esa-sceva/satcom-chunk-collection huggingface dataset:
# Computing the Gromov-Wasserstein Distance between Two Surface Meshes Using Optimal Transport
## 5 Computational Experiments
### Shape Correspondence: Synthetic Data from SHREC19
It consists of four key components, namely modeling of the deformation (assumed to be anisotropic and non-isometric), computing the correspondences, pruning those correspondences, and optimizing the deformation [41]. The third method, KM, is a kernel-based method, where the registration problem is formulated as matching between a set of pairwise and pointwise descriptors, imposing a continuity prior on the mapping [42]. The fourth method, GISC, uses a genetic algorithm to find the permutation matrix that encodes the correspondences between the vertices of the two shapes to compare [43]. Note that this is the most similar method to the GW formalism, as this permutation matrix is akin to a transport plan. Finally the fifth method, WRAP, is based on the commercial software WARP that includes a wrapping tool that non-rigidly fits one 3D shape to another, from which correspondence can be derived. We report the results of the comparisons of the qualities of the different methods in Table 1 as the areas under the curve (AUC) for the cumulative distribution functions of the geodesic normalized errors. Results are divided according to the test sets of SHREC19, as well as summarized over all test sets. There are a few observations we can make based on Table 1. First, the AUC values provides a quantification of the quality of a method for finding correspondence: the larger the value, the better the method. In particular, Table 1 confirms that the correspondences computed from the GW framework with temperature annealing (Algorithm 2) are better than those computed with the fixed point method described in Algorithm 1. Second the four methods based on registration, RTPS, NRP, WRAP, and KM, all perform better than the method that only compute correspondences. This is likely due to the fact the the deformations included in the SHREC19 dataset are all based on a mathematical morphing, and therefore are expected to be captured with a mapping function. Finally, the GW formalism performs better than a genetic algorithm (implemented in GISC). This genetic algorithm is the one closest to the GW formalism in its concept.
# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 1 Introduction
For example, to express that the _honesty_ dimension derives from _reliability_, we use the rule _reliability_\(\xrightarrow{z}\)_honesty_. Here, dimensions such as _reliability_ and _honesty_ are not mere logical literals but real variables with values ranging from 1 to 5, where 5 (and 1) denote maximum (and minimum) trustworthiness, respectively. The parameter \(z\) is a real value quantifying the inferred trust variable.This approach allows multiple trust variables to contribute to the derivation of another, avoiding the complexities of traditional logic programming by adjusting \(z\) values. Furthermore, we introduce the T-Pattern Network (TPN), a framework that represents trust and reputation values alongside dependencies among trust dimensions across all smart objects. In prior work, we presented the theoretical foundation of the T-pattern model [11] but did not demonstrate its capacity for trust inference. To fully implement the T-pattern model and network, inference techniques are required to derive logical rules, potentially utilizing neural networks, fuzzy logic, Bayesian methods, or other approaches. To validate the T-pattern model's ability to infer trust dimensions, we developed its first real implementation. For this purpose, we employed CILIOS (Connectionist Learningand Inter-Ontology Similarities) [12] as the inference engine. CILIOS is a connectionist learning approach that generates logical rules to model agent behavior using a concept graph. It leverages neural-symbolic networks where input and output nodes correspond to logical variables. By observing actor behavior, CILIOS autonomously derives ontologies, requiring only the definition of relevant concepts and categories by the model designer. Using this CILIOS-based implementation, we conducted experiments to assess the model's effectiveness in enabling smart objects to estimate their partners' trustworthiness. The obtained results confirm the T-pattern model's ability to accurately represent dependencies between trust dimensions--an anticipated yet unverified outcome before our experiments. Different inference engines could be integrated into the T-pattern model depending on various application requirements, such as scalability, memory efficiency, or processing time. However, addressing these aspects goes beyond the scope of this work and represents a challenge for future research.
## 4 Experiment Results
Figure 9: ROI-2 (chest) region depth signal, showing the periodic chest wall movement from respiration. The amplitude of the chest wall movement is 4 mm. To analyze the agreement between the reference device (finger pulse oximeter) against our system, we plot the Bland-Altman plot for the HR from the 29 participant study in Figure 11. The left plot illustrates the results with motion compensation, and the right plot illustrates the results without using motion compensation. The \(x\)-axis is the average HR reading of the reference oximeter and our ToF-based system, while the \(y\)-axis is the deviation of the HR measured in the ToF system from the reference oximeter reading. With compensation, the mean value of the deviation is \(-6.25\) BPM, and \(95\%\) of the deviation falls within \(+/-35\) BPM of the mean deviation. In contrast, without compensation the mean value of the deviation is 21 BPM, and \(95\%\) of the deviation falls within \(+/-36\) BPM of the mean deviation. The large standard deviation rises from the extreme erroneous values of the measurement system. These extreme values rise from camera noises as well as intensity artifacts that are not reflected in the depth signal. After the dominating motion artifacts are removed, the system could wrongly pick up these artifacts as HR. This also explains why the wrong measurements of the uncompensated signal groups around the top left corner of the plot since the dominating motion artifacts are typically at a lower frequency compared to HR. Additionally, because the motion artifacts are likely to be mistakenly picked as HR, it also leads to the seemingly small standard deviation of errors for the uncompensated HR measurements. The RR measurements are also examined for the pilot study of 29 participants. Figure 12 shows the percentage of 286 ToF-based RR measurements that fall into different bins of deviation from the reference Masimo pulse oximeter. The mean deviation from the reference reading is found to be 1.7 BPM while 60% of measurements are within \(+/-2\) BPM from the reference RR measurements. However, in our measurements, we can also sometimes see deviation as large as 10 BPM, these errors can be attributed to large body motion as well as the loose clothing that the participants are wearing. Some examples will be discussed in the discussion section. Figure 11: [PERSON]–[PERSON] plot for HR measurements on all 286 measurements with motion compensation (**left**) and without motion compensation (**right**).
# An Efficient Meshless Numerical Method for Heat Conduction Studies in Particle Aggregates
## 5 Results and Discussion
### Single Arrangement and Fully Dispersed Particles
Abbreviations; MLPG: meshless local Petrov-Galerkin, MLS: moving least squares, DC PSE: discretization-corrected particle strength exchange, ref: local refinement, FE: finite element, DEM: differential effective medium approximation. also appears in Figure 9c to a lower extent. This increase was not predicted by any of the EMT analytical models. Figure 8: Single arrangement of spherical particles. (**a**) \(f_{p}=0.3\), (**b**) \(f_{p}=0.7\). Figure 9: Effective thermal conductivity of the single arrangement as a function of the volume fraction of the inclusions for four different conductivity ratios (\(\mathrm{k_{r}}\)), (**a**) 1/100, (**b**) 1/10, (**c**) 10, (**d**) 100. Continuous lines: analytical expressions. Blue lines: Maxwell. Black lines: SC. Green lines: DEM. Dashed lines: simulations. Red: MLPG. Black: FE. Abbreviation; SC: self-consistent approximation, DEM: differential effective medium approximation MLPG: meshless local Petrov-Galerkin, FE: finite element. The commercial software package Comsol Multiphysics(r), which is based on the FE approach, was used to evaluate the effective conductivity in the same particle system. For more advanced cases, where the closely packed spherical objects were interconnected, such as those in Figure 8b, special treatment from the user was required, due to the isolated or/and small regions that were created. This special case was the result of highly packed configurations of spherical objects, and the necessity for special numerical treatment was even more pronounced for real granular particles, which usually deviate from purely spherical shapes, Figure 8b. The approximation of the close packing of non-ideally spherical particles during reconstruction was achieved by allowing overlapping between the spheres to account for their closely-packed shape. Meshing of these intersected regions is usually characterized by increased difficulty, since the mesh generation at these areas is complicated. In addition, these small overlapped areas can be of the same order of magnitude as the minimum element size applied, which potentially leads to element-free spaces. To overcome this, the mesher generates a new finer mesh each time, eventually requiring a large number of elements and, consequently, a high computational cost. The software offers alternative options during the geometry construction, as well as in mesh generation procedure that can treat the overlapped areas. However, the treatment requires manual user input, which may further require trial and error processes. The latest software editions make this process easier; however, some difficulty in mesh construction was encountered when the geometry contained spheres whose surfaces are in contact or intersected with each other, as was the case of the aggregates of the present work.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 3 Methodology and Problem Definition
### Genetic Algorithm
#### 3.3.3 Variant Crossover  
Crossover operators are fundamental genetic algorithms that combine the genetic information from two parent chromosomes to produce offspring. In the context of the routing problem, standard crossover methods were found to be inadequate due to the unique structure of the problem at hand and the specific representation of the routes. To address this, a customized crossover method was developed to meet the specific requirements of the problem, enabling the generation of new routes while retaining successful segments from the parent routes.  
The heuristic rules are defined as follows: if the parent chromosomes share common nodes, a random common node is selected as the crossover point, and the route segments preceding and following this node are exchanged between the parents to create two offspring. When no common nodes are present, the crossover point is positioned near the midpoint of each route, combining segments before the crossover point in one parent with segments after the point in the other parent. The resulting chromosomes are then refined and sorted to ensure their validity and consistency within the constraints. Every selected parent pair underwent crossover in each generation by using a crossover probability of 1.0. This probability was chosen to maintain genetic diversity and facilitate effective recombination across generations. Figure 4 depicts an example of the crossover process with and without common nodes.
## 3 Results
### Wind Speed-Direction Joint Distribution  
In Figure 3, the distribution of wind speed in different wind directions can be seen intuitively. The wind directions in this area are mainly in the north and south directions, which are consistent with the climate characteristics of the southern coastal areas of China. This paper uses the AL model to describe the joint distribution of wind speed and wind direction. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Height (m)**} & \multicolumn{3}{c}{**Goodness-of-Fit Test (A–D Test)**} \\ \cline{2-4}  & **Lognormal Distribution** & **Weibull Distribution** & **Gamma Distribution** \\ \hline
30 & 117.67859 & **3.12768** & 29.16908 \\
40 & 101.15522 & **3.77735** & 20.73015 \\
50 & 86.83476 & **11.36016** & 21.57331 \\
60 & 83.15549 & **5.2625** & 22.8545 \\
70 & 84.38502 & **4.56088** & 24.49062 \\
80 & 88.26657 & **3.90411** & 25.50058 \\
90 & 84.07432 & **3.72759** & 22.2613 \\
100 & 90.78233 & **4.77145** & 27.25642 \\
110 & 93.99505 & **7.09786** & 32.82279 \\
120 & 102.19729 & **5.5742** & 38.62026 \\
130 & 108.79653 & **5.66863** & 41.60047 \\
140 & 105.66157 & **4.06106** & 39.41389 \\
150 & 106.74012 & **3.00408** & 38.5011 \\
160 & 96.69295 & **2.45435** & 33.79262 \\
170 & 104.77383 & **2.79012** & 36.42134 \\
180 & 107.23918 & **2.47474** & 35.762 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Goodness-of-fit test of distribution model. Figure 2: Wind speed probability distribution. Research shows [10] that the sixth-order mvM can describe the wind direction distribution well, and that the low-order mvM can fit \(g(\zeta)\) well. This paper calculated the fitting parameters of the mvM from the second to the seventh order for the wind direction distribution. Due to space limitations, this article only gives the fitting results of different orders of the 30 m height, as shown in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Height (m)** & \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) \\ \hline
30 & 3.64485 & 2.00181 \\
40 & 3.39006 & 1.80455 \\
50 & 3.55701 & 1.7978 \\
60 & 4.3308 & 2.28137 \\
70 & 4.57121 & 2.37895 \\
80 & 4.64485 & 2.37083 \\
90 & 4.56443 & 2.27996 \\
100 & 4.70691 & 2.30711 \\
110 & 4.87823 & 2.36004 \\
120 & 5.14665 & 2.51522 \\
130 & 5.19367 & 2.48331 \\
140 & 5.36856 & 2.57419 \\
150 & 5.47269 & 2.59135 \\
160 & 5.58688 & 2.60495 \\
170 & 5.65285 & 2.56085 \\
180 & 5.75441 & 2.54396 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of the Weibull distribution model at different heights. Figure 3: Wind rose at different heights. In our calculations, we found that using the process shown in Figure 5 to determine the parameters could improve the accuracy of the mvM model:
## 4 Experiments and Analysis
### Entity Location Prediction Experiment  
Due to the specificity of the predefined relationship classes in the SemEval 2010 Task 8 dataset, this section verifies experimentally whether the model confuses the relative positions of two entities. We conducted experiments using the PTKRE-Pos model, which we obtained by removing only the entity position prediction layer from the PTKRE model.  
We have displayed two experimental results of the model predictions in Table 8. Observing the prediction results of the PTKRE-Pos model on the test set, we can see that this model confuses the relative positions of the two entities, which results in incorrect predictions. Then, we employed the PTKRE model to predict the test set once more, and we compared the prediction results with those of the PTKRE-Pos model. The results show that with the addition of the entity location prediction layer, the model can accurately  
Figure 3: Impact of different prompt on F1.  
determine the relative locations of entities, thus obtaining correct prediction results. These experimental results show that the entity location prediction layer can effectively assist the model in recognizing the entity location, thus improving the model's performance.
# Road Traffic Gesture Autonomous Integrity Monitoring Using Fuzzy Logic
## 2 Review of Related Work
However, the leader-selection approach in [20], detrimental to a truly ubiquitous self-organizing traffic control system, was repeated here. Large memory requirements would also be necessary for such heavyweight algorithms. The implementation choice involved the use of a Basic Safety Message (BSM) in the sensing step, WAVE Short Message (WSM) in the leader selection step, and Signal Phase and Timing (SPaT) for broadcast. The WSM was also used during the handover to newly elected leaders. Release of the elected leader was consequential to broadcast stoppage. A tablet was also added for the visual display of the red, yellow, and green lights. The results reported included the system viability in Non-Line of Sight (NLoS) situations for real world applications. The VTL approach in [21] was also based on [20], the difference being the projection of traffic information on the windshield. The introduction of additional images to its custom graphical user interface to prompt users approaching intersections of traffic situations from distances of up to 200 m had an additional advantage over previous VTL implementations. First, a traffic-signal-ahead indication was given, followed by signs indicating priorities at the intersection. Tests were also performed to compare the impact of VTL and roadside mounted traffic lights on the deceleration of vehicles approaching the intersection. Higher deceleration values were recorded in the case of the VTL over that of the conventional traffic lights. As with all new technologies, further testing would be necessary to improve user acceptance comparable to that of the existing traffic control devices. Unfortunately, none of the foregoing approaches to in-vehicle traffic control or the VTL took weather elements into consideration. Research efforts have successfully addressed challenges involving space segment radiation, satellite clock to receiver clock matching, ionospheric and tropospheric interference, multipath interference, and some handling errors to the extent that the GPS receiver can perform geolocation under all climatic conditions. Accurate global positioning systems guarantee that a vehicle's location at all road intersections can be determined. Receiver equipment are getting smaller and cheaper. Some are so accurate that their locations can be pinpointed within 1 to 5 cm accuracy [26]. A truly ubiquitous in-vehicle traffic light system based on fuzzy logic can rely on existing GPS capabilities for accurate input data under all climatic conditions.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset
#### 5.1.1 Feature-Level Fusion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Appearance**} & \multicolumn{4}{c}{**Appearance + Gait**} \\
**Backbone** & & & & & & **(Concatenation)** & \\  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline OSNet [16] & 59.20 & 82.60 & 89.34 & 94.80 & 70.93 (+11.73) & 89.47 (+6.87) & 93.80 (+4.46) & 97.27 (+2.47) \\ MGH [7] & 85.60 & 97.10 & 99.00 & 99.30 & 90.40 (+8.00) & 98.67 (+1.57) & 98.94 (\(-\)0.06) & 99.67 (+0.37) \\ PiT [10] & 92.07 & 98.93 & 99.80 & 100 & 93.07 (+1.00) & 99.34 (+0.41) & 99.94 (+0.14) & 100.00 (+0.00) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Person Re-ID performances (rank-1/rank-5/rank-10/rank-20) on the iLIDS-VID dataset depending according to the model with or without concatenated gait information.
## 1 Introduction
In order to clarify the effect of citrate on particle size and PSD, the coating with citrate was carried out by co-precipitation of magnetite NPs in the presence of citrate (in situ) and by adsorbing citrate ions on the NPs after synthesis (ex situ). The size of the individual NPs was determined by analysing the width of the XRD peaks not only employing the well-established Scherrer and Williams-Hall methods but also by using a procedure introduced by [PERSON] [24] and a fitting analysis based on the Rietveld approach [25; 26]. The last methods allow one to obtain information about the PSD. In addition to that, the size of the primary NPs prepared under various conditions was estimated by fitting the magnetization vs. the intensity of magnetic field curves [27; 28; 29]. The results of the XRD analysis are compared with those of the fitting of the magnetization curves to corroborate the conclusions derived from this study. These methods provide more reliable results than the analysis by Transmission Electron Microscopy (TEM) or Atomic Force Microscopy (AFM) when NPs form aggregates. Due to particle aggregation the size of the individual particles cannot be determined by means of an automated analysis of TEM images and the manual observation is likely to overestimate the particle mean size since the isolated and larger ones are more easy to be seen [30]. DLS was used to determine the size of the aggregates of NPs in the dispersions. Zero Field Cooling/Field Cooling (ZFC/FC) curves and EPR spectra were measured in order to derive information about the interactions between bare and citrate-coated (prepared in both ways) magnetite NPs.
## 5 Energy Consumption on Active Tilting System
### Social Acceptance from Viewpoint of Energy Consumption  
Energy consumption for tilting is only about one thirteenth of the saving effect of cornering drag; thus, the total efficiency of PMVs with an inward tilting mechanism is better than without a tilting mechanism. Additionally, smaller and lighter PMVs are significantly more efficient than general cars, as shown in Figure 38. These benefits of PMVs with an active inward tilting mechanism show sufficient social acceptability.  
Figure 36: Consumed and saved energy on AMS evaluation course.  
Figure 37: Energy balance on AMS course and comparison with rolling resistance energy.
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion
### End-to-End Delay Analysis  
End-to-end delay analysis measures the time taken for packets to traverse the network and is based on calculating the mean delivery time for successfully transmitted packets. This analysis accounts for scenarios where packet transmission, network topology, and network latency exhibit their worst performance. The proposed technique demonstrated superior results compared to other methods, as illustrated in Table 6 and Figure 4d.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 5 SA-Based 5G V2X Services  
In the next generation of vehicular communication for the V2X services, the 3 GPP has made significant contributions. In this regards, SA-based 5G communication technology is envisioned for future V2X communication in various technical reports and specifications of _Rel-16_ and _Rel-17_. With SA-based 5G support for V2X services, the future V2X use case scenarios are possible. It goes without saying that the current support will be evolved in future releases i.e., _Rel-18_ and _Rel-19_ of 3 GPP standardization. In what follows next, we analyze how SA-based 5G communication technology is enabling the V2X services and the success stories associated with them.