esa-sceva/satcom-chunk-collection huggingface dataset:
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 5 Conclusions and Future Works  
### Conclusions  
In this study, we propose an on-ground projection-based shared external human-machine interaction (eHMI) which facilitates the autonomous wheelchair in communicating its motion intentions to both the user and the surrounding pedestrians. By leveraging the innate human experience and intuition in the proposed shared eHMI, a synergistic effect with machine intelligence can enhance the smoothness and quality of wheelchair autonomous navigation. It serves to alleviate the user's cognitive load through machine assistance while utilizing human perception for tailored route planning, achieving a more autonomous and personalized navigation experience. For pedestrians, understanding each other's behavioral intentions can promote safe interactions in a shared space.  
Guided by this approach, we developed an autonomous wheelchair simulation platform that projects motion intentions onto the ground using an interface preferred by the user. The pilot study highlighted the appeal of on-ground projections, though concerns arose pertaining to visibility, safety, trust, and practicality.  
Among the participants, wheelchair users reflected that the user interface proposed in this study enhanced their perception of the intelligence and trustworthiness of the autonomous wheelchair, as well as the overall user experience. Additionally, pedestrian participants indicated that the interface improved their safety experience when interacting with users of autonomous wheelchairs. The shared eHMI, utilizing augmented reality, improved navigation visibility and accuracy. However, wheelchair users expressed a desire for further practical enhancements, and pedestrian users saw room for improvement in visual feedback. Overall, despite concerns about the reliability of autonomous wheelchair technology among wheelchair users, the integration of system status and intent visualization through projection interaction led to a supportive attitude towards autonomous wheelchairs.
## 4 Coded Cooperation with Soft Information Relaying and Relay Selection
### CC-SIR-RS Scheme with Fuzzy Logic-Based Power-Splitting
From Figure 3a, the channel coefficient \(h_{SR_{r}}\) is classified as weak with \(\mu(x_{1})=0.86\) and average with \(\mu(x_{1})=0.14\), which is represented by the set \(X_{1}=\{(\text{weak},0.86),(\text{average},0.14)\}\); the channel coefficient \(h_{R,D}\) is classified as average with \(\mu(x_{2})=0.6\) and strong with \(\mu(x_{2})=0.4\), which is represented by the set \(X_{2}=\{(\text{average},0.6),(\text{strong},0.4)\}\); and the distance \(d_{SR_{r}}\) is classified as medium with \(\mu(x_{3})=1.0\), which is represented by the set \(X_{3}=\{(\text{medium},1.0)\}\). The PSR of this example is determined by the combination of the following four elements \(Y=\{(\rho^{(5)},0.6),(\rho^{(5)},0.4),(\rho^{(6)},0.14),(\rho^{(5)},0.14)\}\), where each element \((y,\mu(y))\) in \(Y\) results according to the input triplet \(((x_{1},\mu(x_{1})),\,(x_{2},\mu(x_{2})),\,(\text{and}\,(x_{3},\mu(x_{3})))\), where \(y\) is obtained as outlined in Table 1, and the membership degree \(\mu(y)\) is given by \(\min\{\mu(x_{1}),\mu(x_{2}),\,\mu(x_{3})\}\). For instance, the triplet \(((\text{weak},0.86),(\text{average},0.6),(\text{medium},0.6))\) leads to the output element \((y,\mu(y))=(\rho^{(5)},0.6)\). Thereafter, the four elements of \(Y\) are combined to obtain the fuzzy output. In this work, the crisp value of this fuzzy operation is given by calculating the weighted average [35], which is expressed by \(\rho_{r}^{*}=\sum x\mu(x)/\sum\mu(x)\), where \(x\) is the point with maximum membership value and \(\mu(x)\) is the membership value corresponding to the maximum. In this example, \(\rho_{r}^{*}\) is 0.511. The SNR of the equivalent end-to-end channel, resulting from the deployment of the PSR \(\rho_{r}^{*}\), is denoted by \(\Gamma_{SR,D}^{*}\). Accordingly, the opportunistic relay selection scheme is formulated as follows:  
\[R_{\text{opt}}^{\text{CC-SIR-RS-FLPS}}=\arg\max_{r=1,\cdots,\mu_{R}}\{\Gamma_ {SR,D}^{*}\}. \tag{32}\]Note that the proposed CC-SIR-RS scheme applying the fuzzy logic-based power-splitting is referred to as the CC-SIR-RS-FLPS scheme.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 6 Discussion  
This study presents a comprehensive analysis and comparison of three overload mitigation strategies for inertial GFM inverters under grid frequency excursions. The structure, tuning, and performance of these strategies are evaluated both in simulation and in experimental setup. Their key characteristics are summarized in Table 3. All three strategies eliminate primary frequency regulation and reduce inertial power while maintaining synchronization with the grid. However, each strategy has specific advantages and drawbacks.  
The parallel PI strategy is a simple approach to limit active power and can be applied regardless of its inner controller structure. However, its recovery from overload is the slowest due to the dynamics of the PI integrator, limiting the performance during frequency oscillations and fast transients.  
The AL strategy provides both overload mitigation and fast recovery. However, its response becomes undamped as the grid weakens. While increasing the proportional action of the PLL can improve damping, it increases sensitivity to noise and compromises stability in weak grids. Additionally, AL is sensitive to control delays and system uncertainties due to its open-loop design and the small phase shifts that must be limited. This strategy also relies on a voltage drop across the inverter and PCC terminals, requiring a virtual impedance or physical impedance between them.  
The EFS strategy provides an overload mitigation capability similar to the parallel PI while solving the uncertainty sensitivity and the undamped response of AL. It also  
Figure 19: Experimental validation of EFS strategy. (**a**) Active power response. (**b**) Outer loop power setpoint.  
Figure 18: Experimental validation of AL strategy. (**a**) Active power response. (**b**) Phase shift between inverter voltage and PCC voltage.  
offers the fastest recovery among the studied strategies. However, its main drawback is the reduced damping during normal operation due to the cascaded controller structure. Decoupling both controllers requires reducing the inertia during overloading, which might not be practical in all situations. Like the AL strategy, it also depends on a voltage drop to operate.  
Finally, it should be noted that while the proposed strategies significantly reduce the inertial contribution during frequency excursions, they do not eliminate it entirely. As a result, some power overloading may still occur. However, the small-signal models presented in this work offer an analytical method to estimate the maximum expected overload based on the minimum inertia, system damping, and the RoCoF of the disturbance. These expressions enable designers to either adjust the active power threshold to remain within safe operating limits or oversize the inverter accordingly to accommodate transient conditions.
# Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems
## 5 Experimental Results
### Results of Tank 2
The adaptive component of FO-APIDSMC automatically adjusted to system uncertainties, further enhancing robustness. The tests showed that FO-APIDSMC not only worked better than the CNPIR and FF-PI controllers but also provided a more accurate and dependable way to manage complex dynamic systems in real-time. In addition, the proposed FO-APIDSMC controller integrates fractional-order and adaptive techniques simultaneously. For the first time, we have applied this proposed strategy in real-time to a liquid level system. This approach allows for superior performance in both tracking precision and disturbance rejection control for nonlinear and uncertain dynamic processes. Future studies will concentrate on developing and implementing hybrid control strategies that integrate artificial intelligence (AI) or machine learning (ML) techniques into the FO-APIDSMC framework. Conceptualization, K.K.A. and A.D.; methodology, A.D. and K.K.A. ; software, A.D. and K.K.A. ; validation, A.D., K.K.A., S.G. and E.T. ; formal analysis, A.D. and K.K.A. ; investigation, A.D. and K.K.A. ; data curation, A.D. and G.K.; writing--original draft preparation, A.D. and K.K.A. ; writing--review and editing, K.K.A. and A.D.; visualization, K.K.A. and A.D.; supervision, A.D.; project administration, A.D. and K.K.A. ; funding acquisition, K.K.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The supporting data of this article will be made available by the authors upon request. The authors declare no conflicts of interest.
# Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning
## 2 Training Database Composition Approaches
### Incremental Training Data-Set  
An incremental data-set occurs when the available samples are limited. Usually this is the case of real-time or time-dependant processes, and data can be acquired only progressively. Consider for example our case study when the monitoring system starts recording data from the first day of operation of the PV plant: initially a small amount of data is recorded, and if we acquire hourly samples, 24 samples are added to the historical data-set every day.  
In this database composition (e.g., see Figure 3), the days which can be employed for ANN training are those available starting from the PV plant commissioning (day 1) until the \(k_{d}\) day before the forecast (day \(X_{d}\)). As a consequence, the size of the training database will increase over time. In order to supply the data-set to the network for the training step, samples can be arranged in different methods. Those adopted in this paper are listed in Figure 4, and determine different results in the forecast. A short description is given in the following:  
* Method A employs the same chronologically consecutive samples by grouping the 90% of the samples which are closest to the forecast day for the training set and the remaining 10% of the samples for the validation set.
* Method A* employs the same chronologically consecutive samples by grouping the 90% of the samples for the training set and the 10% of the samples which are closest to the forecast day for the validation set.
* Method B employs the samples by randomly grouping them separately, 90% for the training set and 10% for the validation set.  
Figure 3: Hourly samples are progressively available in an incremental training database. PV: photovoltaic.  
Figure 2: Physical Hybrid Artificial Neural Network (PHANN) method schematic diagram.  
In the first two methods (A and A*), the effect of the proximity of the training set to the forecast day is examined (implying seasonal variations on the parameter), inspecting how the forecast is affected by the proximity of the samples employed in the training rather than in the validation step. For example, it is clear that forecasting spring days cannot be accurate if the training samples belong to the past autumn or winter, and the same consideration applies for the validation. Reasonably, we are expecting that the further the samples of the validation are, the less accurate the forecast. Obviously, this problem is not addressed in Method B, as samples are randomly chosen.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 3 Matrix Applications in Signal Estimation Theory
### Random Matrix Theory in Signal Estimation and Detection
Consider a multi-source power estimation problem [31], where at time \(t\), a number of \(N\) sensors receive signals from K sources which are equipped with \(n_{1},n_{2},\cdots,n_{K}\) transmitters, respectively. The observed data \(\mathbf{y}\in\mathbb{C}^{N}\) from sensors at time \(t\) can be expressed as  
\[\mathbf{y}(t)=\sum\limits_{k=1}^{K}\sqrt{P_{i}}\tilde{\mathbf{U}}_{i}\mathbf{x }_{i}(t)+\sigma\mathbf{\
u}(t), \tag{35}\]  
where \(P_{i}\) denotes the transmit power of source \(i\), \(\mathbf{x}_{i}\in\mathbb{C}^{n_{i}}\) stands for the signal collected from source \(i\) and it is assumed to be complex Gaussian distributed with zero mean and identity covariance matrix, and \(\sigma\mathbf{\
u}\) represents the additive noise. For simplicity, the channel matrix \(\tilde{\mathbf{U}}_{i}\in\mathbb{C}^{N\times n_{i}}\) is selected suchthat \([\widetilde{\mathbf{U}}_{1},\widetilde{\mathbf{U}}_{2},\cdots,\widetilde{\mathbf{U} }_{K}]\in\mathbb{C}^{N\times(n_{1}+n_{2}+\cdots+n_{K})}\) represent the \(n_{1}+n_{2}+\cdots+n_{K}\) columns of the unitary matrix \(\mathbf{U}\in\mathbb{C}^{N\times N}\). If follows that  
\[\mathrm{E}[\mathbf{y}(t)\mathbf{y}(t)^{H}]=\mathbf{UPU}^{H}+\sigma^{2}\mathbf{ I}_{N},\]  
where \(\mathbf{P}\) is a diagonal matrix with diagonal elements  
\[\underbrace{P_{1},\cdots,P_{1}}_{n_{1}},\underbrace{P_{2},\cdots,P_{2}}_{n_{ 2}},\cdots,\underbrace{P_{K},\cdots,P_{k}}_{n_{K}},\underbrace{0,\cdots,0}_{N -n_{1},\cdots,\cdots n_{K}}.\]  
Suppose that \(L\) independent observations of \(\mathbf{y}(t)\) are available, i.e., \(\mathbf{y}(1),\mathbf{y}(2),\cdots,\mathbf{y}(L)\). Thus, it follows that as \(L\to\infty\):  
\[\left\|\frac{1}{L}\sum_{i=1}^{L}\mathbf{y}(i)\mathbf{y}(i)^{H}-\sigma^{2} \mathbf{I}_{N}-\mathbf{UPU}^{H}\right\|\stackrel{{\mathrm{a.s.} }}{{\longrightarrow}}0, \tag{36}\]  
and a consistent estimator can be derived. However, when \(N\) is also a large number and \(L\) and \(N\) are comparable, (36) does not hold anymore. Using the Stieltjes transform and its extensions, the G-estimation method is generally adopted to improve the estimation performance. More technical and mathematical details about the Stieltjes transform and its generalizations can be found in [31; 35].
## 3 Simulation Results
Figure 11 presents the proportion of total charging time used for energy trading. With \(T_{f}\) = 0.05, the proportion of time for energy trading is increased with the higher antenna gain, as expected. That is, with 12 dB antenna gain, the proportion is approximately 10% higher than that with 0 dB antenna gain for \(N_{T}\) = 100, and it decreases with increasing \(N_{T}\). In fact, higher density of sensor nodes increases the amount of captured RF energy, thus decreasing the time for energy trading. The significant reduction observed for the 12 dB antenna gain is not observed with the other antenna gains. With \(T_{f}\) = 0.01, the proportion of time for energy trading is much smaller because there are substantially larger number of overcharged seller nodes. The \(EL_{\min}^{A}\) values, which are set considerably higher for \(T_{f}\) = 0.01, cause a significantly larger number of overcharged sensor nodes. The higher \(EL_{\min}^{A}\) value set for lower \(T_{f}\) overpowers the compensating effect of the lower \(T_{f}\), leading to a smaller proportion of time for energy  
Figure 10: Variation of total charging time according to number of sensor nodes with different antenna gains for \(T_{f}\) = 0.01. Figure 9: Variation of total charging time according to number of sensor nodes with different antenna gains for \(T_{f}\) = 0.05.  
trading. As seen in Figure 11a,b, a larger number of sensor nodes are, in general, beneficial for reducing the proportion of time required for energy trading. The energy profiles of the sensor nodes before and after the energy trading can be seen in Figure 12 with 12 dB antenna gain and the charging efficiency \(T_{f}\) = 0.05. As seen in Figure 12b, no sensor nodes remain undercharged after energy trading. With the simulation parameters shown in Table 1, all the sensor nodes are charged over the target energy level \(ET_{s}\) = 2 Joules. Figure 12a shows that the energy levels of the overcharged sensor nodes, including cluster heads, are significantly higher than those of the undercharged sensor nodes. The behavior of the hybrid charging scheme is shown in Figure 13 in an integrated fashion. Figure 13 shows the variation of the number of overcharged sensor nodes over time with 12 dB antenna gain and \(N_{T}\) = 100. The two plots in the figure support the previous discussion regarding the time needed for charging by the mobile charger and the time for energy trading. The number of overcharged sensor nodes created in the first stage with T\({}_{f}\) = 0.01 is considerably larger due to higher \(EL_{\text{min}}^{A}\). This results in a reduced time for energy trading, which is also observed in Figure 11.  
Figure 11: Percentage of time for energy trading over total charging time with different antenna gains and charging efficiency: (**a**) \(T_{f}\) = 0.05; and (**b**) \(T_{f}\) = 0.01. Figure 12: Energy level profile over sensor nodes with 12 dB antenna gain and \(T_{f}\) = 0.05: (**a**) before energy trading; and (**b**) after energy trading.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 4 Results and Discussion  
### Experimental Setup for Real-Time Weld Seam Tracing  
The deep learning-based welding robotic system was implemented in a five DOF TAL BRABO manipulator. This manipulator has five joints named \(x,\ y,\ z,\ u\) and \(v\). The \(x,y\), and \(z\) joints are the three main joints and \(u\) and \(v\) are the wrist joints in this robot. Since there is no yaw motion in this robot, it is considered a five DOF robotic manipulator. The main reason to implement this work on this robot is that this robot has different kinematic and dynamic configurations, and no such experimental study has been tested on this robot. Hence, this work was carried out on a TAL BRABO robot. Secondly, there is very limited work on deep learning-based weld shape detection and tracing. The communication to TAL BRABO joints takes place through ActiveX commands written in MATLAB which communicates with the robot via a Trio motion controller. This controller is placed inside the programmable logic circuit (PLC) station from which robot is switched on. The vision system used in this paper was eye-to-hand configuration; a single 2D web camera was used, tilted at an angle of 26\({}^{\circ}\) to view the robot workspace where the weld plate was placed. Additionally, the camera was placed at an angle of approximately 32 degrees away from the robot base axis in order to make the problem a challenging one. Apart from the rotation and the translation of the camera into world coordinates, there was additional rotation from the axis of the robot base. This complex kinematic analysis was solved by using a neural network to convert pixel coordinates into world coordinates, as discussed in Section 4.5. The structure of the entire experimental setup is shown in Figure 8.
## 4 Stability Analysis
Furthermore, it has been proved in Ref. [50] that the compensation signals \(\xi_{i}(i=1,2,3,4)\) in (16) are bounded. Since \(e_{i}=v_{i}+\xi_{i}\), it is clearly known that the tracking errors \(e_{i}\) are also bounded. According to Lemma 1 and Theorem 1, the filter outputs \(z_{i},z_{\text{id}}(i=1,2,3)\) and the estimations \(\tilde{d}_{1},\tilde{d}_{2}\) are bounded. From (38), it is evident that the virtual control laws \(\alpha_{1}(\hat{x}_{\text{d}},e_{1})\), \(\alpha_{2}\Big{(}\chi_{1},e_{2},\tilde{d}_{1},z_{\text{id}},v_{1}\Big{)}\), \(\alpha_{3}(e_{3},z_{\text{id}},v_{2})\), and the actual law \(u\Big{(}x_{1},x_{3},e_{4},\tilde{d}_{2},z_{\text{3d}},v_{3}\Big{)}\) are also bounded because of the boundedness of their independent variables. Therefore, all the signals in the closed-loop control system are uniformly ultimately bounded. In addition, by properly choosing the design parameters, such as large \(k_{i}\)(i=1,2,3,4) and \(\beta_{i}(i=1,2)\), the tracking errors \(e_{i}(i=1,2,3,4)\) can converge to a small neighbourhood around zero. This completes the proof of Theorem 2.
## 3 Results and Discussion
### Mechanical Properties of Cu Meshes and Embedded Cu Meshes  
The main feature of embedded meshes is their excellent physical and mechanical parameters, such as resistance to bending and abrasion and adhesive strength.  
We studied the adhesive properties using two approaches. The first approach is a single test to determine the adhesive strength class of the coating according to ASTM D3359. Figure 9 shows microscopic cross-sectional images before (left) and after (right) tape test for SCM 180 (Figure 9a) and e-LCM 180 (Figure 9b).  
Figure 9a,b show that PET and NOA 63 have different hardnesses, and therefore the notch mesh looks different. In terms of adhesion strength, SCM 180 can be attributed to the lowest adhesive strength score of 0 B (Figure 9a right part, complete removal of the Cu mesh is observed) according to ASTM D3359. At the stage of applying the notch mesh, delamination of the Cu mesh from the substrate was observed (Figure 9a left part). Moreover, the type of template does not significantly affect the adhesive strength.  
Tests showed no damage to the mesh for the embedded Cu mesh, regardless of template type and plating time (Figure 9b). Thus, embedded Cu meshes e-LCM 30 and 180 and e-SCM 30 and 180 can be assigned to adhesive strength class 5B, the highest value according to ASTM D3359. Our results correlate well with the literature data for embedded metal transparent conductors based on micro and nanostructures [42; 43; 44].  
The second approach is a cyclic tape test. It shows how the sheet resistance of a coating changes with repeated exposure. We have studied the effect of the cyclic tape test (Figure 9c) on sheet resistance of all types of coatings. Cyclic tape test data and measurements according to ASTM D3359 give similar results.  
Low adhesion of LCM 180 and SCM 180 enables the defect-free transfer of electroplated meshes into the adhesive layer of scotch tape [45]. This feature of electroplated Cu meshes can be an interesting practical result (Figure 10). The large non-metallized area allows the conductive tape to retain its high adhesive strength.  
Figure 9: Tape test in B geometry before and after testing SCM 180 (**a**) and e-LCM 180 (**b**); Photo (**c**) and results (**d**) of the cyclic tape test.  
Flexural deformation resistance is also an important characteristic of coatings. In general, embedded Cu meshes are at the same level as before the embedding process. The reason for this is the high resistance of transparent coatings based on metallic micro and nanostructures to tensile-compressive deformations [31]. Figure 11a shows a graph of the effect of cyclic bending with a radius of 10 mm on sheet resistance of Cu meshes.  
It can be seen that 1000 bending cycles with a radius of 10 mm do not increase sheet resistance for Cu meshes nor embedded Cu meshes.
## 3 Test Results and Discussion
### Low-Temperature Cracking Resistance  
Table 7 shows the low-temperature bending test results of PAC mixtures. As can be seen from this table, PAC13 had the lowest flexural strain at failure, while PAC10-C had the largest flexural strain at failure. The mixture has larger NMAS may be more brittle than that has smaller NMAS at -10 \({}^{\circ}\)C. Fine aggregate can improve the low-temperature cracking resistance of PAC10, so conventional PAC10-C mixture had larger flexural strain at failure than the novel PAC10 mixtures. For the three novel PAC10 mixtures, with the increasing content of the mineral fillers, the flexural strength and flexural strain at failure increased, but their flexural stiffness at failure are similar. This means increasing the mineral filler content can improve the low-temperature cracking resistance of PAC10 mixture, because more asphalt mastic improves the strength and toughness of the mixture.