esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Modeling of the Smart City Residents' Activity with Multi-Agent Systems
## 3 Research Methodology
### Modeling of the Smart City  
The crucial element of studying the development of a city and the way the urban network works using the (broadly defined) game theory is determining whether the knowledge of individual players (advanced sensors) is symmetrical or if there is an informational asymmetry. Multi-agent systems are one of the tools used for modeling the game theory and the theory of market mechanisms.  
The models use elements of game theory, emergence, sociology, and multi-agent systems. The model assumes that agents, representing the residents of a city (sensors), move around the city and interact with each other. It takes place during every act of verbal or non-verbal communication. This process involves mutual decoding and simultaneous interpretation of the meaning of symbols used by the other party in communication. Interactions influence and change agent's trust in other residents. The information asymmetry phenomenon is easily modeled in a multi-agent system where every agent (an autonomous software element), while making decisions based on private information and interacting with the remaining agents, has a piece of information whose level may be varied. The simulation is divided into small time quanta (e.g., 15 min), during which the residents interact with each other.  
The city is modeled by a system of roads on which the agents move, a set of buildings, including stand-alone and multi-family residential buildings, factories, office buildings, offices, health clinics, schools, and museums; green areas, i.e., boulevards, parks, and water reservoirs. The city is also divided into districts distinguished by a set of general features, which characterize both the district and the people in the district, e.g., the office district is characterized by a significant share of office buildings, and the people in it are blue-collar workers.
## 4 Results
### Developed Potentials on the Grounding System
Figure 12: Developed potentials when a fault occurs on the middle of the MV line (L = 10 km) of GRID A (**a**) touch voltage without surface layer, (**b**) touch voltage with surface layer (10 cm) of high resistivity, \(\rho\) =10.000 \(\Omega\)m, (**c**) step voltage without surface layer, and (**d**) step voltage with surface layer (10 cm) of high resistivity, \(\rho\) = 10.000 \(\Omega\)m.  
Figure 13: (**a**) Developed touch voltage when a fault occurs on the middle of the MV line (L = 10 km) of GRID B without surface layer, (**b**) developed step voltage of GRID B with surface layer of high resistivity, \(\rho\) = 10.000 \(\Omega\)m, (**c**) developed touch voltage of GRID C without surface layer, and (**d**) developed touch voltage of GRID C with surface layer of high resistivity, \(\rho\) = 10.000 \(\Omega\)m.  
For a more detailed analysis, Appendix A presents detailed results of the developed touch and step voltages across all three variants of the grounding grid, considering a horizontal two-layer soil structure. It is noteworthy that this soil structure model offers enhanced accuracy, mirroring the real-world scenario where soil typically comprises multiple layers with varying resistivity.
## 2 Main Result
### Lyapunov Stability for the Estimated State Error
Substituting Equation (18) into Equation (17):  
\[\dot{V}_{L} =\operatorname{tr}(\underbrace{(-\dot{\varrho}_{y}y^{\dagger} \gamma_{y}-aL)}_{=\Delta L}\gamma_{y}^{-1}\Delta L^{\dagger})=\operatorname{ tr}(-\dot{\varrho}_{y}y^{\dagger}\gamma_{y}\gamma_{y}^{-1}\Delta L^{\dagger}-aL \gamma_{y}^{-1}\Delta L^{\dagger}) \tag{19}\] \[=-\operatorname{tr}(\dot{\varrho}_{y}\underbrace{y^{\dagger} \Delta L^{\dagger}}_{=w_{x}^{\dagger}})-\operatorname{tr}(aL\gamma_{y}^{-1} \Delta L^{\dagger})=-\underbrace{\operatorname{tr}(\dot{\varrho}_{y}w_{x}^{ \dagger})}_{=\operatorname{tr}(w_{x}^{\dagger}\dot{\varrho}_{y})}- \operatorname{tr}(aL\gamma_{y}^{-1}\Delta L^{\dagger})\] \[=-\underbrace{w_{x}^{\dagger}\dot{\varrho}_{y}}_{=(w_{x},\dot{ \varrho}_{y})}-\operatorname{tr}(aL\gamma_{y}^{-1}\Delta L^{\dagger})=- \underbrace{(w_{x},\dot{\varrho}_{y})}_{=(\dot{\varrho}_{y},w_{x})}- \operatorname{tr}(aL\gamma_{y}^{-1}\Delta L^{\dagger}).\]  
Therefore, the closed-loop Lyapunov function for the error system can be written as follows:  
\[V_{eL}(e_{x},\Delta L)=V_{e}(e_{x})+V_{L}(\Delta L)=\frac{1}{2}e_{x}^{\dagger} P_{x}e_{x}+\frac{1}{2}\operatorname{tr}(\Delta L\gamma_{y}^{-1}\Delta L^{ \dagger}). \tag{20}\]  
Following, the closed-loop energy-like time rate of change for the error system follows:  
\[\dot{V}_{eL}(e_{x},\Delta L) =\dot{V}_{e}(e_{x})+\dot{V}_{L}(\Delta L) \tag{21}\] \[=-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x}+(\dot{\varrho}_{y},w_{x}) +(P_{x}e_{x},v)-(\dot{\varrho}_{y},w_{x})-\operatorname{tr}(aL\gamma_{y}^{-1} \[PERSON]^{\dagger})\] \[=-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x}+(P_{x}e_{x},v)- \operatorname{tr}(aL\gamma_{y}^{-1}\[PERSON]^{\dagger}).\]  
While adaptive feedback has eliminated some of the residual terms in the closed-loop time derivative of the Lyapunov function, as demonstrated in Equation (21), residual terms still exist such that \(\dot{V}_{e}\leq 0\) is not guaranteed. As a result, the Barbalat-Lyapunov Lemma cannot be applied and therefore does not guarantee \(e_{x}\xrightarrow[t\to\infty]{}0\) asymptotically. An additional argument will be presented to establish an upper bound on the estimated state error term, confining both the internal and external error to a neighborhood around zero.
## 4 Uncertainty Analysis
The contribution of the resolution (\(u_{\text{res}}\)) was estimated from the information in the specification sheet of the rotary encoder provided by the encoder manufacturer. Meanwhile, the contribution of the stability of the encoder reading (\(u_{\text{stb}}\)) could somehow be affected by the stability of the developed setup, and was then evaluated by using experimental results. As can be seen in Figure 6a,b, standard deviations of the encoder reading when the scale grating was kept stationary at the angular positions of the positive and negative first-order Littrow configuration were evaluated to be 0.06054 arc-second and 0.06475 arc-second, respectively. Since a mean of 200 encoder readings at each angular position of the grating was employed as \(\theta_{i}\), by applying a divisor of \(\sqrt{200}\) to the worse case (0.06475 arc-second), \(u_{\text{stb}}\) was evaluated to be \(4.579\times 10^{-3}\) arc-second. Table 2 summarizes the contribution of each uncertainty source to \(u(\theta_{i})\). 4. Uncertainty of the light wavelength detected at each Littrow configuration (\(u(\lambda i)\))  
In the proposed method, the light wavelength \(\lambda_{\text{pos\_}i}\) or \(\lambda_{\text{neg\_}i}\) for pitch measurement is evaluated based on Equation (7) as the peak wavelength of the obtained spectrum. Therefore, light intensity fluctuation at each light wavelength and the uncertainty of the reading in the optical spectrum analyzer could contribute to the uncertainty \(u(\lambda_{i})\) of the light wavelength detected at each Littrow configuration. In this study, a value (\(7.10\times 10^{-4}\) nm) from the previous work by the authors' team [24] was employed as \(u(\lambda_{i})\). (e) Uncertainty of the refractive index in air pair (\(u(nair)\))  
The contribution of \(n_{air}\) was then estimated. Since the experiment had been conducted in a laboratory environment, the contributions of the atmospheric pressure, air temperature,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sources of Uncertainty**} & \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{ \begin{tabular}{c} **Uncertainty** \\ **Value** \\ \end{tabular}  } & \multirow{2}{*}{**Probability**} & \multirow{2}{*}{**Divisor**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{1}{c}{**Sensitivity**} & \multirow{2}{*}{\(|c|\text{$|c|$1-\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \  
and humidity in the room atmospheric range should be considered. It should be noted that a lot of work has been made so far to evaluate the contribution of \(n_{\text{air}}\). In this study, taking a value from the literature [25, 26, 27, 28], \(u(n_{\text{air}})\) was estimated to be \(\pm 5\times 10^{-8}\)[26].
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 3 Dynamic Environment Target Identification and Localization (DETIL)
### Identification and Localization of Interest Targets
#### 3.2.1 Coordinate System Transformation from Pixel Coordinates to World Coordinates  
Most target detection algorithms provide bounding boxes in the image's pixel coordinate system. When using an RGB-D camera or other depth sensors to simultaneously  
Figure 4: Flowchart of the obstacle detection algorithm based on depth images.  
capture the depth of the image, it is possible to transform coordinates from the pixel coordinate system to the world coordinate system through coordinate transformation. This allows for the acquisition of the coordinates of interest targets in the two-dimensional image in the world coordinate system.  
Given a camera resolution of \(w\cdot h\), and a field of view (FOV), the following is known:  
\[c_{x}=\frac{w}{2},c_{y}=\frac{h}{2} \tag{13}\]  
The focal length of the camera on the x-axis and y-axis can be calculated using the following formula:  
\[f_{x}=\frac{c_{x}}{tan(\frac{ FOV}{2})},f_{y}=\frac{c_{y}}{tan(\frac{ FOV}{2})} \tag{14}\]  
From this, the camera's intrinsic matrix K can be obtained:  
\[K=\begin{pmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{pmatrix} \tag{15}\]  
If the coordinates of a point in the pixel coordinate system are \((u,v)\), and the corresponding depth is \(z\), then its coordinates \(P\) in the camera coordinate system are as follows:  
\[P=ZK^{-1}\begin{pmatrix}u\\ v\\ 1\end{pmatrix}=Z\begin{pmatrix}\frac{\frac{u-c_{x}}{f_{x}}}{\frac{u-c_{y}}{f_{ y}}}\\ 1\end{pmatrix} \tag{16}\]  
Subsequently, based on the relationship between the camera and the intelligent agent's position, the coordinates are transformed from the camera coordinate system to the intelligent agent's coordinate system. Finally, using the position information of the intelligent agent, the coordinates are transformed to the world coordinate system.  
\[\begin{pmatrix}X_{w}\\ Y_{w}\\ Z_{w}\end{pmatrix}=R_{x}(\theta_{1})R_{y}(\theta_{2})R_{z}(\theta_{3})P+ \begin{pmatrix}\Delta X\\ \Delta Y\\ \Delta Z\end{pmatrix} \tag{17}\]  
where  
\[R_{x}(\theta)=\begin{pmatrix}1&0&0\\ 0&\cos\theta&\sin\theta\\ 0&-\sin\theta&\cos\theta\end{pmatrix},R_{y}(\theta)=\begin{pmatrix}\cos\theta &0&-\sin\theta\\ 0&1&0\\ \sin\theta&0&\cos\theta\end{pmatrix},R_{z}(\theta)=\begin{pmatrix}\cos\theta& \sin\theta&0\\ -\sin\theta&\cos\theta&0\\ 0&0&1\end{pmatrix} \tag{18}\]
## 4 Results  
### Validation of the Numerical Model  
In order to simplify the work, the correctness of the numerical model is verified only by the measured data from the start of construction to the completion of the excavation of the 2 nd underground floor.  
Figure 4 shows the measured ground settlements and the numerical simulation results at the locations of the monitoring points DB-1 and DB-2. It can be seen that the measured data are consistent with the simulation results. In addition, Figure 5 gives the settlement  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Thickness or** \\ **Diameter** \\ **\(t\) or \(d\) (m)** \\ \end{tabular} & \begin{tabular}{c} **Elastic Modulus** \\ **\(E\) (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Poisson** \\ **Ratio** \\ **\(\
u\)** \\ \end{tabular} &
\begin{tabular}{c} **Unit Weight** \\ **\(\gamma\) (kN/m3)** \\ \end{tabular} \\ \hline Piles & 1.50 & 32,500 & 0.2 & 23.4 \\ Columns & 0.80 & 45,000 & 0.2 & 24.5 \\ The diaphragm wall & 0.80 & 30,000 & 0.3 & 23.4 \\ Core tubes & 0.80 & 37,000 & 0.2 & 24.0 \\ The roof of the 1 st underground floor & 0.50 & 34,500 & 0.2 & 23.7 \\ The roof of the 2 nd to the 4 th underground floor & 0.25 & 32,500 & 0.2 & 23.5 \\ The roof of the 5 th underground floor & 0.60 & 32,500 & 0.2 & 23.5 \\ The bottom slab of the 5 th underground floor & 1.50 & 32,500 & 0.2 & 23.5 \\ The side wall & 0.70 & 37,000 & 0.2 & 24.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of structure components.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process** & **Clarification of the Status** \\ \hline
1 & Initial ground stress equilibrium \\
2 & Apply the existing buildings loads \\
3 & Construct the diaphragm wall, the core tubes, the piles, and the steel pipe columns \\
4 & Excavate 6.7 m downwards and construct the roof of the 1 st underground floor \\
5 & Excavate 2.3 m downwards, construct the bottom slab and the side wall of the 1 st \\  & underground floor, and construct the superstructure to the 5 th floor \\
6 & Excavate 4.1 m downwards, construct the bottom slab and the side wall of the 2 nd \\  & underground floor, and construct the superstructure to the 8 th floor \\
7 & Excavate 3.6 m downwards, construct the bottom slab and the side wall of the 3 rd \\  & underground floor, and construct the superstructure to the 10 th floor \\
8 & Excavate 4.0 m downwards, construct the bottom slab and the side wall of the 4 th \\  & underground floor, and construct the superstructure to the 15 th floor \\
9 & Excavate 4.8 m downwards and construct the bottom slab and the side wall of the 5 th \\  & underground floor \\
10 & Construct the superstructure from the 16 th to the 23 rd floor \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical simulation of synchronous construction processes.  
history curves of existing buildings, indicating that the simulation results also match the measured data for building settlement. From Figures 4 and 5, it can be concluded that the numerical model is reasonable, and the numerical model will be used to analyze the whole construction process.
## 2 Materials and Methods
### Clustering  
A drawback of the histogram approach is that the cell border may occasionally pass through the idler structures. Consequently, some points belonging to the idlers might not be included among the idler candidates during the density segmentation stage. To address this issue, the clustering stage was introduced. This stage uses a clustering algorithm to recover missing points in the idler candidates identified during segmentation. The specifics of this stage are that the objects to form the clusters are of irregular shape; therefore, the classification to the clusters is based on the distance of a point in question to the points already classified as the idler frame. The initial cluster cores are formed by the idler candidates from the previous stage. An algorithm used for this purpose is Euclidean clustering from the PCL library. The algorithm was proposed in [45] and it is presented in Listing 1.  
The action of the algorithm may be modified by setting 3 parameters: distance defining neighborhood \(d_{th}\), and minimum \(c_{min}\) and maximum \(c_{max}\) size of accepted clusters.  
The points that passed the density filter become the idler frame candidates for Euclidean clustering. The remaining points are tested as to whether a point that is their nearest neighbor belongs to a frame candidate and whether the distance to that point is smaller than a predefined maximum distance. After processing all the points, the clusters are verified to contain a number of points within predefined limits. The accepted clusters are passed to the next stage.  
```
let \(C\) be empty list of clusters, \(Q\) list of points to be checked for every point \(p_{i}\in P\): add \(p_{i}\) to \(Q\) for every point \(p_{i}\in Q\) do: search for the set \(P_{i}^{k}\) of point neighbors of \(p_{i}\) in a sphere with radius \(r<d_{th}\) for every neighbor \(p_{i}^{k}\in P_{i}^{k}\): check if the point has already been processed, and if not add it to Q if the list of all points in Q has been processed: if number of points in Q is within the limit \((c_{min},c_{max})\): add Q to the list of clusters C reset Q to an empty list STOP if all points \(p_{i}\in P\) processed and are now part of the list of point clusters
```  
**Listing 1** Euclidean clustering [45], PCL library implementation.
## 4 Discussion
### Socio-Economic Impacts  
#### 4.2.1 Democratizing Access to Mentorship and Innovation  
SPARK-IT has the potential to transform innovation ecosystems by democratizing access to mentorship and resources. The platform uses decentralized architecture and AI-driven matchmaking. This bridges the gap between innovators and experts, no matter their geographic location or socio-economic background. Innovators from under-represented regions, startups, and academic institutions can gain access to high-quality mentorship and collaboration opportunities traditionally reserved for well-connected individuals or larger organizations. This democratization fosters inclusivity, empowering a broader range of voices and ideas to contribute to global innovation.
## 2 Proposed Circuit Arrangement
### Non-Inverting Behavior  
This mode explains the voltage in phase operation of the proposed topology by ensuring the phase difference between the output and input voltage is zero that is why it is treated as voltage in-phase operation. The detail of this operation is depicted and highlighted in power transfer loops of Figure 6a,b for positive and negative input voltage, respectively. During the positive input voltage, the anode voltage of the diode \(D_{1}\) is at a higher potential with respect to the anode voltage of the diode \(D_{3}\). The diode \(D_{1}\) conducts as the cathode voltage of \(D_{1}\) and \(D_{3}\) are equal because they are connected to the same point. In the same way, the anode of the diodes \(D_{2}\) and \(D_{4}\) are equal as they are connected to the same point. The cathode voltage of diode \(D_{4}\) is positive, so it ensures the forward biasing of diode \(D_{2}\) as it has low cathode potential. The turning on of the switching transistor \(Q_{1}\) and \(Q_{2}\) transfers the input power to output through the highlighted loop, as shown in Figure 6a. Here, the output voltage is in phase with the input voltage.  
In the same way, once the input voltage is negative, the anode voltage of diode \(D_{3}\) and cathode voltage of \(D_{2}\) is positive that ensures the forward biasing of the diodes \(D_{3}\) and \(D_{4}\). The highlighted power transfer loop of Figure 6b in which the switching transistor \(Q_{3}\) and \(Q_{4}\) are on validates the negative output voltage or voltage in-phase operation. This behavior can also be validated by employing the concept of the volt-second product of the inductor.  
The value of inductor voltage during the overlapping interval (\(t_{ov}\)) and non-overlapping interval (\(t_{con}\)) can be realized in Equations (3) and (4), respectively.  
\[v_{L}(t)=v_{s} \tag{3}\]  
\[v_{L}(t)=(v_{s}-v_{o}) \tag{4}\]  
Figure 6: Power transfer loops for operation; (a,b) voltage in phase; (c,d) voltage out of phase.  
These values may be treated as constant as switching frequency is chosen much higher than the frequency of the input source. In a steady-state, the inductor current value at the start and end of the switching intervals becomes equal. Therefore, the average value of the inductor voltage in one switching interval should be zero that may be written as:  
\[v_{s}\cdot t_{ov}+t_{con}\cdot(v_{s}-v_{o})=0 \tag{5}\]  
\[\frac{v_{o}}{v_{s}}=\frac{t_{ov}+t_{con}}{t_{con}}\simeq 1 \tag{6}\]  
This ensures the voltage in phase operation with either polarity of the input voltage, that is to say:  
\[v_{o}(\omega t)=V_{m}sin(\omega t) \tag{7}\]
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
## 3 Results and Discussion
### Impact of Disorder in Solar Cells
In contrast, solar cells with a higher degree of radius disorder exhibit lower J\({}_{\text{SC}}\) than solar cells with a moderate degree of radius disorder. This different behavior is explained as follows. In the case of the position disorder, the improvement of J\({}_{\text{SC}}\) is mainly explained by a diffuse wave vector transfer. This follows from the Fourier transforms of the topographies of the different disordered grating textures, which show the strongest diffuse part in the case of the position disorder. Figure 5: (**a**) External quantum efficiency of a-Si:H solar cells with different degrees of position disorder in the grating textures. The position disorder is parameterized by the Gaussian width \(\sigma_{\text{P}}\). (**b**) External quantum efficiency of a-Si:H solar cells with different degrees of radius disorder in the grating textures. The radius disorder is parameterized by the Gaussian width \(\sigma_{\text{r}}\). The increase of \(\mathrm{J_{SC}}\) in the case of the radius disorder is mainly explained by a broadening of the waveguide mode. Here, the Fourier transforms of the textures show only a weak diffuse part. Furthermore, the local layer thickness varies strongly from unit cell to unit cell due to the isotropic growth of the a-Si:H layer. This leads to a broadening of the resonance condition of the waveguide mode. A further increase of the radius disorder results in a disturbance of the waveguide mode because the differences between the individual resonances of the unit cells become too large to form an extended optical mode in the layer. This explains the decrease of \(\mathrm{J_{SC}}\) for larger radius disorder. The two presented ways of implementing disorder into a grating texture are merely two possible examples. Of course, there are many other possibilities to vary the shape of the particles or their spatial distribution. The investigated position and radius disorder represent two extreme cases where the two basic physical mechanisms are dominating, respectively. How other types of disorder impact the light coupling to waveguide modes and, thereby, solar cell efficiency is open to further research.
# Micro Vibration Measurement with Microscopic Speckle Interferometry Based on Orthogonal Phase
## 4 Experiment
### Realization of Surface Measurement  
We have completed the measurement of vibration of speckle interference. Then, we will show how to use our method to realize surface measurement.  
The key of this method is to find the pixels that satisfy the orthogonal characteristics. We choose the speckle pattern when the amplitude is 40 nm and the frequency is 10 Hz. It can be seen that the interference imaging area is approximately a circle. The resolution of the image is 160 \(\times\) 120 pixels. We select the 120 \(\times\) 120 pixels area for calculation, and the starting pixel is (1, 21). Then, the selected area is traversed with a 6 \(\times\) 6 pixel window, and 20 \(\times\) 20 windows can be obtained, as shown in Figure 10a. It can be seen from Figure 10a that the four corners of the red square dotted frame are black and do not contain vibration information, so the windows at these positions need to be excluded. Then, we need to find one point that satisfies the orthogonal characteristics in each of the remaining windows. After calculating the vibration amplitude of these points, the surface distribution can be obtained by connecting these points and smoothing. In the end, we get an approximately circular surface distribution, as shown in Figure 10d. Among these points, the maximum amplitude obtained is 41.8 nm, the minimum amplitude is 38.7 nm, the average value is 40.3 nm, and the standard deviation is 0.28.