esa-sceva/satcom-chunk-collection huggingface dataset:
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### Evaluation Logic Layer (_EVL_)
#### 4.3.2 Probabilistic Mapping of Manifestation Scenarios  
Scenario manifestations need to be mapped to create an executable model. Deterministic models, like mathematical formulas, and logical \"if-then\" relationships can be used for this, especially for physical or economic relationships. Figure 8 shows deterministic mappings using mathematical formulas. In the example, simple multiplication maps the relationship between (A) and (B). Binary logic is demonstrated by defining the _type of vehicle_ according to the powertrain (e.g., _electric or gasoline_). For _electric_ powertrains, binary logic  
Figure 8: Semantic understanding and symbolic representation of causal relationships.  
assigns 0 g/km, but for _gasoline_ powertrains, the calculation formula uses 120 g/km for _influence_ (_B_) (The difference between deterministic and probabilistic assignments lies in the fact that deterministic assignments yield the same fixed result for the same input, whereas probabilistic assignments result in different outcomes according to predefined probabilities for each input). For causal relationships that cannot be described deterministically because no formulas or other deterministic models exist, a probabilistic assignment is made as illustrated on the right side of Figure 8. Probabilistic assignments can either be based on subjective probabilities estimated by experts who assess the likelihood of each manifestation scenario or rely on existing datasets or simulation models that provide objective probabilities in terms of expected frequency distributions for the manifestations.  
The chosen qualitative discrete description for manifestation scenarios directly influences the probabilities that must be defined during the causal mapping. Detailed descriptions need expert input that is accordingly detailed, leading to dependency between scenario choices and their mapping. A qualitative description of scenarios that is difficult to quantify probabilistically should be avoided. Conversely, trivial distinctions that can be easily quantified probabilistically but do not meaningfully differentiate between cases should not be chosen either. Hence, in order to define relevant discrete manifestation scenarios and map them appropriately, PSS developers must possess a sufficient understanding of the relationships being described. Depending on the developers' ability to formalize specific relationships in this way, varying levels of understanding can be identified.
## Nomenclature  
\begin{tabular}{l l l} \(a\) & acceleration (ms\({}^{-2}\)) \\ \(a_{peak}\) & peak acceleration (ms\({}^{-2}\)) & \(N_{P\text{-}PCH_{j}}\) & \(=\frac{Q_{j}}{\rho_{PC}A_{H}u_{s}}\frac{\beta_{PC}}{C_{PC}}\) \\ \(a_{max}\) & non-dimensional peak magnitude & \\ \(a_{ses}^{+}\) & non-dimensional seismic acceleration, \(=a_{ses}/g\) & \(N_{TPCj}\) & true trans-pseudo-critical number for j-th channel, \\ \(a_{V}^{+}\) & non-dimensional vertical acceleration, \(=a_{V}/g\) & \(=\frac{Q_{j}}{\rho_{ij}A_{H}u_{s}}\frac{\beta_{PC}}{C_{PC}}\) \\ \(A\) & cross sectional area (m\({}^{2}\)) & \\ \(A_{H}\) & cross sectional area of heated channel (m\({}^{2}\)) & \(N_{TPC}\) & Average true trans-pseudo-critical number, \(=\frac{Q_{tot}}{W_{tot}}\frac{\beta_{PC}}{C_{P,PC}}\) \\ \(C_{p}\) & constant pressure specific heat (Jkg\({}^{-1}\)K\({}^{-1}\)) & \\ \(D\) & diameter (m) & \\ \(D_{H}\) & diameter of heated channel (m) & \(N_{P\text{-}SUBj}\) & pseudo-subcooling number for j-th channel, \(=\frac{\beta_{PC}}{C_{P,PC}}\left(h_{PC}-h_{i,j}\right)\) \\ \(f\) & friction factor or frequency (Hz) & \\ \(f^{+}\) & non-dimensional frequency, \(=fL_{H}/u_{s}\) & \\ \(f\) & & \\ \(Fr\) & Froude number, \(=u_{s}^{2}/gL_{H}\) & \\ \(g\) & gravity acceleration (ms\({}^{-2}\)) & \\ \(h\) & enthalpy (Jkg\({}^{-1}\)) & \\ \(h_{PC}\) & fluid enthalpy at pseudo-critical point (Jkg\({}^{-1}\)) & \\ \(h_{i}\) & inlet fluid enthalpy (Jkg\({}^{-1}\)) & \\ \(u\) & & \\ \end{tabular}
## 2 Materials and Methods
### Support Vector Machines  
While SVM have been widely used for classification purposes, the metaheuristic may also be applied to regression based on the same principles. It is consensual in the research community that this approach relies more on a structural risk minimization principle rather than an empirical risk minimization principle [16; 25; 26], quite common, for example, in ANN. Other particularity is that its training derives from quadratic programming, resulting in a global optimal solution. As it addresses a regression problem, SVM attempt to find an optimal hyperplane such that distance to data is minimized. The original input space is usually nonlinearly related to the predictor variable. Therefore, it is mapped onto a higher dimensional feature space through a nonlinear mapping function (kernel function) to deal with these nonlinearities. The kernel transformation allows the handling of nonlinear relationships in the data as a higher dimensional space is obtained, in which a linear regression model can be built.  
An SVM model may be optimized by tuning the kernel function, the kernel parameter (gamma), the cost parameter, and the error margin (epsilon). The Radial Basis Function (RBF), which is commonly used in energy demand problems, is the kernel function best fit to deal with non-linearities. The level of non-linearity of an SVM model is defined, according to the width of the ensuing bell-shaped function (RBF). The cost parameter (C), used to apply a penalty on individual errors, is also important to avoid model overfitting.  
The selection of the three non-negative parameters (epsilon, C, and gamma) is determinant to forecasting accuracy. The use of a combinatorial method is often suggested to optimize the parameter selection procedure [18; 25; 26; 27; 28]. The authors selected simulated annealing (SA) to approach the parameter selection stage.  
Figure 2: Representation of the multivariable artificial neural network adopted.  
A number of combinations were tested in an attempt to find the best-performing combination for the given dataset. The lowest values for error metrics MAE and RMSE were obtained for C = 500, gamma = 0.5 and epsilon = 0.5.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## 3 Improvement of A* Algorithm
### Algorithm Simulation  
The parameter weights \(\omega_{1}\) and \(\omega_{2}\) of the evaluation function are presented and different weights will lead to different results. In order to study the influence of weight coefficients on the path planning, five groups of different weights (\(\omega_{1}\) = 0.9, \(\omega_{2}\) = 0.1; \(\omega_{1}\) = 0.7, \(\omega_{2}\) = 0.3; \(\omega_{1}\) = 0.5, \(\omega_{2}\) = 0.5, \(\omega_{1}\) = 0.3, \(\omega_{2}\) = 0.7, \(\omega_{1}\) = 0.1, \(\omega_{2}\) = 0.9) were chosen to carry out simulations for the improved environment model. The simulation results are shown in Figure 4.  
The specific parameters of planning results shown in Table 1 are where the risky path indicates the path in the threatened regions and the dangerous path indicates the path in the state where the distance from the obstacles is less than half of the safe distance.  
It can be seen from Table 1 that the greater the distance weight coefficient \(\omega_{1}\) is, the shorter the length of the planned path. In addition to increasing the safety cost weight coefficient \(\omega_{2}\), the lengths of both the risky path and dangerous path become smaller; this indicates that the planned path becomes safer as the path length becomes longer.  
The influence of each weight coefficient on the performance of planned path can be determined from a comparison and is consistent with our expectations. In actual use, the weight coefficients should be set as required. Different results, such as the shortest path or the safest path, can be obtained by setting different weights heuristically. In general, all parameters of the planned path should be taken into account to optimize the planning result. Table 1 demonstrates that the performance of the planned path is excellent when \(\omega_{1}\) = 0.5 and \(\omega_{2}\) = 0.5; the planning result is as shown in Figure 4c. From a comparison of Figures 2 and 4c, we can easily see that the path planned by using the improved A* algorithm is farther away from the path planned obtained by using the conventional A* algorithm. This demonstrates that the improved A* algorithm can determine a safer path.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\omega_{1}\):\(\omega_{2}\) & Path Length (m) & Length of Risky Path & Length of Dangerous Path \\ \hline
0.9:0.1 & 66.74 & 22.63 & 12.73 \\
0.7:0.3 & 68.50 & 17.97 & 6.66 \\
0.5:0.5 & 70.25 & 13.31 & 5.24 \\
0.3:0.7 & 80.21 & 0 & 0 \\
0.1:0.9 & 80.21 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Path planning of the improved A* algorithm.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 1 Introduction
Texture parameters were derived from the gray-level co-occurrence matrix (GLCM) in addition to utilizing local binary patterns (LBPs). [PERSON] classified the vectors using various distance calculation methods. The system was tested using five publicly available image databases. [PERSON] [12] utilized HSV color space transformations to detect cauliflower plants. He performed erosion and dilation procedures on the images and then identified the contour, center, and bounding rectangle of the cauliflower. This method achieved a 99% accuracy rate in detecting cauliflower, even in images with minimal weed interference. The experiment was tested under various weather conditions, including sunny, partially cloudy, and completely cloudy scenarios. The subsequent study by [PERSON] [13] implemented the YOLO deep learning system to distinguish between two classes: wheat and weed. This research used a relatively large dataset of 5400 images, with 80% allocated for training and 20% for testing. Both the traditional YOLO and an optimized version were employed, with the traditional YOLO achieving a recognition rate of 93.86% and the optimized YOLO reaching 94.86%. [PERSON]'s research [14] focused on weed detection, aiming to recognize five different plant species. The study employed contour Fourier descriptors and extracted Hu moments and other parameters from the images. The obtained vectors were classified using artificial neural networks (ANNs) and support vector machines (SVMs), with the ANN classifier achieving a recognition rate of 92.67% and the SVM classifier achieving 93.33%. During weed detection, the histogram of oriented gradients (HOG) is also useful, although achieving over 90% accuracy on its own is typically not attainable. One study [15] created a single-layer image using the excess green (ExG) index procedure. HOG values were computed on these images, which were then transformed using a visual word technique and classified. This method succeeded in achieving around 90% accuracy, and in some cases, even approached 95%. In a recent study [16], researchers investigated the problem of detecting partially overlapping weeds and crops. They focused on identifying the boundary between a weed and a crop within the green vegetative area. The images were divided into 64 \(\times\) 64, 32 \(\times\) 32, and 16 \(\times\) 16-pixel cells, and three texture parameters (HOG, LBP, and GLCM) were computed for each cell. These parameters, as well as their combinations, were classified using four different methods. The classified values were then assigned to the cells and propagated between layers, with adjustments made to the classified values as needed. This approach successfully separated weeds from crops.
## 3 Methodology
### Marching Cubes  
The surface reconstruction generates a mathematical model that the marching cubes algorithm can utilize to estimate the soil volume in the excavation area. Each marching cube consists of eight vertices and twelve edges, as shown in Figures 7 and 8 from [14], respectively. Figure 9 illustrates the vertex indexing scheme for a single marching cube, also adapted from [14].  
The marching cubes algorithm employs a three-dimensional discrete scalar field to extract a 3D mesh from an iso-surface. In this particular case, the iso-surface is constructed from the surface reconstruction detailed in Section 3.4. Each vertex includes a Boolean value that indicates whether it lies inside the soil or not. The \(z\) coordinate of the reconstructed surface is calculated at the same \((x,y)\) coordinates of the vertex. If the \(z\) value of the vertex is lower than that of the reconstructed surface, it signifies that the vertex lies below the soil surface.  
As a marching cube consists of eight vertices, each with two possible states, there are a total of \(2^{8}=256\) different cases. However, only 15 cases are unique, while the remaining  
Figure 8: Edges of a marching cube [14].  
Figure 7: Vertices of a marching cube [14].  
Figure 9: Marching cube [14].  
cases are simply a combination of inverted, rotated, or flipped variations of the unique instances. The 15 unique variations of a 3D marching cube are depicted in Figure 10 [14].  
By generating a 3D array of marching cubes in the vicinity of the excavation area as shown in Figure 11, it becomes possible to convert the soil surface into a soil volume. The volume of each marching cube can be pre-calculated, as there is only a finite number of cases. The volume for each case is defined in Table 1. It is worth noting that in case of inverted variation, the weight would be complement of the given values in Table 1. Summing up the volumes of all the marching cubes enables estimation of the total soil volume within the excavation area. By multiplying the volume by the density of the soil, it is possible to estimate the total weight of the soil as well.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case No.** & **Volume** & **Case No.** & **Volume** & **Case No.** & **Volume** \\ \hline Case 0 & 0.0000 & Case 5 & 0.3542 & Case 10 & 0.2500 \\ \hline Case 1 & 0.0208 & Case 6 & 0.1458 & Case 11 & 0.5000 \\ \hline Case 2 & 0.1250 & Case 7 & 0.0625 & Case 12 & 0.3750 \\ \hline Case 3 & 0.0417 & Case 8 & 0.5000 & Case 13 & 0.0833 \\ \hline Case 4 & 0.0417 & Case 9 & 0.5000 & Case 14 & 0.5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Volume of the unique cases of marching cubes.  
Figure 11: Example of marching cube application.  
Figure 10: Fifteen unique cases for a 3D marching cube [14].  
The results obtained from this method are reliable, deterministic, and computationally efficient, ensuring accurate reconstruction and volume estimation for excavation planning.
## 2 Background and Review of Related Works
### Review of Related Works  
#### 2.5.1 Review of Survey on XAI Related Works  
The concept of XAI has penetrated several fields such as healthcare [76, 77, 78], regression modeling [79], anomaly detection [80], power system emergency control [81], fault detection [82], landslide prediction [83], measuring uranium enrichment [84], online game prediction [85], civil engineering [86], and no-teardown vehicle component cost estimation [87]. Thus, the application of XAI to the ITS is attracting a lot of attention from researchers.  
Although some review papers on XAI have been written, to the best of our knowledge, this is the first attempt to conduct an extensive review of XAI's applicability to ITS with a focus on intrusion mitigation. The authors in [88] presented a review on XAI without delimiting any field. They focused on the general concepts, applications, emerging issues, and approaches of XAI designs. On the other hand, the authors in [17] reviewed XAI with a focus on the major requirements and agenda setting for the future implementation of XAI. Specifically, they explained the provenance information concept and its applicability to XAI. [PERSON] et al. [89], as a follow-up to their previous work [90], posited that algebraic aggregation [90] combined with semantic analyses solving for infeasible path reduction, has an impact on both explainability and velocity. Specifically, the work indicated their impact concerning running time [89].  
Similarly, the authors in [77] reviewed the applicability of XAI to the healthcare sector while focusing on data analysis and interpretation of models. The mention of XAI's applicability to the transportation sector was only done in passing. Focusing on the emergence of XAI for Industry 4.0, the authors in [91] provided a comprehensive survey of XAI-based approaches adopted to meet their demands for automatic and real-time implementation. In [79], the authors present a review aimed at establishing the unique features of XAI for regression models known as XAIR. The review provided a brief overview of the general concept of XAI before narrowing it down to XAIR, emphasizing the important specificity of the regression problem that necessitates XAI adaptation. Moreover, authors in [92] reviewed XAI from the perspective of data and knowledge engineering, enumerating the existing works of XAI for data-driven and knowledge-aware scenarios.  
The closest review of XAI for security is presented in [93]. The authors of this study provided an overview of XAI concepts, trends, enabling technologies, and applications for system security. However, the reviews confirm the hypothesis that review works available on XAI and the security of ITS is limited. Due to the scarcity of research on XAI for ITS security, this study provides an exhaustive contemporary study with a systematic and comprehensive review of the extensive research on XAI-IDS for ITS. This study differs from previous XAI reviews in that it is the first to systematically survey research works and directions in the use of XAI for the security of ITS limited to IDS.
## 2 Materials and Methods
### Application as Photocatalysts for Congo Red Dye Removal  
The photocatalysis experiments were carried out in a PhotoLAB B400-700 Basic Batch-L-type photocatalytic reactor, purchased from Peschl-Ultraviolet GmbH, Mainz, Germany, equipped with a TQ150-type submerged lamp (150 W Hg medium pressure). The temperature of the system did not exceed 20 \({}^{\circ}\)C, due to the circulation of tap water in the outer mantle of the reactor. An amount of 700 cm\({}^{3}\) of CR solution (concentrations: 25, 50 and 100 mg/L) was magnetically stirred with a certain mass of photocatalyst, so that the catalyst amount was 0.5, 1 and 2 g/L. Preliminary dark adsorption experiments were carried out, in order to study the equilibrium time achieved between the surface of the photocatalyst and the dye. After this time, the lamp was turned on. At well-established time intervals, 5 mL aliquots were taken, filtered through a Millipore syringe filter (45 \(\upmu\)m pore size) and spectrophotometrically analysed. The spectra were recorded between 200 and 800 nm and the maximum absorption values at \(\uplambda\) = 498 nm were monitored. The Lambert-Beer's law was utilized to establish the relationship between absorption and CR concentration. The efficiency of the removal of CR was calculated by using the following equation:  
\[\mathrm{Efficiency}(\%)=\frac{\mathrm{c}_{0}\ -\ \mathrm{c}}{\mathrm{c}_{0}} \cdot 100, \tag{1}\]  
where \(\mathrm{c}_{0}\) is the initial concentration of CR (mg/L) and \(\mathrm{c}\) is the concentration of CR at time t (mg/L).
## 4 FWA Design Considerations, Challenges, and Solutions
### High Radio Power Radiation Hazards  
The associated high propagation loses demand for the CPE to radiate at high power in order to offer good QoS and desired QoE. Although the possible safety hazards of RF energy exposure to humans and the recommended values for Maximum Permissible Exposure are debatable, further research effort is required concerning harmless exposure limits to electromagnetic fields by the consumer. Besides, there is a great need for novel technologies, to ensure enough safety from the RF energy exposure [9].
## 4 Experimental Investigation
### TES Pilot Plant Set Up  
The high-temperature TES test facility HOTREG at the German Aerospace Center in Stuttgart was prepared to investigate slag as a TES inventory (Figure 4). The test facility is based on the regenerator principle with a solid inventory and air or flue gas as a heat transfer fluid. The main structural components are:  
* Air compressor;
* Compressed air heater;
* Regenerative storage tank;
* Cooling circuit;
* Central control unit.  
The air compressor is a speed-controlled screw compressor with a rated motor power of 90 kW. It provides continuous air flows between 220 kg h\({}^{-1}\) and 720 kg h\({}^{-1}\) at pressures between 1.1 bar and 11 bar at the compressed air heater inlet. The compressed air heater is an indirectly heated, double-flue heater in cylindrical, horizontal design with helical-shaped single line plain tube heat exchanger surfaces, separated for \"charging\"- and \"discharging\"-mode. The central part is the regenerative storage tank, which is shown in Figure 4a. It consists of a pressure vessel with inner insulation and water cooling at the outside (double shell). For experiments, the inner storage tank can be changed to test different designs of TES. The main technical details of the pilot plant are listed in Figure 4[24].  
For the test campaign, the inner storage tank is equipped with an inner insulation and filled with 4.85 tons of slag pellets, see Figure 4b. A total of 35 \"Type K\" thermocouples (TC) are used to measure the temperature in the storage tank. Seven levels are distributed over the storage height, as shown in Figure 5. Levels 1 and 7 are close to the inventory, and Levels 2 to 6 are located within the slag packed bed. For inventory temperature recording, each level is equipped with five TCs, with the exception of Levels 3 and 5, where four additional thermocouples are placed in the insulation. The TCs of each level  
Figure 4: High-temperature thermal energy storage (TES) test facility HOTREG (**a**); test setup for slag-based TES (**b**) and basic specifications of HOTREG (**c**) [27].  
are arranged in a circle with a diameter of 1000 mm. For the evaluation, the mean level temperature values are used. The TCs have a specified accuracy of 1.5 K [24].  
The TES test facility is used for different cyclic experiments, which differ while charging and discharging in duration as well as in mass flow. These parameters are determined by adapting the full-scale design to the pilot scale [24]. All test parameters are listed in Table 2.