esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 4 Numerical Simulations
### Algorithm Test with Weak Couplings
#### 4.2.2 Results for Algorithm without Couplings
As is shown in Figure 5d,e, the linear velocities are similar to those of the QV controller, but the initial angular velocities have higher values. When the vehicle starts to move, the force \(\tau_{u}\) and torque \(\tau_{q}\) have large values, which indicates a large impulse in this phase of movement (Figure 5f). Figure 4: Comparative CL controller results, linear trajectory, and (26): (**a**) linear errors of position; (**b**) angular errors of position; (**c**) force and torque applied. For linear trajectories, weak couplings, and new control parameters set, the settling time for linear position errors is about 200 s for \(y_{e}\) (and about 20 s and 80 s for \(x_{e}\) and \(z_{e}\), respectively), and for angular position errors, it is about 80 s. The error values are higher than they are for the QV controller. _Complex trajectory_. This test allowed us to check how weak dynamic couplings affect the operation of CL controller when the trajectory is changed. It turned out that the parameter set (28) also allows the tracking of a complex trajectory. By comparing Figures 3 and 6, it can be noticed that, in the case of weak coupling, the CL algorithm is able to track the complex trajectory, but with a level of precision that may not be sufficient. Trajectory tracking is less exact than it is with the QV controller because the errors of the linear variables have larger values (cf. Figures 3a,b and 6a,b). As can be noted from Figure 6d-f, the values of speed, force, and torque show that the correct vehicle response is achieved. From a comparison of the results obtained, it is clear that the main problem is tracking linear positions when center of mass position shifts are not considered. It is worth noting, however, that it is necessary to use larger values of \(\lambda_{1},\lambda_{2},\lambda_{3}\) (relating to the sliding surfaces), smaller \(\Gamma_{2},\Gamma_{3}\) (relating to inaccurate knowledge of the model parameters), and larger \(k_{\theta},k_{\psi}\) (relating to the kinematic controller). These modifications are due to changes in vehicle dynamics by omitting couplings. Figure 5: Comparative CL controller results, linear trajectory, and (28): (**a**) trajectories: \(p_{d}\)—desired, \(p\)—actual; (**b**) linear errors of position; (**c**) angular errors of position; (**d**) linear velocities; (**e**) angular velocities; (**f**) force and torque applied. For complex trajectories, weak couplings, and new gains set (until a change in the direction of motion), the settling time for linear position errors is about 120 s, and for angular position errors, it is about 80 s. In this case, there is an improvement in the performance of the CL controller, but the linear position errors are larger than they are for the QV controller.
# Topology Optimization Considering Porosity Defects in Metal Additive Manufacturing
## 4 Constraint Function Considering Porosity Defects  
Topology optimization is to find the optimal material distribution to achieve the best structural performance. However, the appearance of porosity in the subsequent AM process will affect the performance of the structure, causing the manufactured parts to fail to satisfy the performance requirements. Therefore, it is necessary to consider the effects of porosity during the structural topology optimization, so that the manufactured parts still satisfy the requirements.  
In actual production, the generation of porosity is closely related to laser power, forming speed, scanning scheme, forming environment, and other factors in additive manufacturing. Our method cannot avoid the generation of porosity during the manufacturing process, that is, porosity will still be generated in the optimization results. However, we can consider these porosity defects in the optimization process, so as to reduce its effects on the structural performance of the optimization results. In other words, the optimization results without considering the porosity constraints cannot meet the actual working conditions due to the existence of porosity, while the optimization results considering the porosity constraints can still meet. Therefore, this method can greatly promote the combination of additive manufacturing and topology optimization, so as to greatly improve the production efficiency and capacity.  
Topological sensitivity is defined as the impact of objective when a small hole is inserted, which is compatible with the impact of porosity on structural performance. Therefore, this paper will use topological sensitivity to define the porosity constraint function.  
Figure 1: Flowchart of the level set topology optimization combining BESO and topological sensitivity.
## 1 Introduction
These functionalities have been technically developed and are reported in various deliverables [27; 28] of the ongoing research project named EASY-RES [8] which is funded by European Union. Each DRES can provide: (1) inertial response; (2) primary frequency response; (3) power smoothing; (4) reactive power support; (5) harmonic mitigation; (6) FRT capability and fault-current contribution. Functionalities (1), (3) and (6) require an additional ESS, which will be a FSS, because large amounts of power are required for dynamic and transient phenomena. This FSS would be a supercapacitor that is connected to the DC-link of the DRES converter (Figure 3). It is noted that these functionalities require a new type of converter control, like the one presented in [28], for the converter-interfaced DRES (CI-DRES). Actually the CI-DRES should operate like virtual synchronous generators in order to provide in a unified way all the aforementioned functionalities. This is applicable to all CI-DRES irrespective of their primary source (PV, wind, etc). However, just for simplicity reasons, many of the AS addressed in this paper use PVs as primary source. * The distribution system has physical boundaries and a single POI with the transmission system as shown in Figure 1.
* Battery energy storage systems (BESS) are assumed to be installed only at the POI of a LV uG with the upstream MV gird and the POI of the ICA with the transmission system (see Figure 1). The BESS are sized only for the following AS: (1) power smoothing, (2) exchange of reactive power for voltage regulation purposes, while they will be able to provide also, (3) contribution to harmonic mitigation and (4) contribution to fault clearing. By exploiting the aforementioned DRES/BESS functionalities, proper control schemes for voltage regulation and protection coordination will be applied at ICA level, while each ICA will be able to offer inertial and PFR by optimally allocating the respective responses to the DRES within each ICA. The definition, measurement and quantification for each of these six AS is presented in the rest of this section together with rough estimations of the challenges for their introduction. The costs and benefits resulting by the introduction of each AS are also roughly presented. Figure 1: General representation of a distribution grid (ICA). New Types of AS Provided by DRES/ESS: Proposed Definition and Its Justification, Suggested Measurement and Quantification, Associated Costs/Benefits
## 3 Results
### Spatial Resolution with the GEMPix
#### 3.3.3 Modulation Transfer Function  
As for the LaGEMPix, we carried out a set of measurements using the lead mask model 53 (see Figures 9 and 12). Contrary to the LaGEMPix, the GEMPix yielded an MTF below 10% at 1.4 LP/mm which corresponds to 0.71 mm, achieving the targeted submillimeter spatial resolution. The normalized MTF obtained by using Equation (2) is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**FWHM (mm)**} \\ \cline{2-4} \(\boldsymbol{\varnothing}\)**Hole** & **Hole 1** & **Hole 2** & **Hole 3** \\ \hline
6 mm & 6.60 \(\pm\) 0.10 & - & - \\
3 mm & 3.08 \(\pm\) 0.06 & 3.04 \(\pm\) 0.09 & 3.46 \(\pm\) 0.09 \\
1.3 mm & 2.07 \(\pm\) 0.05 & 1.49 \(\pm\) 0.03 & 1.85 \(\pm\) 0.03 \\ \hline \end{tabular}
\end{table}
Table 1: Calculated FWHM of the 1D profiles for three regions of interest of the images obtained by the GEMPix and the 1 mm-thick copper plate.  
Figure 11: The line-average response profile of 1.3 mm diameter holes spaced by 1 mm (edge to edge). The FWHM of the Gaussian distribution of the three holes (Table 1) were obtained by fitting the profile by a triple Gaussian function.
###### Abstract
Others use the addition of color as a semi-transparent overlay on a grayscale image, combined with further manipulation in the H channel to enhance the clinical differences, without variations in S and V [25]. A similar process was used to represent speckle variance over optical coherence tomography images, but the assignment of data to HSV was performed in terms of noise intensity [26]. Overall, having multiple maps for different optical properties is useful but, in some environments where rapid decision making is crucial, having an additional single image that contains all the necessary information might yield faster results. Inspired by the approaches taken in polarization imaging research, we propose a method of encoding the optical properties of biological tissue in the three components of the HSV color space by specifically choosing the parameters based on their visualization properties. This approach allows for the combination of high- and low-resolution data into a single image that conveys more information and possesses better explainability than its individual components, while still being easily interpretable and avoiding the need to select an optimal colormap for each one. The assignment to HSV makes use of the three colorimetric components and is designed to provide a clinical interpretation of the samples through color, with the most clinically relevant optical property being assigned to the H component. The S and V components are chosen based on the data with the highest resolution and spatial features, taking advantage of the human ability to recognize luminance variations as structural differences. The proposed enhancement of HSV is done automatically, taking into account the range of all possible values in a dataset, and thus using only one HSV legend shared by all the samples in it. With this work, the aim is to continue expanding the research on colormaps and color perception in the realm of clinical imaging.
## 3 Results and Discussion
### Lower Dead Center
#### 3.1.2 Linear Static Analysis  
Herein, we present the findings related to the critical position of the lower dead center, following the closure of the steam inlet to the cylinder, leading to its subsequent expansion within the chamber.  
As previously discussed, an initial linear static analysis was conducted under a gauge working pressure of 0.62 MPa, yielding a maximum von Mises stress of 96.12 MPa (Figure 13) and a minimum safety factor of 1.33 (Figure 14), both located in the same region. As noted earlier, the safety factor must exceed unity, but for an optimal machine design, a recommended range between 2 and 4 is advised to ensure reliable operation.  
Figure 12: Natural frequency vs. mode number.  
Figure 13: Maximum von Mises stress for a pressure of 0.62 MPa.  
Thus, it can be inferred that the machine would not fail at a steam inlet pressure of 0.62 MPa. However, to optimize this value towards a safety factor of 2, a subsequent analysis was required with a reduced working pressure, specifically 0.32 MPa. The objective was to evaluate if this pressure ensured the machine's proper functioning.  
Through these simulations, the location with the lowest safety factor was identified to refine the mesh accordingly. In this case, it was determined that this critical point was at the junction between the piston rod and the connecting rod.  
After carrying out the analysis with various mesh element sizes in the piston rod and connecting rod, the mesh convergence analysis graph was obtained (Figure 15). Additionally, data on the element size, von Mises stress, relative error percentage, and iteration number are summarized in Table 2.  
Figure 14: Minimum safety factor for a pressure of 0.62 MPa.  
Figure 15: Mesh convergence graph.  
In Table 2, it is demonstrated that mesh convergence was achieved with an element size of 5 mm, resulting in a relative error of 7.56% in the second iteration. However, further refinement was performed, reducing the relative error to 4.74% with an element size of 2.5 mm in the third iteration.  
Furthermore, Figure 16 illustrates the distribution of von Mises stresses, while Figure 17 pinpoints the location of the maximum stress value, which was specifically at the junction between the piston rod and the connecting rod, known as the crosshead.  
Similarly, the distribution of displacements was obtained (Figure 18) and is shown with an increased scale factor (\(\times\)2) for better observation. As can be seen, the value was negligible, but it was considered since it was the only part of the engine that was cantilevered.  
Finally, Figure 19 shows the distribution of the safety factor, and Figure 20 shows the location of its minimum value (2.35).  
Figure 17: Location of the highest value of von Mises stress.  
Figure 18: Displacement distribution with a \(\times 2\) scale factor.  
Figure 19: Safety factor distribution.  
Figure 20: Location of the minimum safety factor.
# Immersive Phobia Therapy through Adaptive Virtual Reality and Biofeedback
## 1 Introduction  
The PhoVR system, a tool for immersive phobia therapy through adaptive virtual reality and biofeedback, was developed with the purpose of treating various phobias and anxieties with the help of virtual reality, by gradually controlling patients' exposure in highly realistic 3D environments. During the therapy sessions, patients can access a 3D world, which they will be able to visualize through virtual reality (VR) glasses and navigate using controllers. The 3D environments contain complex scenes with terrain, vegetation, buildings, and other elements that allow for the simulation of real scenarios from nature or the urban environment. The 3D environment is able to adapt automatically or be adjusted by the therapist, in real time. One component of adaptation is changing the difficulty of the 3D environment, namely the degree of exposure to phobia stimuli. This involves changing the 3D scene or the position of some elements in the scene. Another component of the adaptation of the 3D environment includes the introduction of some support elements for the patient (music, video, texts, favorite objects, or scenes) with the purpose of relaxation or prerecorded audio containing relaxation and self-control techniques. During the sessions, biophysical data are acquired from patients (heart rate and electrodermal activity), which are presented in VR in an intuitive way, providing biofeedback regarding the currentemotional state. The proposed solution includes quests and games that challenge the patient to explore the virtual environment and confront their fears. A quest contains a list of tasks that must be completed. Games are a form of gamified quests, which have a funny, attractive side and use techniques taken from the field of video games to increase patient engagement.
## 3 Results
### Vortex Shedding  
Due to the O-ring blocking the inlet flow, regions of low pressure are immediately created behind the body (Figures 13-28). These vacuums inevitably introduce a pressure gradient, causing nearby trajectories to separate from the main flow to fill the voids in a diffusion-like movement. This random recirculation causes flowlines to produce turbulent eddies in the wake region, increasing air resistance in a process commonly referred to as form drag [13].  
Relationship Between Flow Velocity/Surface Roughness and Vorticity Concentration  
The setup results concur that the pressure gradient is significantly increased with airflow velocity (Figures 11-28); this subsequently expedites the diffusion movement of flowlines into the wake region and enhances the recirculation of fluid behind the O-ring, intensifying vorticity (Figures 29-31) from 3.5/s (Figure 29) to 1000/s (Figure 31). However, since the wake region is consolidated at higher airflow (Figures 7-10), the vortex distribution is controlled into narrower regions [6; 13; 17; 18] (Figures 29-31).  
Nothing noticeable was observed when changing the flow velocities from 0.01 m/s to 0.02 m/s or between surface roughnesses of 5 m and 100 m. This is because both sets of values were too similar and quantitatively insignificant [13].  
Figure 29: Vorticity plot for Setup 5 (0.01 m/s, 100 m).
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Link Faults
#### 3.2.1 Overcurrent Algorithm  
The threshold is selected, at an absolute value, to cover both directions of the current. Figure 8(a) shows a pole-to-pole fault in the middle of link L34. On the other hand, Figure 8(b) shows a pole-to-ground fault at the end of link L24. Detection times are collected in Tables 7 and 8.  
It can be seen in Figure 8(a),b that, in a steady state, both end relays are measuring a current with the same magnitude but with the opposite polarity.  
In Table 7, which includes the results of all the simulated scenarios, the effect of this factor, when the current is negative in a steady state, can be appreciated. Consequently, it takes more time to detect the fault. The most remarkable cases are pole-to-ground faults with a fault resistance of 20 \(\Omega\), as measured by relay R42.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault at Bus 2**} & \multicolumn{2}{c}{**Detection Time at R21 (ms)**} & \multicolumn{2}{c}{**Detection Time at R24 (ms)**} \\ \cline{2-5}  & **P2P** & **P2G+** & **P2P** & **P2G+** \\ \hline \multirow{3}{*}{LIVRD} & R = 0.01 \(\Omega\) & 0.010 & 0.010 & 0.010 & 0.010 \\ \cline{2-5}  & R = 2 \(\Omega\) & 0.010 & 0.010 & 0.010 & 0.010 \\ \cline{2-5}  & R = 20 \(\Omega\) & 0.010 & 0.010 & 0.010 & 0.010 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fault at bus 2, detection times at R21 and R24, for the LIVRD algorithm.  
Figure 8: Fault at bus 2, the LIVRD at R21 and R24: (**a**) pole-to-pole fault, and (**b**) pole-to-ground fault.  
Figure 9b shows that the current is negative in a steady state, but when the fault occurs, it reaches a positive value without crossing the threshold, until it becomes negative again and crosses the threshold in about 8 ms. This fault detection time is quite high for DC systems' protection purposes.  
To sum up, it can be said that the overcurrent algorithm presents a performance that is not valid for DC links' protection, at least not for faults with high fault resistance.
## 5 Experimental Design  
### Dataset  
The Communications Security Establishment (CSE) and the Canadian Institute for Cyber Security (CIC) have jointly developed the CSE-CIC-IDS2018 dataset for attack detection and prevention. This open-source dataset provides traffic patterns and various attacks found in network architecture [52].  
There are 80 features in the dataset, including tags, most consisting of data in terms of statistics regarding internet traffic. The CICFlowMeter tool obtains these features by generating information based on network traffic flow. The dataset includes continual characteristics such as labels, timestamps, destination ports, and protocols. It includes six scenarios and fourteen types of assaults, such as botnet, denial of service (DoS), infiltration, Web attack, and DDoS [28; 52].  
Amazon Web Services (AWSs) offers the CSE-CIC-IDS2018 dataset for download; included are ten CSV files totaling over 16.2 million samples, representing ten recorded network flow days. The CICFlowMeter program extracted more than 80 features to produce the dataset. The CSE-CIC-IDS2018 dataset's Table 6[29; 54] offers comprehensive details on the different kinds of assaults and safe traffic.  
Table 7 shows the record count and attack percentage of the CSE-CIC-IDS2018 dataset.  
There are 16,232,943 records in the CSE-CIC-IDS2018 dataset, with 83.07 percent (or records) consisting of benign traffic, while the remaining records represent malicious activity. This study used 80 percent of the records for training, while the remaining 20 percent was allocated for testing [28; 52; 53; 54; 29; 55].
## 7 Conclusions  
This paper focuses on the problem of improving the consumption ability of DG in the distribution network, and proposes a control strategy based on FMSS for maximum consumption of DG in the complex distribution network. Further, improved particle swarm optimization algorithms are used to realize the real-time control of DG.  
The effectiveness of this strategy is verified by numerical examples, and the advantages of this strategy are proven by comparison with traditional reactive power compensation devices. Meanwhile, different from most studies based on the planning level, this paper focuses on the real-time operation level of the system and has the advantage of real-time adjustment.  
From summarizing the theoretical analysis, the model establishment and the verification of simulation examples in this paper, the following conclusions can be drawn:  
1. The main limiting factor of the consumption capacity of DG of the distribution network is the voltage deviation of network lines. FMSS can reduce the voltage deviation of the line by adjusting the active and reactive power flow distribution of the network, thus promoting the consumption ability of DG of the distribution network.
2. By comparing with traditional reactive power compensation devices, such as SVC and STATCOM, it can be seen that FMSS can simultaneously affect active and reactive power flows and have a stronger ability to consume DG.
3. In a certain range, appropriately increasing the rated capacity of FMSS can improve the consumption capacity of DG.
4. In the more complex distribution system, a regulation strategy based on FMSS proposed in this paper, which is oriented to the maximum absorption of the distributed power supply, can dynamically adjust with the change of the system state and has good real-time regulation characteristics. This improves the economy and efficiency of the distribution system.  
The authors contribute as follows: Conceptualization, G.Z.; methodology, G.Z., J.L. and S.L.; software, S.L. and X.Z.; validation, G.Z., S.L. and X.L.; formal analysis, J.L. and S.L.; investigation and resources, J.L. and Y.L.; data curation, S.L.; writing--original draft preparation, G.Z., J.L. and S.L.; writing--review and editing, J.L.; supervision, G.Z., J.L. and Y.L.; project administration, J.L. and Y.L.; funding, J.L. and Y.L.  
This research was funded by National Key R&D Program of China, 2017 YFB0903100 and Science and Technology Projects of State Grid Corporation of China, 521104170043.  
The authors declare no conflicts of interest.  
Figure 10: Optimal output of DG in network 2 with static var compensator (SVC).