esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Three-Dimensional Line-of-Sight Analysis Using Ray Casting
In the 3D space, the FOV must be considered in two directions: horizontal and vertical. Under the assumption that the FOV is the same, the spacing between the rays decreases as the number of rays increases, thus enabling precise analysis. For example, in Figure 1, the number of collision points on the left side of the square is insufficient; therefore, the number of rays must be increased to obtain precise results. However, increasing the number of rays increases the amount of data to be processed, which, in turn, increases the computation time. The objective of this study was to evaluate the communication area by analyzing the LOS in an underground mine. To achieve this, the FOV and direction of the emitted rays must be appropriately determined. If the FOV is greater than \(180^{\circ}\), the rays are excessively dispersed and cannot be processed by the algorithm. Therefore, the FOV must be less than \(180^{\circ}\). In this study, to analyze the LOS from any point in all directions, rays were emitted with an FOV close to \(180^{\circ}\) in the direction of the normal vector perpendicular to the inner surface of the underground mine. The \"estimate_normals\" function in the Open3D library was used to calculate the normal vectors for all points. This function determines adjacent points and uses covariance analysis to calculate the principal axes [24]. If the rays are emitted in a direction other than the normal vector direction of the surface, incorrect LOS results are obtained, such as point A in Figure 2, and the communicable area can be missed. By contrast, point B is the result of emitting the rays in the direction of the normal vector of the inner surface of the underground mine. Figure 1: Concept of points connected by line-of-sight (LOS) using the ray-casting algorithm.
## 1 Introduction
[PERSON] [14] studied two-dimensional lidar to construct high-precision maps of parking lots to realize accurate real-time positioning and mapping. [PERSON] [15] finished an indoor positioning system based on ultra-wideband, which meets the needs of indoor positioning systems in most scenarios. The indoor positioning accuracy of the system can reach 10 cm. Although the strategy above-mentioned not only achieves the communication between parking lot and vehicle, but also between parking lot and parking space, it does not fulfill the interaction between parking space and vehicle. So the closed-loop communication is not formed. Moreover, the construction and maintenance of infrastructure transformation cost a lot. So it is not easy to promote on a large scale. In order to solve these challenges, a parking space recognition method based on parking space features construction in the scene of AVP is proposed in this paper. Firstly, because of the characteristics of low-cost, easy identification and large amount of stored information of the Quick Response (QR) code, it can be used as an information carrier and a new parking space feature that can be recognized by the vehicle to meet the needs of information interaction between parking spaces and vehicles. According to the characteristics of the fisheye camera and the installation location, the arrangement of the QR code on the parking space is proposed to improve the recognition efficiency. Secondly, the parking space images collected by the on-board camera were pre-processed. Since vehicles near the parking space may cross the parking line and the parking space is partially occupied, a method is designed to identify effective empty parking spaces. Thirdly, the template matching algorithm is used to match the QR code which is easy to get defaced. According to the characteristics of the standard binary matrix, a new similarity calculation formula is proposed to improve matching efficiency and accuracy. After the parking space feature is identified and located by the visual sensor, the parking space and its boundary is recognized according to the relative position relationship between the constructed feature and the parking space. This method simplifies the recognition algorithm and improves the reliability of the parking space recognition. Finally, the actual vehicle experiment verifies the feasibility and effectiveness of this method.
## 2 Background  
### Resource-Constrained Multi-Project Scheduling Problem (RCMPSP)  
In practical scheduling scenarios, companies typically manage multiple projects simultaneously. This multi-project case increases the complexity of the scheduling process due to the possible existence of resources that need to be shared across multiple projects. This challenge is studied under the term Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) [9].  
RCMPSP is an expanded version of the Resource-Constrained Project Scheduling Problem (RCPSP), which involves scheduling multiple projects concurrently while sharing common resources. This extension also incorporates consideration of each project's arrival time; that is, the time at which the activities can start. It is a practical problem that aims at minimizing project duration under resource constraints by determining start dates for each activity within a portfolio of projects. Since the RCMPSP, which is based on the RCPSP, is NP-hard [2; 10], it becomes computationally challenging for large-scale instances. Consequently, researchers have developed heuristics and meta-heuristics to solve the related problems [11; 12; 13; 14].
# Kernel Density Estimation on the Siegel Space with an Application to Radar Processing +
## 5 Conclusions  
Three non parametric density estimation techniques have been considered. The main advantage of histograms in the Euclidean context is their simplicity of use. This makes histograms an interesting tool despite the fact that they do not present optimal convergence rates. On the Siegel space, histograms lose their simplicity advantage. They were thus not deeply studied. The orthogonal series density estimation also presents technical disadvantages on the Siegel space. Indeed, the series become integrals, which make the numerical computation of the estimator more difficult than in the Euclidean case. On the other hand, the use of the kernel density estimator does not present major differences with the Euclidean case. The convergence rate obtained in [1] can be extended to compactly supported random variables on non compact Riemannian manifolds. Furthermore, the corrective term whose computation is required to use Euclidean kernels on Riemannian manifolds turns out to have a reasonably simple expression. Our future efforts will concentrate on the use of kernel density estimation on the Siegel space in radar signal processing. As the experimental section suggests, we strongly believe that the estimation of the densities of the \(\Omega_{k}\) will provide an interesting description of the different backgrounds. This non-parametric method of density estimation should be compared with parametric ones, as \"Maximum Entropy Density\" (Gibbs density) on homogenesous manifold as proposed in [37] based on the works of [PERSON]. As proposed in [38], a median-shift approach might also be investigated.  
The authors would like to thank [PERSON], [PERSON] and [PERSON] for the help they provided in the understanding of symmetric spaces and the Siegel space.  
[PERSON] carried out the mathematical development. [PERSON] has set up the Radar clutter segmentation. [PERSON] has introduced Poincare/Siegel Half space and Poincare/Siegel Disk parameterization for Radar Doppler and Space-Time Adaptive Processing based on Metric spaces deduced from Information Geometry. This parameterization has been re-used in this paper. [PERSON] was the Ph.D. supervisor of [PERSON] and participates in the supervision of master thesis of [PERSON], both thesis are at the origin of this study. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 6 Data Encoding and Decoding
### Communication Modulation
#### 6.1.1 Modulation of Exfiltration Communication
Taking binary phase modulation as an example, when the symbol is set to \"0\", the modulated carrier remains in phase with the unmodulated carrier; whereas, when the symbol is set to \"1\", the modulated carrier undergoes a 180\" inversion from its original state. The waveform representing the difference in carrier phases between \"0\" and \"1\" exhibits a 180\" change, and for 2 PSK, it follows a modulation pattern of \"01001001\", as illustrated in Figure 21.  
Figure 19: A waveform modulated by 8-amplitude ASK. Figure 20: Time-domain and frequency spectrum of FSK signal modulated by four frequencies. (**a**) 4 FSK time-domain signal; (**b**) 4 FSK signal spectrum.
## 4 Incorporation of Synthetic Data
### Data Fitting with Two Parameters
#### 4.2.2 \(N=2\)  
In Figure 13, we demonstrate fitting \(b\) and \(\eta\) to synthetic data when \(N=2\). Figure 13a illustrates that \(b\) and \(\eta\) are both overestimated. As we have seen previously, overestimating both parameters can balance out and lead to a comparable territory to the true equilibrium. This is demonstrated when comparing the true territory, Figure 13b, to the estimated  
Figure 11: Iterations of SGD minimizing the negative log-likelihood function to estimate \(\eta\) using data generated with \(M=150\), \(N=2\), \(b=0.25\), \(a=1\), and \(\eta=5\) and the equilibrium solutions to System (2) using the parameter used to generate the data and the estimated parameter. (**a**) Iterations of SGD and corresponding estimated \(\eta\) (**top**) and negative log-likelihood value (**bottom**). (**b**) PDF used to generate data. (**c**) PDF from estimated parameters.  
Figure 12: Iterations of SGD minimizing the negative log-likelihood function to estimate \(\eta\) and \(b\) using data generated with \(M=300\), \(N=1\), \(b=0.25\), \(a=1\), and \(\eta=5\) and the equilibrium solutions to System (2) using the parameters used to generate the data and the estimated parameters. (**a**) Iterations of SGD and corresponding estimated \(b\) and \(\eta\) (**top**, **middle**) and negative log-likelihood value (**bottom**). (**b**) PDF used to generate data. (**c**) PDF from estimated parameters.  
territory, Figure 13c. The estimated territory is similar to the true territory, it is more aggregated and has a higher maximum value. As is true of other estimated territories, the genuine equilibrium solution is also more symmetric.
## 3 UVSQ-SAT Data Method and Map Reconstruction of the Variables (Observations and Model)
### UVSQ-SAT Data Processing to Obtain Observation Time Series
#### 3.1.1 Instrumental Equations of the Earth's Radiative Sensors
By combining information from two ERS sensors on the same face (sensor with carbon nanotubes (solar absorption close to 1) and sensor with optical solar reflector (solar absorption less that 0.1)), we get the albedo (\(a\)) of the planet and the outgoing longwave radiation (OLR) at a measurement point located in latitude and longitude on the world map (Equations (2) and (3) are given as example if a satellite face observes at the nadir):  
\[\Phi p_{in}=\epsilon\times\text{OLR}\times\left(\frac{R}{R+z_{sat}}\right)^{2} \tag{2}\]  
\[\Phi a_{in}=\alpha\times a\times\left(\frac{R}{R+z_{sat}}\right)^{2}\times \cos(\xi)\times\text{TSI}\times\left(\frac{1\,au}{d_{s}}\right)^{2} \tag{3}\]  
where \(R\) is Earth's radius, \(z_{sat}\) is the satellite altitude, \(\alpha\) is the ERS (coating) solar absorption, \(\xi\) is the solar zenith angle, \(1\,au\) is one astronomical unit, and \(d_{s}\) is the distance UVSQ-SAT--Sun. During satellite eclipse periods, two ERS sensors on the same face measure only OLR, which allow to obtain direct measurements and inter-calibration of the sensors.
# Utilization of Yacon Damaged Roots as a Source of FOS-Enriched Sweet-Tasting Syrup
## 2 Materials and Methods
### Syrups Sweetness and Nutritional Features  
The theoretical relative sweetness (TRS), theoretical glycemic index (TGI), and caloric values of syrups were determined by accounting for the relative proportion of free sugars (Glc and Fru), Suc, FOS, and inulin, and their estimated relative sweetness, glycemia, and calories reported in the literature. For the estimation of syrup TRS (Equation (2)), a parameter that only accounts for the contribution of sugars to the sweetness, excluding potential synergisms and matrix effects, a relative sweetness level of 1.7 was used for Fru, 0.8 for Glc, and 0.4 for FOS [24; 25; 26]. Yacon inulin, being linear, was assigned a value of 0.1 for calculating the sweetening power of the syrups [13]. These values are normalized to sucrose, used as a reference sugar with a sweetening power of 1.0 at room temperature [24; 25; 26].  
\[\text{TRS}=\text{Fru}\ (\%_{w/w})\times 1.7+\text{Glc}\ (\%_{w/w})\times 0.8+\text{ Suc}\ (\%_{w/w})\times 1.0+\text{FOS}\ (\%_{w/w})\times 0.4+\text{ inulin}\ (\%_{w/w})\times 0.1. \tag{2}\]  
For the estimation of the TGI (Equation (3)), a parameter that only accounts for the contribution of sugars to the glycemic index, excluding potential synergisms, matrix effects, and variability among the health status of consumers, a relative glycemic level of 0.3 was used for Fru (average of 7 studies) and 1.5 for Glc (average of 11 studies) [27]. A relative glycemic index of 0.015 was used for FOS and inulin [25; 28]. These values were obtained by accounting for the relative glycemic indexes of each sugar and their normalization to sucrose, the reference sugar, with a relative glycemic index of 1.0 (average of 12 studies [27]).  
\[\text{TGI}=\text{Fru}\ (\%_{w/w})\times 0.3+\text{Glc}\ (\%_{w/w})\times 1.5+ \text{Suc}\ (\%_{w/w})\times 1+\text{FOS}\ (\%_{w/w})\times 0.015+\text{ inulin}\ (\%_{w/w})\times 0.015. \tag{3}\]  
For the estimation of syrups caloric value (Equation (4)), a caloric value of 4 kcal was used for Fru, Glc, and Suc and 2 kcal for FOS and inulin [29; 30].  
\[\text{Caloric value}=\text{Fru}\ (\%_{w/w})\times 4+\text{Glc}\ (\%_{w/w})\times 4+\text{ Suc}\ (\%_{w/w})\times 4+\text{FOS}\ (\%_{w/w})\times 2+\text{ inulin}\ (\%_{w/w})\times 2. \tag{4}\]
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 3 Results
### QoT Execution
#### 3.1.1 Effectiveness  
The interaction of LOA and workload did not have a significant effect on accuracy (F (3, 152) = 0.512, \(p\) = 0.675) and neither did the LOA (F (1, 152) = 1.024, \(p\) = 0.313) and workload (F (3, 152) = 0.376, \(p\) = 0.77) as main effects. Workload level however, had a significant effect on the performance in the secondary task (F (1, 32) = 4.23, \(p\) < 0.001) with MWL2 (M = 2.02, SD = 1.239) resulting in better performance compared to HWL (M = 1.93, SD = 1.047). All of the participants who did the secondary task finished the first stage of the game. The majority (71/80) reached the second stage of the game, 56/80 reached the third stage while only 10/80 reached the fourth stage.
# Industrial Cyber-Physical System Evolution Detection and Alert Generation
## 7 Conclusions and Future Work
\begin{tabular}{l l l l l l l l l} \hline \hline  & & \multicolumn{6}{c}{**500 Devices**} \\ \hline  & INSERT & & MODIFY & & REMOVE & & RANDOM \\ \hline  & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) \\ \hline
20 & 179.135 & 2.836 & 159.188 & 2.234 & 144.423 & 1.828 & 161.05 & 2.366 \\ \hline
40 & 198.863 & 3.486 & 164.873 & 3.699 & 127.603 & 1.774 & 161.808 & 2.217 \\ \hline
60 & 214.576 & 3.57 & 161.443 & 2.364 & 114.108 & 1.576 & 159.476 & 2.093 \\ \hline
80 & 233.163 & 4.82 & 160.746 & 2.112 & 94.59 & 1.258 & 154.081 & 1.793 \\ \hline
100 & 258.216 & 4.841 & 162.846 & 2.117 & - & - & 36.612 & 1.965 \\ \hline \multicolumn{8}{c}{**1000 Devices**} \\ \hline  & INSERT & & MODIFY & & REMOVE & & RANDOM \\ \hline  & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) \\ \hline
20 & 966.694 & 42.941 & 812.452 & 31.71 & 479.819 & 60.566 & 816.526 & 34.539 \\ \hline
40 & 1062.361 & 41.325 & 805.063 & 34.992 & 390.658 & 39.605 & 781.418 & 23.924 \\ \hline
60 & 1206.35 & 32.811 & 778.48 & 28.555 & 315.706 & 38.158 & 810.628 & 41.136 \\ \hline
80 & 1312.73 & 56.793 & 812.255 & 37.455 & 226.409 & 4.901 & 759.616 & 16.87 \\ \hline
100 & 1391.139 & 51.778 & 795.341 & 26.494 & - & - & 754.174 & 17.28 \\ \hline \multicolumn{8}{c}{**5000 Devices**} \\ \hline  & INSERT & & MODIFY & & REMOVE & & RANDOM \\ \hline  & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) \\ \hline
20 & 7504.666 & 253.046 & 7948.106 & 192.028 & 4599.335 & 601.986 & 7948.296 & 208.028 \\ \hline
40 & 8207.304 & 137.2 & 7983.115 & 199.595 & 3825.186 & 376.088 & 7871.655 & 223.714 \\ \hline
60 & 7932.337 & 251.416 & 7937.625 & 195.618 & 3794.048 & 401.019 & 7123.38 & 570.081 \\ \hline
80 & 7751.169 & 133.73 & 7906.775 & 186.239 & 3338.893 & 521.681 & 4872.885 & 694.389 \\ \hline
100 & 10,434.83 & 1422.032 & 8013.856 & 204.03 & - & - & 3743.969 & 259.904 \\ \hline \multicolumn{8}{c}{**10,000 Devices**} \\ \hline  & INSERT & & MODIFY & & REMOVE & & RANDOM \\ \hline  & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) & AVG & CI \(\pm\) \\ \hline
20 & 12,723.95 & 383.693 & 12,734.7 & 42.86 & 12,254.94 & 33.845 & 12,671.83 & 43.162 \\ \hline
40 & 14,616.28 & 82.877 & 12,754.4 & 35.86 & 12,168.72 & 402.94 & 12,565.88 &
# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 4 Results and Discussion
### Comparison with Other Different Models  
The following table presents a comparative analysis of this paper's model with five models from existing studies [38; 39; 40; 41; 42]. The overall accuracy, precision, recall, and F1-score metrics were evaluated and compared, and the results are displayed in the table. The accuracy, recall, and F1-score in this paper reached 0.8904, 0.9015, and 0.8876, respectively, which demonstrated the highest performance in the comparison experiments (Table 4).  
In order to gain insight into the learning status of dermoscopic images within the model, the optimal model was visualised and analysed using the Gradient-Weighted Class Activation Mapping (Grad-CAM) method. The results are presented in Figure 13. This method enables specific image regions that provide meaningful information for pre-model predictions to be highlighted. This paper employed Grad-CAM to visualise the decision-making mechanism for the predictions in the trained model. Grad-CAM is a gradient-based deep network visualisation method that elucidates the basis of deep neural network classification in the form of heat maps, which facilitate the interpretation of the classification judgments based on the pixel points of an image. The colours in the heat map indicate areas of interest, with red indicating a high degree of relevance to the target category and blue indicating a lesser degree of attention to the area. The purple area represents the result of filling in the blank area after data enhancement of the image. Grad-CAM facilitates the prediction of outcomes when observing the regions of interest that the model prioritises. As illustrated in the figure, the most considerable regions of interest that the model focuses on for dermatoscopic image classification are in the lesion region, indicating that the model has effectively learned the features of the lesion region.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Datasets** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
2D superpixels + RCNN [40] & HAM-10000 & 0.8550 & 0.8340 & 0.8450 & 0.8530 \\ ResNeXt101 [41] & ISIC-2019 & 0.8850 & 0.8740 & 0.8810 & 0.8830 \\ MobileNetV2 [42] & ISIC-2019 & 0.8530 & 0.7890 & 0.8633 & \\ VGG19 [43] & ISIC-2019, & 0.8857 & 0.9070 & 0.8666 & 0.8765 \\ ConvNet [44] & ISIC-2018, & 0.8690 & 0.8614 & 0.8747 & \\ Ours & ISIC-2019 & **0.8904** & 0.8737 & **0.9015** & **0.8876** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with existing models. Significant values are in [bold].  
Figure 13: Grad-CAM heat maps of dermatological images for the network proposed in this paper.
# MBSE Testbed for Rapid, Cost-Effective Prototyping and Evaluation of System Modeling Approaches
## 3 Results
### Logical Architecture of MBSE Testbed
#### 3.3.2 Rapid Scenario Authoring  
Eclipse Papyrus is used along with SysML and Unity 3D virtual environment for scenario authoring and definition of entity behaviors. The testbed offers scenario authoring and visualization for multiple domains. For example, Figures 3 and 4 show the results of autonomous vehicle scenario authoring. Figure 5 shows visualizations for aircraft perimeter security scenario. These exemplar scenarios are used in experimenting with planning and decision-making models and algorithms. The initial scenario contexts are defined in SysML (Figure 6), with Python XMI being used to extract data from the SysML model to populate the Unity virtual environment.  
The behaviors of scenario entities such as autonomous vehicles, pedestrians, UAVs, and surveillance cameras are defined in Unity. The UAV, pedestrian, and vehicle behaviors are defined using standard waypoint-following algorithms in Unity. The planning and decision-making algorithms are exercised and tested with both autonomous vehicle navigation and control operations, and multi-UAV operations in aircraft perimeter security mission. Figure 3 presents a visualization of a pedestrian crossing scenario, while Figure 3 presents a visualization of a four-way stop sign scenario. Similarly, Figure 4 presents a visualization of a vehicle crossing scenario, while Figure 4 presents a visualization of a vehicle braking scenario.  
Figure 5 depicts the aircraft perimeter security scenario with one UAV and one video camera conducting surveillance. On the bottom right corner of the figure, camera views are shown. Figure 5 presents the aircraft perimeter security with three UAVs. Eclipse Papyrus for SysML, Python XML, and Unity 3D are used to rapidly author the scenarios with various agents. The scenarios have static agents, perception agents, dynamic auxiliary agents, and system-of-interest (Sol) agents. Static agents such as standing aircraft, traffic signs, and buildings are part of the scenario context. Perception agents, such as cameras that capture simulation environment data, are used for processing. Dynamic auxiliary agents, such as pedestrians and auxiliary cars, as well as auxiliary UAVs, follow predefined behavior in experiments. The system-of-interest (SoI) agents such as the autonomous car or UAV are used to test different algorithms defined by the experimenters.  
Figure 4: (**a**) Vehicle crossing scenario, (**b**) vehicle braking scenario.  
Figure 3: (**a**) Pedestrian crossing scenario, (**b**) four-way stop sign scenario.  
Figure 6 presents a SysML representation of scenario context for the aircraft perimeter security example. Included in the context are the UAV, airstrip, building, surveillance camera, and aircraft. Attributes and operations of each of those entities are defined in specific blocks.
## 2 Materials and Methods
### Data Availability
#### 2.2.2 Fluxcom GPP  
The FLUXCOM GPP product is derived by training three machine learning algorithms using meteorological observations and satellite data as inputs, with daily carbon flux measurements from ground flux tower sites [45]. This product transforms the flux tower observations into a globally explicit distribution. It has been widely applied in various studies [46; 47]. In this research, the FLUXCOM GPP data for the period 2007-2015 with a resolution of 0.05\({}^{\circ}\) and an interval of months were utilized. The data download link is [[http://fluxcom.org/EF-Download/](http://fluxcom.org/EF-Download/)]([http://fluxcom.org/EF-Download/](http://fluxcom.org/EF-Download/)) (accessed on 18 May 2022).