esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Incorporating Estimation Noise  
Until now, we assumed that channel vectors \(h_{l,f}\) are available noiselessly to the predictor. In practice, channel information needs to be estimated via pilots. To elaborate on this point, let us assume the received signal model  
\[y_{l,f}^{\mathrm{P}}[i]=h_{l,f}x_{l,f}^{\mathrm{P}}[i]+n_{l,f}[i], \tag{10}\]  
where \(x_{l,f}^{\mathrm{P}}[i]\) stands for the \(i\)th transmitted pilot symbol in block \(l\) of frame \(f\), \(y_{l,f}^{\mathrm{P}}[i]\) for the corresponding received signal, and \(h_{l,f}\) for the channel with additive white complex Gaussian noise \(n_{l,f}[i]\sim\mathcal{CN}(0,N_{0}I_{S})\). Given an average energy constraint \(\mathbb{E}[x_{l,f}^{\mathrm{P}}[i]^{2}]=E_{x}\) for the training symbol, the average signal-to-noise ratio (SNR) is given as \(E_{x}/N_{0}\). From (10), we can estimate the channel as  
\[\tilde{h}_{l,f}=\frac{y_{l,f}^{\mathrm{P}}[i]}{x_{l,f}^{\mathrm{P}}[i]}=h_{l, f}+\frac{n_{l,f}[i]}{x_{l,f}^{\mathrm{P}}[i]}=h_{l,f}+\xi, \tag{11}\]  
which suffers from channel estimation noise \(\xi\sim\mathcal{CN}(0,\mathrm{SNR}^{-1}I_{S})\). If \(P\) training symbols are available in each block, the channel estimation noise can be reduced via averaging to \(\mathrm{SNR}^{-1}/P\). Channels \(\tilde{h}_{l,f}\) can be used as training data in the schemes described in the previous subsections. More efficient channel estimation methods, including sparse Bayesian learning [50] and approximate message passing approaches [51] may further reduce the channel estimation noise.
# Engineering Challenges Ahead for Robot Teamwork in Dynamic Environments
## 3 Teamwork Challenges
### Socio-Technical Concerns  
Even if a team of robots is able to operate autonomously and perform application tasks without human intervention, experience with self-adaptive applications shows that the human user does not always appreciate being out of the loop [70]. Self-adaptive systems may fail to meet user expectations, and autonomous actions may be inappropriate in certain user situations. In other words, the user wants to stay in control in certain situations, or, even more importantly, in safety critical application domains such as autonomous driving, the user must be able to override automatic decisions.  
This automation paradox, also called the irony of automation [70], is known since automated control systems took over tasks that were previously carried out by human operators. Psychologists identified human contribution in automated systems as not less but more important. A more advanced automated system denotes a more demanding interaction with the human user. In cases of failures or irregular conditions, humans should still have a chance to intervene. At all times, humans need to be protected against harm caused by the robot behavior. Clearly, this general insight related to automation applies also to the engineering of an MRT, especially if the MRT may self-adapt its plans to situations that the designer did not anticipate.  
In addition to the _human in the loop_ aspect, concerns about the social embedding of a robotic application solution arise when a team of robots operates in a dynamic environment where users and robots interact. Most of the concerns are of a general nature for adaptive systems, such as _transparency of decisions, trust in technology, fairness, privacy of context information, liability_, and more. Surely, these concerns play a crucial role for the user acceptance of any technical system and particularly in safety-critical applications. They apply to single robots, as well as multi-robot systems. However, one question remains unanswered so far in the literature:  
* Will the envisaged teamwork of robots, in comparison to a single robot application, create more complex or even additional challenges in respect to socio-technical design concerns?
# Traffic-Predictive Routing Strategy for Satellite Networks
## 4 Route Discovery  
In the route discovery phase, the G-AODV routing protocol uses the forward control mechanism and the path replacement mechanism to select the appropriate route effectively. The synergy of this dual mechanism is designed to ensure that heavy load nodes do not appear in the path of the build. At the same time, the network load is evenly distributed to avoid network congestion caused by excessive concentration of network traffic on certain nodes. Traffic load balancing is achieved by ensuring that each satellite node actively sends a congestion control signal to the upstream node after detecting a potential congestion node. Once the upstream node receives this signal, it re-evaluates the available neighbor nodes and selects a forward path for routing the request packet RREQ. This means that congestion-aware nodes avoid congested nodes, ensuring that data traffic can bypass areas that are already overloaded. This can effectively spread the network load and reduce the risk of congestion.
## 3 Results
### Stability
The first two rows (a-f) represent the operating points for no thrust nor torque, with the top row (a-c) representing a smaller airspeed, \(0.5V_{D}\), and thus smaller advance ratio. At the bottom (g-i), a thrusting operating point is depicted at \(V_{D}\) with a thrust coefficient of 0.1. Looking at the top two rows in Figure 12, the standard Houbolt/Reed method is found to be less conservative for \(C_{T}=0\) compared with the mid-fidelity methods (up to 10%  
Figure 12: Comparison of stability measure relative to Houbolt/Reed among different methods at three operating points (rows) and for three pylon lengths (columns). deviation, e.g., in plot (f)), while the quasi-steady Houbolt/Reed method shows a large conservative tendency (up to 40% deviation from the standard Houbolt/Reed). On the other hand, most mid-fidelity codes (i.e., UPM, DUST-Panel and DUST-UVLM) reveal consistency for all operating points and provide a good reference for analysis. The lifting line method is generally more conservative compared with Houbolt/Reed and the other mid-fidelity methods. The BEM+Wagner method shows more unstable predictions compared with the mid-fidelity methods but captures the same trends relative to the Houbolt/Reed results, only with smaller \(\Delta\omega_{stab}\) values. The differences in the stability predictions are in general higher for the operating point with higher advance ratio (i.e., \(C_{T}=0,~{}V=V_{D}\)) than the one at lower airspeeds (i.e., \(C_{T}=0,~{}V=0.5~{}V_{D}\)). The bottom row of the plots in Figure 12 contains results with non-zero thrust and torque for operating point \(C_{T}=0.1,~{}V=V_{D}\). The unsteady aerodynamics from the Houbolt/Reed method show no thrust nor torque dependency; thus, the reference value of \(\omega_{stab,Houbolt/Reed}\) remains constant for the bottom row. The zero-thrust operating point (i.e., \(C_{T}=0,~{}V=V_{D}\)) is found to lead to a more unstable system compared with \(C_{T}=0.1,~{}V=V_{D}\), indicating a conservative nature, which is consistent with the literature. All methods except Houbolt/Reed exhibit this trend. The reason for this can be found in the torque vector and the steady lift acting on the blades (a more detailed explanation is given in Appendix B).
## 3 Materials and Methods
### Problem Formulation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ID** & **Timestamp** & **U\({}_{x}\)** & \(a_{x}\) & **U\({}_{y}\)** & \(a_{y}\) & **b\({}_{x}\)** & **w\({}_{x}\)** & **b\({}_{y}\)** & **w\({}_{y}\)** \\ \hline
1 & 9 March 2013 10:00:01 & 4.37 & 22,242,219.9 & 1.03 & 4,800,692.9 & 0.23 & \(-\)5,093,637.6 & 0.97 & \(-\)4,645,833.3 \\
1 & 9 March 2013 10:00:04 & 13.4 & 22,242,156.2 & 5.83 & 4,800,651.2 & 0.075 & \(-\)1,659,862.40 & 0.17 & \(-\)82,3641.2 \\
1 & 9 March 2013 10:00:11 & 15.88 & 22,242,287.4 & 3.79 & 4,800,713.7 & 0.0946 & \(-\)2,103,289.59 & 0.26 & \(-\)1,267,515.2 \\
1 & 9 March 2013 10:00:19 & 27.3 & 22,242,427.4 & 11 & 4,800,762 & 0.04 & \(-\)814,740.93 & 0.09 & \(-\)436,432.91 \\
1 & 9 March 2013 10:00:21 & 27.3 & 22,242,427.4 & 11 & 4,800,762 & 0.04 & \(-\)814,740.93 & 0.09 & \(-\)436,432.91 \\
2 & 9 March 2013 10:00:03 & 2.92 & 22,256,723.4 & 7.32 & 4,804,052.4 & 0.3425 & \(-\)7,622,165.55 & 0.14 & \(-\)656,291.3 \\
2 & 9 March 2013 10:00:08 & 1.15 & 22,256,709.8 & 5.75 & 4,803,996 & 0.87 & \(-\)19,353,660.69 & 0.17 & \(-\)385,477.56 \\
2 & 9 March 2013 10:00:16 & 4.27 & 22,256,692.6 & 4.64 & 4,803,941.2 & 0.23 & \(-\)5,216,411.92 & 0.22 & \(-\)1,043,411.51 \\
2 & 9 March 2013 10:00:25 & 4.6 & 22,256,639.5 & 0.23 & 4,803,925.9 & 0.22 & \(-\)4,826,741.21 & 0.42 & \(-\)20,588,283.27 \\
2 & 9 March 2013 10:00:34 & 1.5 & 22,256,625.5 & 5.2 & 4,803,925.8 & 0.67 & \(-\)14,837,750.3 & 0.19 & \(-\)923,831.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An Overview of the Transformed Spatio-Temporal Database. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Time Moment** & **knns \(id\)** \\ \hline
1 & 1 & \([id_{111},id_{112},\ldots,id_{11k}]\) \\
1 & 2 & \([id_{121},id_{122},\ldots,id_{12k}]\) \\
1 & 3 & \([id_{131},id_{132},\ldots,id_{13k}]\) \\
1 & 4 & \([id_{141},id_{142},\ldots,id_{14k}]\) \\
1 & 5 & \([id_{151},id_{152},\ldots,id_{15k}]\) \\
2 & 1 & \([id_{211},id_{212},\ldots,id_{21k}]\) \\
2 & 2 & \([id_{221},id_{222},\ldots,id_{22k}]\) \\
2 & 3 & \([id_{231},id_{232},\ldots,id_{23k}]\) \\
2 & 4 & \([id_{241},id_{242},\ldots,id_{24k}]\) \\
2 & 5 & \([id_{251},id_{252},\ldots,id_{25k}]\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ N & 1 & \([id_{N11},id_{N12},\ldots,id_{N1k}]\) \\ N & 2 & \([id_{N221},id_{N22},\ldots,id_{N2k}]\) \\ N & 3 & \([id_{N31},id_{N32},\ldots,id_{N3k}]\) \\ N & 4 & \([id_{N41},id_{N42},\ldots,id_{N4k}]\) \\ N & 5 & \([id_{N51},id_{N32},\ldots,id_{N5k}]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(k\)-Anonymity Sets for \(N\) Mobile Users in \(L=5\) Timestamps. **Definition 7**. : \(k\)_-anonymous database: A database is \(k\)-anonymous, i.e., its records are \(k\)-anonymous with respect to the selected features, if \(k-1\) discrete records in the same specific timestamp \(\tau\), have at least the same nearest neighbors so that no record of \(k\) is distinguished from its \(k-1\) neighboring records._
## 4 Numerical Simulation Model  
### Geometric Model  
Figure 1 illustrates the pivotal point locations on the selected rotor. In this depiction, the \(X\)-axis represents the horizontal direction, with the rightward direction denoted as positive, while the \(Y\)-axis signifies the vertical direction, with the upward direction representing the positive axis. The rotor encompasses three key points: \(O_{2}\), \(O_{1}\), and \(O\). Point \(O_{2}\) is situated at the geometric center of the rotor, whereas Point \(O_{1}\) serves as the rotational center of the rotor. Point \(O_{1}\) undergoes counterclockwise rotation around the stator's geometric center, maintaining a defined radius of rotation. Point \(O\) marks the position where the rotor's initial location coincides with the geometric center of the stator. The motion equation of Point \(O_{1}\) is:  
\[\left\{\begin{array}{l}x_{B}=e\cdot\cos(\frac{\pi}{2}+\omega t)=-e\sin wt\\ y_{B}=e\cdot\sin(\frac{\pi}{2}+\omega t)=e\cos wt\end{array}\right. \tag{1}\]  
All other points rotate clockwise around Point \(O_{1}\), and their motion equations are:  
\[\left\{\begin{array}{l}x=x_{B}+L\cdot\sin(\varphi-\omega t)\\ y=y_{B}+L\cdot\cos(\varphi-\omega t)\end{array}\right. \tag{2}\]  
Let \(L\) represent the distance between the point in question and \(O_{2}\), \(\varphi\) denote the angle between the line connecting the point and \(O_{2}\) and the abscissa, and \(\omega\) indicate the angular velocity of the rotor's combined rotation and revolution. In this context, a counterclockwise rotation is considered positive, while a clockwise rotation is deemed to be negative. Consequently, the angular velocity for the rotor's revolution is denoted as \(\omega\), whereas the angular velocity for its rotation is represented as \(-\omega\).  
Based on these considerations, the motion equations for each point can be derived, taking into account the revolution radius denoted as \(e\) and the rotor radius designated as \(R\). Table 2 shows the trajectory equations and speed equations of each point on the rotor.
## 3 Results and Discussion
### Microstructure Analysis of NiTiNb Prepared by LSF
#### 3.1.2 Microstructure Features
The difference is that the microstructure of LSF-NiTiNb parallel to the building direction (z-direction) presents epitaxial growth morphology, and the microstructure perpendicular to the building direction (z-direction) presents equiaxed crystal morphology, while most of the microstructure of the NiTiNb prepared by traditional methods presents irregular solidification structure, and most of the microstructure is columnar. In the three positions, a small amount of Nb is dispersed in the matrix and a large amount of Nb is enriched at the grain boundary. For the atoms of Ni: Ti: Nb, as shown in Table 2, during the preparation process, due to the high temperature, the evaporation of elements will occur, the evaporation of Ni in position 2 and position 7 (perpendicular to the building direction) is relatively more, and the content of Ti in position 1 (parallel to the building direction) decreases more, indicating that the element distribution of NiTiNb sample prepared by LSF is uneven, and the evaporation of elements is different at different building heights. During preparation, the grains grow along the direction of the maximum temperature gradient, which is usually the same as the building direction (BD) [45]. In the additive manufacturing process, due to the inconsistency of temperature gradient, there are non-uniform nucleation and growth of equiaxed crystals and epitaxial growth of columnar crystals at the same time, which will produce a microstructure composed of coarse continuous columnar grains and fine inter columnar equiaxed grains [54]. Therefore, position 1 (Figure 10a) presents a discontinuous columnar morphology. Figure 10: EDS diagram of the selected position of sample L7 and sample L8: (**a**) sample L7 position 1, (**b**) sample L7 position 2, (**c**) sample L8 position 7.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 5 Fundamental Consistency Checks  
### Energy Conservation  
For an isolated system (\(\dot{S}=0\)), the total energy \(H\) is conserved:  
\[\frac{dH}{dt}=\underbrace{\frac{\partial H}{\partial t}}_{\text{ Explicit time dependence}}+\underbrace{[H,H]}_{\text{Poisson bracket}}-T\underbrace{[H,\overline{S}]}_{\text{Entropy coupling}}=0. \tag{82}\]  
For open systems, energy exchange with the environment is governed by the entropy gradient term \(T\
abla\overline{S}\), ensuring thermodynamic consistency.
## 4 Experimental Results
### Analysis of Results
#### 4.4.3 Experiment 3: Traffic Trend Prediction  
This traffic trend prediction experiment prepares traffic data of illegal red light running events on Yangjiang road in Jiulongp District, Chongqing, for six months in 2020. In this paper, the road trends are calculated using the mentioned road traffic trend prediction method. The results are the predicted roads that vehicles are monitored after the event, and the data of the control group are the actual road choices of vehicles.  
We compared the results of the proposed GT-AHP-EM method with AHP, EM and some well-known methods for road traffic situation assessment, such as the Combination of the Entropy Method and Variation Coefficient Method (EM-VC) [46] and the Bayesian network (BN) [47]. We performed the prediction evaluation based on the four metrics presented in Section 3.3. The results of the comparison with the four methods are shown in Table 12.  
As shown in Table 12, the proposed GT-AHP-EM method has the best overall _Accuracy_ and _Precision_ on the Traffic Trend Prediction Monitoring Dataset. Compared with the EM-CV  
Figure 7: Comparison of the weights of traffic trend indicators calculated by 3 methods.  
method, the GT-AHP-EM method has slightly higher _Accuracy_ and _Precision_ (both 0.23% more). However, the _Recall_ (0.41% lower) and _F1 score_ (0.07% lower) are slightly lower.  
The actual monitoring results of the dataset in this experiment are only 0 and 1. The 0 in the result means that the vehicle data associated with the emergency were not monitored after the event, and the 1 means that the vehicle information associated with the emergency was monitored. Once there is similar vehicle information with the emergency event, such as the same vehicle model, wrong license plate number identification, etc., it may be misjudged that the emergency event has affected the road traffic, and the final monitoring results will conclude that the emergency event has some impact on the road. This will lead to the occurrence of certain errors in the actual monitoring results and affect the evaluation of various methods in the predication comparison.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 5 Results
### Data Processing and Analytic Systems
#### 5.2.1 Spark Streaming Innovations  
In Algorithm A5, several enhancements are introduced to improve the real-time data-processing capabilities of Spark Streaming:  
* Real-time system monitoring: the system tracks CPU and memory usage before and after each batch, enabling efficient real-time resource management. This feature is vital for adjusting manufacturing processes based on available resources.
* Adaptive termination mechanism: the algorithm includes an automatic termination feature that stops processing when no new data are received beyond a defined threshold, optimizing resource utilization and enhancing energy efficiency.
* Enhanced aggregation of environmental data: temperature and humidity readings are averaged over time, providing real-time environmental data that are critical for precise manufacturing control.
* Cumulative metrics tracking: the algorithm logs average latencies, processing times, and resource usage, offering a detailed performance log that aids in refining CM processes or scaling for higher demand.
* Configurable query timeout: a flexible timeout parameter is included, allowing adaptability to various manufacturing needs, whether for continuous operation or on-demand scaling.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 2 Model Description
### Sky Division
#### 2.2.2 Division of Satellite  
To facilitate the partitioning of the field of view for NGSO satellites, we established a coordinate system for the satellite and introduced the definitions of the elevation and azimuth angles according to ITU Rec. S.1503-3 [27]. The elevation and azimuth angles are illustrated in Figure 2. In this coordinate system, the X-axis points to the east, the Y-axis points to the centre of the earth, and the Z-axis points to the north. The elevation angle of a pointing is defined as the angle between the pointing and the XOY plane, and the azimuth angle is defined as the angle between the projection of the pointing onto the XOY plane and the Y-axis. Given the vector of the pointing, \(p\) (\(p=(x,y,z)^{T}\)), the expressions for the azimuth and elevation angles are as follows:  
\[El=arcsin(\frac{z}{\sqrt{x^{2}+y^{2}+z^{2}}}), \tag{6}\]  
\[Az=arctan(\frac{x}{y}). \tag{7}\]  
Since the interference from an NGSO satellite can potentially affect the entire visible surface of the Earth, the maximum azimuth and elevation angles can be expressed as \(arcsin(\frac{R_{e}}{R_{e}+h})\), where \(R_{e}\) is the radius of the Earth and \(h\) is the orbit height of the satellite. This corresponds to the pointing that is exactly tangent to the surface of the Earth.  
Dividing the field of view of the NGSO satellite into cells with equal size elevation angle and azimuth angle gaps, the range of elevation and azimuth angles of cell \(G_{ij}^{sat}\) are denoted as \([i\times\Delta El-El_{max},(i+1)\times\Delta El-El_{max})\) and \([j\times\Delta Az-Az_{max},(j+1)\times\Delta Az-Az_{max})\), respectively, where \(\Delta El\) and \(\Delta Az\) are the lengths of a cell in elevation and azimuth angle and \(i\in\{0,1,\cdots,2\lceil E_{max}/\Delta El\rceil-1\}\), \(j\in\{0,1,\cdots,2\lceil Az_{max}/\Delta Az\rceil-1\}\). In this paper, we considered the splitting scheme such that \(\Delta El\) and \(\Delta Az\) were equal and the maximum elevation and azimuth angles were both \(arcsin(\frac{R_{e}}{R_{e}+h})\).
## 3 DAB LCC Resonant Converter TPS Control Validation
### Experimental Results with/without Rectifier AC Load Resistance Feedback Control
The measurement data shows that when R\({}_{\rm ac}\) = 38 \(\Omega\) control is enabled, power efficiency is greatly improved when a larger R\({}_{\rm dc}\) is connected, especially in lower output power operation conditions of DAB. The power efficiency for R\({}_{\rm dc}\) = 250 \(\Omega\) in [1 W, 5 W] output power range is improved from [30%, 40%] to [60%, 75%]. The efficiency variation range is reduced from [30%, 80%] for R\({}_{\rm ac}\) control disabled to [60%, 80%] with R\({}_{\rm ac}\) = 38 \(\Omega\) control enabled. The maximum power efficiency from measurement data around 80% is slightly lower than 86% shown in Figure 9b obtained from analytical calculation. The reason is that the SiC MOSFET switching loss and conduction loss of the DAB converter have not been included in the analytical calculation. Figure 25: Power efficiency measurement with/without R\({}_{\rm ac}\) feedback control. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{ \begin{tabular}{c} **Vo** \\ **(V)** \\ \end{tabular}  } & \multicolumn{3}{c}{**Disabled R\({}_{\rm ac}\) = 38 \(\Omega\) Feedback Control**} & \multicolumn{3}{c}{**Enabled R\({}_{\rm ac}\) = 38 \(\Omega\)**} \\  & \multicolumn{3}{c}{\(\beta\)s (deg) = 180\({}^{\circ}\)} & \multicolumn{3}{c}{**Feedback Control**} \\ \cline{2-9}  & **V\_Pri (V)** & **I\_Pri (A)** & **V\_Sec (V)** & **I\_Sec (A)** & **V\_Pri (V)** & **I\_Pri (A)** & **V\_Sec (V)** & **I\_Sec (A)** \\ \hline
10 & 17.23 & 0.654 & 20.34 & 0.713 & 9.18 & 0.299 & 14.79 & 0.437 \\ \hline
20 & 22.08 & 0.911 & 42.75 & 1.414 & 18.05 & 0.552 & 28.29 & 0.839 \\ \hline
30 & 26.21 & 1.113 & 64.3 & 2.07 & 28.4 & 0.818 & 42.15 & 1.22 \\ \hline
40 & 32.31 & 1.342 & 87.53 & 2.
## 3 Near-Field Simulation of Metasurfaces with Varying Dimensions
### Near-Field Simulation of the Compact Metasurface
#### 3.2.2 Normal Incidence of a Circularly Polarized Plane Wave on a Compact Metasurface  
The design simulation of a 20 \(\times\) 20 rectangular metasurface vortex wave in the full-wave simulation software is illustrated in Figure 9. The design mode is \(l=-3\), and the incident wave is circularly polarized. The calculated frequency is 12 GHz. Figure 9a displays the OAM spectrum observed at \(z=130\) mm. It can be observed that \(LH=-3\) dominates as the main polarization component, while \(RH\) right-hand circular polarization remains low as a cross-polarization component. A high-purity vortex wave with mode \(LH=-3\) is generated, exhibiting a maximum electric field amplitude of 1.83 V/m in Figure 9b. Figure 9c,d depict the calculated ring-shaped intensity of the left-polarized electric field and three-period helical phase, respectively, confirming that the metasurface generates an expected mode vortex wave characterized by a regular ring-shaped electric field amplitude. Figure 9e,f present both the intensity and phase of the calculated right-handed polarization electric field, demonstrating sufficiently low undesired right-handed circular polarization amplitudes and verifying the high conversion efficiency achieved by the designed metasurface. It can be seen that using circularly polarized wave excitation yields better results.  
Figure 8: Near-field calculation of vortex waves generated by the \(l=-3\) metasurface. (**a**) Noiseless. (**b**) The noise amplitude is 0.2. (**c**) The noise amplitude is 0.3.