esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Anonymity Analysis
### Anonymity Equation  
The ideal case when \(\mathbf{d(\alpha,\beta)}\) is equal to 0 indicates the system is perfectly secure. Therefore, the proposed techniques should provide \(\mathbf{d(\alpha,\beta)}\) very close to 0 to confirm the high anonymity of our techniques, since the neural network in the previous subsection created some probabilities of the false positive and false negative. These probabilities can be used to generate \(\alpha\) and \(\beta\), respectively.  
Table 2 shows that \(\mathbf{d(\alpha,\beta)}\) is very close to 0 in all three techniques using different rates which satisfy a very small \(\epsilon\) that is required by most of the applications in order to have a high level of anonymity.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rate** & **DUD** & **DAD** & **CAD** \\ \hline
0.05 & 50.4 & 49.4 & 56.5 \\
0.1 & 48.1 & 48.6 & 53.0 \\
0.15 & 52.3 & 51.3 & 53.4 \\ \hline \end{tabular}
\end{table}
Table 1: Error Percentage for testing data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**DUD**} & \multicolumn{3}{c}{**DAD**} & \multicolumn{3}{c}{**CAD**} \\ \cline{2-10} \multirow{2}{*}{**Rate**} & \(\alpha\) & \(\beta\) & \(\mathbf{d(\alpha,\beta)}\) & \(\alpha\) & \(\beta\) & \(\mathbf{d(\alpha,\beta)}\) & \(\alpha\) & \(\beta\) & \(\mathbf{d(\alpha,\beta)}\) \\ \hline
0.05 & 0.499 & 0.494 & 0.0001 & 0.508 & 0.504 & 0.0004 & 0.442 & 0.426 & 0.0507 \\
0.1 & 0.521 & 0.516 & 0.0004 & 0.517 & 0.511 & 0.0023 & 0.472 & 0.468 & 0.0104 \\
0.15 & 0.481 & 0.474 & 0.0058 & 0.491 & 0.486 & 0.0015 & 0.471 & 0.461 & 0.0134 \\ \hline \end{tabular}
\end{table}
Table 2: \(\mathbf{d(\alpha,\beta)}\) based on different \(\alpha\) and \(\beta\) values.  
Figure 12: CAD with rate of (**a**) 0.05; (**b**) 0.1 and (**c**) 0.15.  
All analysis models prove that the proposed techniques provide a high level of anonymity and the global adversary cannot recognize the existence of the real event. Subsequently, this leads to location and time privacy of the real event.
## 4 Results
### Sounding Model Validation
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Satellite** & **Model** & **NDT mAP [CI]** & **FGD mAP [CI]** \\ \hline PlanetScope & XGBoost & 0.956 [0.932, 0.987] & 0.886 [0.857, 0.911] \\ PlanetScope & CNN-RESISC & 0.930 [0.901, 0.963] & 0.885 [0.861, 0.916] \\ Sentinel-2 & XGBoost & 0.932 [0.904, 0.980] & 0.889 [0.858, 0.917] \\ Sentinel-2 & CNN-RESISC & 0.959 [0.941, 0.974] & 0.903 [0.880, 0.925] \\ Sentinel-2 & CNN-BigEarthNet & 0.933 [0.906, 0.966] & 0.839 [0.801, 0.873] \\ Landsat 8 & XGBoost & 0.899 [0.866, 0.942] & 0.878 [0.855, 0.904] \\ Landsat 8 & CNN-RESISC & 0.901 [0.866, 0.942] & 0.824 [0.793, 0.854] \\ Landsat 8 & CNN-BigEarthNet & 0.865 [0.826, 0.899] & 0.811 [0.784, 0.837] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: On/off classification cross-validated mean average precision (mAP) and block-bootstrapped 90% confidence intervals for 2015–2022. -frequent capturing of the on/off and capacity factor identification at power plants, which produces a greater number of total observations in each year. Appendix D includes some additional results validating our sounding models on non-training plants in the US, Europe (ENTSO-E), Australia, and Turkiye. On these plants, our NDT classification models using Sentinel-2 and PlanetScope imagery performed similarly with an mAP between 0.74 and 0.78, while the Landsat 8 models received lower average classification mAP scores of \(\sim\)0.72. These NDT classification results are worse than the cross-validation performance but are still significantly (alpha = 0.1) better than a naive baseline of 0.5 mAP. The NDT regression models' overall average performance was 0.22, 0.23, and 0.27 for PlanetScope, Sentinel-2, and Landsat 8 models, respectively, which is comparable to the cross-validation performance above. For the FGD model validation, only three plants with FGD had reported data available to compare against, but a similar trend was seen as the Sentinel-2 and PlanetScope models performed better than the Landsat 8 models.
# Synthesis, Characterization, and Self-Assembly of a Tetrathiafulvalene (TTF)-Triglycyl Derivative
## 3 Results and Discussion  
### Synthesis of TTF-Triglycyl Derivative  
The synthesis of the TTF-triglycyl derivative **14** was achieved by assembling the l-threoninol, and triglycine units with the TTF carboxylic derivative **8** (Scheme 1). First, compound **8** was prepared following the procedure described in the literature [10] with slight modifications. The first step consisted in the synthesis of 4,5-bis-(butylthio)-l,3-dithiole-2-thione (**3**) by alkylation of the zinc complex of bis(1,3-dithiole-2-thione-4,5-dithiolate) tetrabutylammonium salt (**1**) with 1-bromobutane (**2**) as described for similar compounds by [PERSON] and co-workers [77]. Phosphite-induced coupling of compounds **3** and **4**[77] gave the symmetric TTF derivative **5** in good yield (64%). Selective sequential deprotection of one 2-cyanoethyl group followed by alkylation with 1-bromobutane (**2**) gave the TTF derivative **6** in very good yield (89%). A second round of sequential deprotection of the second 2-cyanoethyl group followed by alkylation with 3-bromopropionic acid (**7**) [78] gave the desired TTF carboxylic derivative **8** in 91% yield.  
**Scheme 1. Synthesis of TTF-triglycyl derivative **14**.  
Then Boc-protected triglycine **9** was activated with DCC and 4-nitropenol (**10**). Then, the obtained nitrophenyl active ester **11** was reacted with L-threonineol (**12**) yielding the _t_-butoxycarbonyl (Boc)-protected triglycine-L-threonineol derivative **13**. This compound was treated with a TFA solution in DCM to remove the Boc-amino protecting group yielding the corresponding L-threonineol-triglycine trifluoroacetate salt that was subsequently used without purification for the coupling with compound **8** in the presence of [PERSON]'s base. This reaction was done via classical activation of the carboxyl group with DCC and NHS, affording the desired TTF-based gelator **14** in 24% (isolated yield).
## 3 Calculation of Coal Pillar Width of Gob-Side Entry Driving
### Study on the Reasonable Coal Pillar Width
These values are substituted in Equation (13) to obtain the internal field range \(x_{0}=12.58\) m. The constraints of the reserved coal pillars in gob-side entry driving is to arrange roadways within the internal stress field. The 4602 head roadway is designed 4.6 m wide, and the width of coal pillars should not exceed 8 m.  
A reasonable width should ensure not only the stability of the roadway surrounding the rock during excavation, but also the safety and stability of the working face during recovery. Although the 4602 head roadway can be arranged in a low stress environment by reserving 3-5 m small coal pillars, the 4602 working face belongs to an island face which is also affected by the residual coal from the #1 coal seam. Hence, the 4602 working face is likely to experience large-scale instability and damage. Internal fractures of coal pillars may develop, leading to poor stability and air leakage in the gob, unfavorable for fire prevention. Meanwhile, according to the above calculation, the roadway should also be arranged outside the influence range of the coal pillar stress concentration in the upper coal seam, so the coal pillar width is preliminarily determined to be in the range of 6-8 m. Due to the limitations of theoretical model calculation, the final coal pillar width is determined by calculating the stress distribution characteristics of coal pillars with different widths through numerical simulation.
## 3 Robust Extended Kalman Filter  
Extended Kalman Filter (EKF) is an extended application of Kalman filter in solving nonlinear optimal filtering problems. It is used in a wide range of technological fields (radar, electronic vision, communication). However, in hybrid positioning, the large outliers can negatively impact its application to position estimation. According to [17; 18], the resulting Kalman filter outliers can be sorted into three types: observation, innovation and structural outliers. In this paper, robust extended Kalman filter is used to handle observation and innovation outliers. Robust-EKF is presented as the combination of the Extended Kalman Filter with the Iterative Reweighted Least Squares algorithm (Section 3.3) with a weight matrix determined by MM-estimation (Section 3.2). Moreover, a combination of Robust-EKF with the RAIM algorithm (Section 3.4) is used to check the accuracy of user zone.
## 4 System modeling
### Supervised learning methods
#### 4.1.1 Principal component regression (PCR)  
As a simple machine learning method, least square (LS) regression is the most popular method to create a map from independent variables \(\mathbf{X}\) to dependent variables \(\mathbf{Y}\). The major problems for ordinary LS methods are that (1) the sample number must be equal to or larger than the number of model parameters (i.e., the number of independent variables) and (2) possible co-linearity in the independent variables could result in the ill-condition of a regression matrix and eventually lead to an unreliable solution. One feasible way to solve these problems is to create a regression model based on principal components, called PCR [21]. Thus, the regressors are decorrelated and the number of regressors is also significantly reduced. However, the extraction of principal components is solely reliant on the independent variables (sensor signals), so the direction of maximal variance in PCA is not related with the dependent variables, which usually results in the modeling effect of PCR being a little poorer than the effect of a model to be discussed next.
## 2 Internet of Vehicles and Smart Cities
### Main Challenges
#### 2.4.1 Standardization  
One of the main challenges in IoV is the need to develop standards and protocols to achieve interoperability and ease the development of applications. Many consortia and organizations are trying to develop these standards and protocols that answer to the requirements posed by IoV, and there are already some prominent protocols for these systems, as showcased in Figure 5. These protocols are utilized in IoT as well, as the two paradigms share similarities [31]. Even so, with the imminent turn to the fifth generation, i.e., 5G, these protocols will probably need to be revised.
# Influence of Dielectric Plate Parameters on the Reflection Coefficient of a Planar Aperture Antenna
## 3 Numerical Results  
The goal of this article is to investigate the influence of all possible parameters of the dielectric plate on the change in the total reflection coefficient S\({}_{11}\). The parameters are divided into structural-geometric (thickness d and dielectric density \(\rho\)) and electromagnetic parameters (relative dielectric constant \(\epsilon_{\rm r}\), dielectric losses tg \(\delta\) and tg \(\delta_{\mu}\), dielectric conductivity \(\sigma\), and relative dielectric permeability \(\mu_{\rm r}\)).  
The antenna and the dielectric plate are modeled with Ansys HFSS simulation software for solving electromagnetic problems using the finite element method. The antenna model is shown in Figure 6.  
The dimensions (width h\({}_{1}\), length h\({}_{2}\)) of the grounded plane and dielectric plate are h\({}_{1}\) = 423 mm (2.51 \(\lambda\)) and h\({}_{2}\) = 450 mm (2.67 \(\lambda\)), and the radius and length of the waveguide are r\({}_{\rm w}\) = 61 mm (0.276 \(\lambda\)) and l\({}_{\rm w}\) = 171 mm (1.02 \(\lambda\)), respectively. The space inside the waveguide is defined by \(\epsilon_{1}\) and \(\mu_{1}\) (the waveguide is air-filled). The dielectric plate is characterized by \(\epsilon_{2}\), \(\mu_{2}\), and the thickness d. The space following behind the dielectric plate is free space, and it is characterized by \(\epsilon_{0}\), \(\mu_{0}\).  
The dominant mode of the waveguide with a circular cross-section is TE\({}_{11}\) with the corresponding cut-off frequency f\({}_{\rm cTE11}\). The next mode is TM\({}_{01}\) with the cut-off frequency f\({}_{\rm cTM01}\). Therefore, numerical calculations were carried out in the frequency range f\({}_{\rm cTE11}\) < f < f\({}_{\rm cTM01}\).
## 1 Introduction
The superposition of grid reconstruction errors with discretization errors results in the difficulty of reproducing temperature distributions under strong temperature gradients within a reliably reconstructed grid size. Consequently, some researchers have enhanced the reconstruction process beyond the reconstruction algorithm, thereby improving AP reconstruction performance. For the modification of domain discretization, [PERSON] [30] used appropriate central nonuniform domain discretization and transceiver collocation to significantly improve the accuracy of acoustic pyrometry. [PERSON] [25] successfully tracked the shift pattern of the hotspot via the spatial moving sample (SMS) method on hexagonal meshing. Based on the coarse grid temperature averages obtained from the reconstruction algorithm, [PERSON] [11] and [PERSON] [31] built neural networks to predict the temperature distribution on a refined grid. However, the nonuniform discretization proposed by [PERSON] is fixed at the center and requires a large number of transceivers (at least 44). The objective of [PERSON]'s study is a simple temper ature field with single heat source in a small circular region. And it is difficult to obtain sufficient reliable sample data, which limits the practicability of neural networks. In this research, a novel temperature reconstruction method for acoustic pyrometry under strong temperature gradients and limited measurement points is proposed and demonstrated. The key metrics include the reconstruction accuracy, the accuracy of peak temperatures and hotspot positions. To quantitatively assess the temperature gradient of the reconstructed temperature field, the coefficient of heating effect (\(C_{\text{HE}}\)) is first proposed, and the existing acoustic temperature tomography studies are evaluated [4, 6, 11, 14, 17, 20, 28, 32, 33, 34, 35, 36, 37, 38, 39]. Then, an adaptive hybrid kernel method based on Multiquadric (MQ) and Gaussian (G) radial basis function was proposed and utilized to reconstruct the temperature field. After that, the effect of nonuniform meshes on improving performance is investigated and a novel adaptive grid evolution strategy (AGES) is introduced. Consequently, the finalized AGES-AHK method implements adaptive kernel adjustments on AGES-optimized nonuniform grids, integrating the optimization of AHK and AGES. The primary benefit of the proposed AGES-AHK method lies in its ability to accurately reconstruct temperature fields under strong gradients and limited measurement points, whereas traditional acoustic pyrometry methods typically fail. In comparison to conventional approaches which rely on the uniform grid discretization and a fixed radial basis function approximation, the AGES-AHK method adaptively optimizes both the grid density and the kernel functions based on the specific characteristics of the temperature field, which allows for precise tracking of hotspots and significantly improved reconstruction accuracy. Qualitative and quantitative analyses demonstrate the AGES-AHK method's superior performance in challenging conditions.
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 3 Nonlinear Mobility Model
### Gauss-Markov Mobility Model
#### 3.1.1 Gauss-Markov Variables  
GM has several adjustable variables. The main tuning parameter is \(\alpha\), which controls the level of randomness of the model. Another important parameter is time step, which represents how frequently the new values are calculated, and, additionally, the values of the standard deviation of the speed and direction. In the following, we explain the effect of each of these variables.  
Alpha \(\alpha\): The variable \(\alpha\) is the tuning parameter for GM. Setting \(\alpha\) to a value between 0 and 1 allows us to adjust the degree of the model randomness. When \(\alpha=1\), the model will be predictable and lose all of its randomnesses, and the new direction and velocity will be the same as the previous direction and velocity. Thus, when \(\alpha=1\), the drone moves in a straight line:  
\[v_{t} =v_{t-1} \tag{3}\] \[\theta_{t} =\theta_{t-1}\]  
On the other hand, when \(\alpha=0\), the model will become memoryless, and the new direction and velocity will depend on the mean and standard deviation values of the direction and velocity and the Gaussian random variables.  
\[v_{t} =\mu_{\varphi}+\sigma_{\varphi}w_{v_{t-1}} \tag{4}\] \[\theta_{t} =\mu_{\theta}+\sigma_{\theta}w_{\theta_{t-1}}\]  
Time step: The time step greatly affects the dynamics of GM. As the speed and direction will be fixed until the next time step, choosing a large time step will cause a long period of straight movements.  
Standard deviation: The value of the standard deviation for the different variables will directly impact the trajectories as it affects the range of possible change of these variables. A much greater standard deviation than the averages produces different movement patterns than if the value is small. Choosing small values for the standard deviation of the direction will make a small direction change at each step, which leads to smoother turns. On the other hand, choosing large values for the standard deviation of the direction can lead to more significant direction change at each step, which leads to sharper turns.  
The same thing applies for the speed; choosing small values for the standard deviation of the speed will make a small speed change at each step. For example, if the average speed equals 3, if we want speed values to be within the interval [2.5-3.5] (approximately), the speed standard deviation should be 0.1, but if we want speed values to be in the interval [2-4] (approximately), the speed standard deviation should be 0.2, and so on.  
Thus, the standard deviations need to be set carefully based on the physical characteristics of the UAV.
###### Abstract  
In this paper, we present the smallest form factor microstrip-fed ultra-wideband antenna with quintuple rejection bands for use in wireless sensor networks, mobile handsets, and Internet of things (IoT). Five rejection bands have been achieved at the frequencies of 3.5, 4.5, 5.25, 5.7, and 8.2 GHz, inseminating four rectangular complementary split ring resonators (RCSRRs) on the radiating patch and placing two rectangular split-ring resonators (RSRR) near the feedline-patch junction of the conventional ultra-wideband (UWB) antenna. The design guidelines of the implemented notched bands are provided at the desired frequency bands and analyzed. The measured results demonstrate that the proposed antenna delivers a wide impedance bandwidth from 3 to 11 GHz with a nearly omnidirectional radiation pattern, high rejection in the multiple notched-bands, and good radiation efficiency over the entire frequency band except at the notched frequencies. Simulated and measured response match well specifically at the stop-bands.  
UWB antenna; rejection bands; rectangular complementary split ring resonator (R-CSRR); rectangular split-ring resonator (RSRR); Internet of things (IoT) +
Footnote †: journal: Sensors
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 5 Technological Trends and Challenges  
The fusion of computer vision (CV) and artificial intelligence (AI) in collaborative robotics has already demonstrated considerable advancements across perception, decision-making, and interaction modalities [95; 96]. Research momentum is also being built around  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Description** & **Key Advantages** & **Example Applications** \\ \hline Deep Learning & End-to-end models for & High accuracy; handles & Visual part detection; \\ Architectures [65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83] & perception and planning & complex scenes & semantic mapping \\ \hline Reinforcement Learning (RL) & Learning adaptive policies & Handles dynamic, & Adaptive path planning; \\
[75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86] & via trial-and-error & changing tasks & force-sensitive assembly \\ \hline Explainable AI (XAI) and & Interpretable, safe & & Visual feedback; regulatory \\ Ethical Autonomy [87; 88; 89; 90] & decision-making & Improves trust and safety & compliance \\ Cognitive Architectures & Human-like integration of & Enables proactive, intuitive & Intention prediction; \\
[91; 92; 93; 94] & perception and planning & interaction & shared task handover \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of AI-driven decision-making and autonomy methods.  
multimodal sensor fusion, embodied AI agents, and open-source robotic ecosystems [97]. These trends point toward the development of proactive cobots that are capable of understanding intent, adapting behavior in real time, and collaborating seamlessly with humans in complex, dynamic environments [98].  
However, while the reviewed literature showcases significant progress in the perception, reasoning, and adaptive behavior of cobots, a deeper analysis reveals that, despite notable advances in perception and control architectures, significant challenges remain in achieving seamless, real-time, and trustworthy human-robot collaboration. Several key thematic insights and limitations warrant critical discussion to inform the next generation of human-centered collaborative systems.
## 2 Materials and Methods  
For all the DFT calculations presented here, we use the plane wave code CASTEP [37; 38], with the PBE [39] exchange-correlation functional. Furthermore, the ultrasoft pseudopotentials [40] were used for all of our calculations. The wave basis's cut-off energy was converged at 480 eV, in conjunction with a 2 \(\times\) 2 \(\times\) 3 k-point Monkhost-Pack [41] grid. For our calculations, we used a supercell of 108 atoms. To encounter the effect of localized electrons, onsite Coulomb repulsions [42] of 8.2 eV were set for the 3d orbitals of Ti [15], 2.8 eV for the 3d of Cr [43], 2.3 eV for Mo 4d, and 2.1 eV for W 5d [44]. We have also examined other values for the +U parameter, but the band gap was significantly underestimated. For the DOS calculations, a denser mesh of 5 \(\times\) 5 \(\times\) 5 k-points was applied. The convergence criteria for the undoped TiO\({}_{2}\) and doped structures were chosen as (a) the SCF tolerance of 2.0 \(\times\) 10\({}^{-5}\) eV/atom, (b) max force tolerance of 0.05 eV/A, (c) max stress tolerance of 0.1 GPa, and d) max displacement tolerance of 0.001 A. For the optical calculations, a denser mesh of 3 \(\times\) 3 \(\times\) 3 k-points was applied.