBT model is not a geometrical-limited application. MUB is found to be an adaptive method of analysis that saves resolution time, having a simpler implementation than other methods. Now, in this paper, a flat-inclined punch combination is implemented, generating different shapes as seen in Figure 7. For these cases, FP + IP shows that the external part of the punch has a flat consideration and the central part has an inclined consideration, being IP + FP inversely. With these punches, diverse indentations are analysed, covering more applications and adapting the MUB model if necessary.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Settings** & **Value** \\ \hline Number of Simulation Steps & 100 \\ Step Increment to save & 5 \\ With equal die displacement & 0.26 (mm) \\ Remesh maximum step increment & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General simulation settings.  
Figure 7: Geometries studied. Flat punch (FP) + inclined punch (IP) **(A)**; IP + FP **(B)**; inverted punch **(C)** and curve punch **(D)**.
## 2 Modeling Methodology
### Numerical Modeling  
Figure 6 shows typical mesh and boundary extents adopted in this study. By applying the simplified modeling, the number of elements was reduced from approximately 6000 to 4000 and the analysis time was remarkably reduced by about 50% in comparison with that of the full modeling of near- and far-field zones. By applying symmetric conditions, half of the entire system was modeled. The vertical and horizontal displacements at the bottom boundary and the normal displacements at the lateral boundaries were constrained. The size of the soil elements gradually increased from the pile foundation to the domain boundary. In addition, the optimum mesh sizes were determined to minimize the effect of the mesh size on the results from the preliminary analyses. The 3D elements of the hexahedron type were selected to model the soil domain and the pile foundation. As a result of modeling the pile by applying elastic model, it was found that the stress on pile foundation induced in the analysis process was even smaller than the yielding stress of material itself. The weight of the superstructure was correctly simulated by controlling the unit weight of the corresponding elements. Numerical analyses were performed following three steps; (1) simulation of geostatic condition, (2) pile installation, and (3) dynamic loading. At the first step, an in-situ geostatic condition was simulated by achieving the stress equilibrium under gravitational load. In the second step, the soil elements for pile area were eliminated and pile elements were created with the interface elements. The stress equilibrium condition was then gained for increasing stress due to pile weight once again. Finally, dynamic loading was applied at the bottom boundary and the left and right boundaries corresponding to loading direction. The time history of input seismic acceleration was applied to the bottom boundary nodes, and time histories of far-filed horizontal acceleration were applied to the left and right boundary nodes.