esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### DAP Measurement with Single-Spot Method  
The single-spot method is a direct determination of DAP. A large-area ionization chamber (Bragg peak chamber type TM34070, PTW-Freiburg) was positioned at depth 2 cm in the water tank. The BPC is cross-calibrated against an MC. The experiment setup is shown in Figure 2.  
DAP measured with BPC in a beamlet, \(DAP_{w}^{A(BPC)}\) is obtained as:  
\[DAP_{w}^{A(BPC)}=M_{BPC}\times N_{D,w,BPC}\times A_{BPC}\times CSCF \tag{2}\]  
where \(M_{BPC}\) is the charge measured with the BPC. \(N_{D,w,BPC}\) is the cross-calibrated coefficient in a proton beamlet for BPC. \(A_{BPC}\) is the area of the BPC. \(CSCF\) is the chamber size correction factor (CSCF).  
Figure 1: Illustration of the setup for the DAP measurement in water with the single-layer method.  
Figure 2: Illustration of the setup for the DAP measurement in water with the single-spot method.  
The calibration coefficient for the BPC is cross-calibrated with the MC in a large scanned field. The calibration coefficient is given by:  
\[N_{D,w,BPC}=\frac{M_{Markus}\times N_{D,w,Markus}\times k_{Q,Q_{0}}}{M_{BPC}^{FIELD} \times CRH_{BPC}}\times\iint\limits_{A}OAR(x,y)dxdy \tag{3}\]  
where \(M_{BPC}^{FIELD}\) is the charge measured with BPC in the 10 \(\times\) 10 cm\({}^{2}\) large field. \(CRH_{BPC}\) is the chamber response heterogeneity (CRH) which is attributable to the non-uniform lateral response of BPC [4; 6]. _OAR(x,y)_ is the off-axis ratio at lateral position x and y [6]. If the lateral dose distribution is not uniform, the additional factor (integration of the off-axis ratio) is added. In order to check the dose uniformity over the chamber area, a log file-based dose reconstruction was employed. The details for the reconstruction are described in Section 2.5. The 10 \(\times\) 10 cm\({}^{2}\) field may not be large enough for the BPC. A correction factor is added with the Monte Carlo simulation. Two square fields were simulated, 10 \(\times\) 10 cm\({}^{2}\) and 20 \(\times\) 20 cm\({}^{2}\) and dose was scored for the sensitive volume of BPC. The correction factor is the ratio of the scored dose with the two large fields.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles  
[PERSON]  
1 AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, 30-059 Krakow, Poland; [EMAIL_ADDRESS]  
This review will also examine studies investigating the impact of AVs on overall traffic flow, speed, and congestion. Research has shown that even a small percentage of AVs on the road can significantly improve traffic efficiency. Furthermore, the evaluation of different driving algorithms and their influence on traffic stability and throughput will be analyzed.  
Interactions between autonomous and human-driven vehicles, including conflict points, cooperative behaviors, and the potential for new forms of traffic congestion, will be explored in the context of mixed traffic environments. Mixed traffic scenarios present unique challenges, such as the need for AVs to anticipate and react to the unpredictable behaviors of human drivers.  
The successful integration of AVs into existing transportation systems requires supportive infrastructure and policies. This review will consider issues related to the requirements for road infrastructures, traffic signals, and regulatory measures needed to facilitate the integration of AVs.  
Simulation is a critical tool in AV traffic modeling, enabling the testing of hypotheses, validation of models, and execution of large-scale traffic experiments in virtual environments. This review will provide an overview of various simulation tools and platforms used in AV traffic modeling, emphasizing their role in advancing our understanding of autonomous vehicle dynamics.  
Despite significant progress in AV traffic modeling, several challenges persist. These include the need for more accurate and scalable models, the integration of real-world data, and the consideration of human factors in mixed traffic environments. Additionally, ethical and legal issues related to AV deployment, such as liability and privacy concerns, remain to be addressed.  
Looking ahead, this review will outline potential research directions, including the development of more sophisticated models that incorporate machine learning and artificial intelligence, the use of edge computing and 5G networks for real-time data processing, and the exploration of new traffic management strategies that leverage the capabilities of AVs. By advancing our understanding of autonomous vehicle traffic dynamics, we can pave the way for a safer, more efficient, and sustainable transportation system.
## 3 Results
### Principal Component Analysis  
The non-targeted QTOF-MS analysis generates a tremendous amount of data and requires pre-treatment prior to the application of statistical tools. After data pre-processing, 8867 features (\(m/z\_\)RT pairs) were detected by the SPE method and 12,694 features were detected by the SPME method. The principal task of the present study was the discrimination of volatile compounds between two blueberry wines, and the detection of corresponding markers. For the initial overview of the dataset, PCA was carried out. The score plots showed that both SPE and SPME methods could well distinguish the two blueberry wines, and two blueberry wines were mainly separated on principal component 1 (PC1). Score plot of SPE (Figure 2a) explained 42.9% of the total variance. Score plot of SPME (Figure 2c) explained 41.1% of the total variance. However, due to the extremely large number of data included, it was very hard to extract effective information from the loading plot of PCA (Figure 2b,d), so further statistical analysis was required. Nevertheless, the PCA results certified that the scaling method used was appropriate and there was a real separation between the two groups, as the separation was seen despite no class data being included in the algorithm.
## Appendix A Simulation Data of the AUV
\]  
To obtain the above hydrodynamic coefficients, the strip theory is utilized for numerical calculation, where the fluid density is assumed to be 1030 kg/m\({}^{3}\), the linear-skin coefficient is assumed to be 0.4 and the drag coefficient is assumed to be 1. Moreover, some of the obtained coefficients are adjusted based on comparisons with data of the REMUS AUV according to dynamic similarity [15]. Then, the adjusted hydrodynamic coefficients are shown in Table A1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Added Mass Coefficients**} \\ \hline
**Force** & **Value** & **Units** & **Moment** & **Value** & **Units** \\ \hline \(X_{\rm{u}|\rm{u}|}\) & \(-2.33\) & Kg & \(K_{\rm{p}}\) & \(-0.3\) & Kg\(\cdot\)m\({}^{2}\)/rad \\ \(Y_{\rm{v}}\) & \(-90.8\) & Kg & \(M_{\rm{q}}\) & \(-20.3\) & Kg\(\cdot\)m\({}^{2}\)/rad \\ \(Y_{\rm{r}}\) & \(4.53\) & Kg\(\cdot\)m & \(M_{\rm{w}}\) & \(-4.53\) & Kg\(\cdot\)m \\ \(Z_{\rm{w}}\) & \(-90.8\) & Kg & \(N_{\rm{r}}\) & \(-20.3\) & Kg\(\cdot\)m\({}^{2}\)/rad \\ \(Z_{\rm{q}}\) & \(-4.53\) & Kg\(\cdot\)m & \(N_{\rm{v}}\) & \(4.53\) & Kg\(\cdot\)m \\ \hline \multicolumn{6}{c}{**Drag Coefficients**} \\ \hline
**Force** & **Value** & **Units** & **Moment** & **Value** & **Units** \\ \hline \(X_{\rm{u}|\rm{u}|}\) & \(-2.96\) & Kg/m & \(K_{\rm{p}|\rm{p}}\) & -0.558 & Kg\(\cdot\)m\({}^{2}\)/rad \\ \(Y_{\rm{v}|\rm{v}}\) & \(-2346\) & Kg/m & \(M_{\rm{q}|\rm{q}}\) & \(-807\) & Kg\(\cdot\)m\({}^{2}\)/rad \\ \(Y_{\rm{r}|\rm{r}}\) & \(0.759\) & Kg\(\cdot\)m/rad\({}^{2}\) & \(M_{\rm{w}|\rm{w}}\) & \(8.76\) & Kg \\ \(Z_{\rm{w}|\rm{w}}\) & \(-242\) & Kg/m & \(N_{\rm{r}|\rm{r}}\) & \(-404\) & Kg\(\cdot\)m\({}^{2}\)/rad \\ \(Z_{\rm{q}|\rm{q}}\) & \(-0.759\) & Kg\(\cdot\)m/rad\({}^{2}\) & \(N_{\rm{v}|\rm{v}}\) & \(-8.76\) & Kg \\ \hline \multicolumn{6}{c}{**Lift Coefficients**} \\ \hline
**Force** & **Value** & **Units** & **Moment** & **Value** & **Units** \\ \hline \(Y_{\rm{uv}}\) & \(-56.5\) & Kg/m & \(M_{\rm{uq}}\) & \(-8.9\) & Kg\(\cdot\)m/rad \\ \(Y_{\rm{ur}}\) & \(11.8\) & Kg/rad & \(M_{\rm{uw}}\) & \(-24.9\) & Kg \\ \(Z_{\rm{uw}}\) & \(-56.5\) & Kg/m & \(N_{\rm{ur}}\) & \(-8.9\) & Kg\(\cdot\)m/rad \\ \(Z_{\rm{uq}}\) & \(-11.8\) & Kg/rad & \(N_{\rm{uv}}\) & \(24.9\) & Kg \\ \hline \end{tabular}
\end{table} 
Table A1: The list of AUV coefficients. For the AUV, the thruster configuration matrix and its pseudo inverse are given as  
\[\mathbf{B}_{v}= \begin{bmatrix}1&1&0&0&0\\ 0&0&0&0&1\\ 0&0&1&1&0\\ 0&0&0&0&0\\ 0&0&r_{3}&-r_{4}&0\\ r_{1}&-r_{2}&0&0&r_{5}\end{bmatrix} ,\mathbf{B}_{v}^{+}= \begin{bmatrix}\frac{r_{2}}{r_{1 }+r_{2}}&-\frac{r_{5}}{r_{1}+r_{2}}&0&0&0&\frac{1}{r_{1}+r_{2}}\\ \frac{r_{1}}{r_{1}+r_{2}}&\frac{r_{5}}{r_{1}+r_{2}}&0&0&0&\frac{1}{r_{1}+r_{2}} \\ 0&0&\frac{r_{4}}{r_{3}+r_{4}}&0&\frac{1}{r_{3}+r_{4}}&0\\ 0&0&\frac{r_{3}}{r_{3}+r_{4}}&0&-\frac{1}{r_{3}+r_{4}}&0\\ 0&1&0&0&0&0\end{bmatrix} \]  
where \(r_{1}=0.18\) m, \(r_{2}=0.18\) m, \(r_{3}=0.525\) m, \(r_{4}=0.245\) m, \(r_{5}=0.485\) m.
## 3 Methods
### Terrain Recognition and Stability Analysis
#### 3.3.2 Stability Analysis
When the robot is located on a slope with a slope angle \(a\), the contact point between the front wheel and the ground is \(O_{1}\), the contact point between the rear wheel and the ground is \(O_{2}\), and the height of the center of gravity \(O\) from the slope is \(h\), and its projection on the ground moves from point \(P_{1}\) on the level ground to point \(P_{2}\) on the sloped ground, the distances between point \(P_{2}\) and points \(O_{1}\) and \(O_{2}\) are \(L_{2}\) and \(L_{1}\), respectively. At this time, \(L_{2}<L_{1}\), so the pressure of the robot on the ground at point \(O_{2}\) is greater than that at point \(O_{1}\). The condition for a wheel-footed robot to travel on a slope without tipping longitudinally is that the vertical pressure on the ground at the contact point between the wheels with a higher horizontal position on the slope and the ground is greater than zero. Since the slope will affect the position of the robot's center of gravity at point \(P_{2}\) on the slope, when the slope is large enough, point \(P_{2}\) coincides with point \(O_{2}\). At this time, \(L_{1}=0\), \(L_{2}=L\), the pressure of the robot's front wheels on the ground is 0, and the pressure of the rear wheels on the ground is \(G\). Since the friction force provided by the ground for the wheels under extreme slope conditions is only used to offset the component of the robot's gravity parallel to the slope, it will cause the robot to tip over on the slope. At this time, the distance from point \(P_{1}\) to point \(O_{2}\) is 0.5 \(L\), and \(h\) is the distance from the center of gravity \(O\) of the robot to the slope surface, as shown in Figure 7(b). The expression of the limit tilt angle according to the geometric relationship is as follows:  
\[\beta_{o-\text{lim}}=\arctan\frac{0.5L_{3}}{h} \tag{19}\]  
Combined with the wheel-footed robot body structure, the height of the center of gravity \(h\) is 0.350 m, the distance \(L\) between the two wheel axes is 0.532 m, \(L_{3}\) is 0.710 m, the limit tilt angle for the lateral direction is \(\beta_{o-\text{lim}}=45.41^{\circ}\). Figure 8: Stability analysis of wheel-legged self-balancing robot on different terrains.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Feature Selection and Preprocessing
#### 2.2.3 Addressing Multicollinearity in the Dataset  
For the purpose of addressing the multicollinearity in the dataset, namely identifying redundant independent features that bring similar information about the yield (target variable in this study), highly correlated variables were removed based on defined criteria.  
In particular, a Spearman correlation matrix was calculated so as to assess the relationships between the variables [58]. Any pair of features with a Spearman correlation coefficient greater than 0.85 was considered highly correlated. Subsequently, only one feature from each was retained. The selection process was driven by the following criteria: (a) In cases where a spectral band was highly correlated with a VI, the later was retained, as information for the spectral bands has already been integrated in VI formulation; (b) When a strong correlation was observed between two spectral bands or between two VIs, the feature with the higher absolute correlation with the target variable was retained, because it presents more predictive relevance. However, in certain instances, two highly correlated features could be retained, as they provide complementary agronomic information critical for accurate yield modeling. Although correlated, such features capture distinct physiological and structural characteristics of the crop canopy that influence yield variability. For example, both \(EVI\) and \(NDVI\) were preserved despite their correlation, as \(EVI\) is more sensitive to canopy structure and reduces soil background noise [59], while \(NDVI\) effectively captures overall vegetation vigor [60]. Retaining both indices enriches the model's ability to reflect complex crop biophysical conditions, thereby enhancing predictive robustness and biological interpretability. Overall, this process ensures that the model requires less computation to converge, prevents overfitting or biased predictions, and makes the model more interpretable.  
Figure 2 presents the correlation heatmap summarizing the pairwise Spearman correlation coefficients. Each cell shows the correlation between two features with the color intensity corresponding to the magnitude of the correlation. Based on the aforementioned criteria, 13 input features were considered for the present analysis: 2 spectral bands (\(B03,\ B12\)) and 11 VIs (\(EVI,\ GRVI,\ MSAVI,\ NDVI,\ NDWI,\ OSAVI,\ SAVI,\ SBI,\ SR,\ WI,\ WDRVI\)).
## 2 Theoretical Background
### Computational Intelligence Methods
#### 2.2.1 Genetic Algorithms  
Genetic algorithms (GAs) are a category of algorithms that search to find the optimal solution, i.e., a solution that maximizes or minimizes a particular function, to given computational problems. In order to do so, they imitate the operation of evolution. A population consisting of possible solutions is built, and new solutions are generated by breeding the best solutions found within all the members of the population in order to create a new generation. The population develops for many generations so that when the execution of the algorithm is completed the best solution is found [23].  
Specifically, in GAs every individual represents a possible solution to an optimization problem. The characteristics of each individual are described by the chromosomes (also known as genomes) and constitute the variables of this problem. Before executing a GA, the chromosomes should be suitably encoded, in order to reflect the given problem. Additionally, a proper fitness function should be defined, in order to describe the problem as well as attribute a certain degree of fitness to each encoded solution. During the execution of the genetic algorithm, the best parents for crossover and reproduction of descendants are selected. It is of outmost importance that these kinds of algorithms are constructed in such a way so that the diversity among the individuals of the population is ensured. In this way, the premature convergence of the algorithm can be prevented. One of the operators which strengthens the diversity of the genetic algorithm--and, thus, prevents its premature convergence--is the mutation operator; through its application, the random appearance of a chromosome could be noticed, which otherwise would be impossible to be detected through the crossover. In order to balance the size of the population in the new generation, some of the parents in the produced population are relocated, so its total size remains intact [24].
## 3 Methodology
### Incorporating Defuzzification Methods  
Section 1 shows that only a specific number of functions are used in the design of fuzzy systems. Unlike the fuzzification functions, there is a set of defuzzification functions, each developed to improve the conversion according to the fuzzy system's application. This stage describes the defuzzification (output) functions to evaluate a fuzzy system's input and output functions.  
[PERSON] and [PERSON] [29] introduced thirty-one theoretical defuzzification functions derived from fuzzy set theory. These functions are categorized into four primary classes: those associated with the mean (22), those associated with the minimum (2), those associated with the maximum (2), and finally, other methods (5). However, in order to streamline the evaluation process, five functions were selected due to their relevance in industrial applications [4]:  
* Centroid or center of gravity. This is associated with the mean, also named the center of area, [PERSON] developed this technique as the most popular for defuzzification tasks.
* Bisector. This is a vertical line that divides the region into two similar sub-regions, sometimes coincident with the centroid.
* MOM. Associated with the maximum, only active rules with the highest degree of fulfillment are considered.
* SOM. This is associated with the maximum. It chooses the smallest output with the maximum MF as the crisp value.
* LOM. Associated with the maximum, this takes the largest among all \(z\) that belong to \([z_{1},z_{2}]\) as the crisp value called Z (LOM).
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 4 Results and Discussion
### FFT Accuracy  
As the algorithm for the fault detection is based on the exceeding of given thresholds in a set of frequency ranges, the accuracy of the acquisition channel and of the spectrum evaluation plays a key role in the capability of the system to detect faults when they occur, avoiding false positives and false negatives. In order to characterized the acquisition channel and the DEFT algorithm in terms of frequency offset and amplitude error, a sinusoidal signal was applied to the ADC input, it was sampled, and the frequency spectrum evaluated by the microcontroller. The sinusoidal input signal is characterized by 1 kHz fundamental frequency (\(f_{0,nom}\)), 600 mV average value, and 700 mV peak amplitude (\(A_{0,nom}\)). In such a way, the input range of the ADC is almost entirely covered, and the quantization error is minimized. The nominal parameters of such a signal were measured using a 6-digit multimeter and a counter, and compared against the ones extracted from the frequency spectrum evaluated by the microcontroller, i.e., \(f_{0,\mu C}\) and \(A_{0,\mu C}\). The relative errors affecting the fundamental frequency (\(e_{r}^{f_{0}}\)) and the amplitude of the fundamental tone (\(e_{r}^{A_{0}}\)) were evaluated as  
\[e_{r}^{f_{0}}=100\cdot\frac{f_{0,\mu C}-f_{0,nom}}{f_{0,nom}},\quad e_{r}^{A_{ 0}}=100\cdot\frac{A_{0,\mu C}-A_{0,nom}}{A_{0,nom}}. \tag{10}\]  
More precisely, different number of samples (\(N_{s}\)), sampling frequencies (\(f_{s}\)), and different windowing functions were considered, resulting in the graphs shown in Figure 5 (no windowing), Figure 6 (Hann-2 window), and Figure 7 (top flat).  
As far as \(e_{r}^{f_{0}}\) is concerned, higher is \(N_{s}\), lower is \(e_{r}^{f_{0}}\) independently from the sampling frequency, whereas, in case of low \(N_{s}\), the highest \(f_{s}=50\) kHz (star markers) corresponds to the highest error. Indeed, the \(e_{r}^{A_{0}}\) is strongly affected by the windowing function, as in the worst case (no windowing) such error may reach \(-35\%\), while for the top-flat window, it is within \(\pm 0.6\%\). Such a result validates the exploitation of the windowing technique, since it is crucial for a proper estimation of the amplitude of the frequency spectrum.
## 3 Joint Simulation Process and Modelling of Vibration and Circuit
### Finite Element Simulation of Vibration Signal  
ABAQUS software was selected for the bridge vibration analysis. The main process was as follows: Firstly, the bridge model was simplified as a rectangular flat plate, and the vehicle movement was simplified as a moving load on the beam plane. The length, width and height of the model were 600 mm, 100 mm and 2 mm, respectively. The total number of the elements was 6902. The element type was the 8-node linear brick (C3D8R). The material properties of the model are shown in Table 1. Secondly, three-dimensional deformable solid components were selected for the fixed beam, and the bottoms of both ends were fixed as shown in Figure 5a. The constraint length on each side was 30 mm. Finally, the DLOAD subroutine was used to load the bridge. This subprogram is suitable for dynamic implicit calculations. On the upper surface of the model, a uniformly distributed moving load was applied. As shown in Figure 5b, the loading area was x \(\times\) z = 2 mm \(\times\) 10 mm and the load moved uniformly along the central axis. The load value was 0.0147 MPa and the moving speed of the load was 0.5 m/s.  
Figure 4: Combined numerical simulation and circuit simulation flow.  
Figure 5: Fixed-beam model. (**a**) The boundary conditions. (**b**) The load conditions.
## 2 Materials and Methods
### RTMN 2.0--Extension of RTMN
#### 2.2.1 The RTMN Elements  
RTMN originally contained eleven basic notations differentiated by shapes, colors, and icons [2]. These notations were developed based on the well-known business process modeling notation (BPMN) [53]. The goal was to develop a modeling language that covers the robotics domain with familiar notations, rather than completely new ones. These notations adopt colors, icons, and shapes in their design, aiming to make the modeling more intuitive. Four BPMN standard notations, \"sequence flow\", \"event\" (start, end), and \"gate way\" (exclusive), are reused in RTMN (see BPMN specification [53]).  
There are five new robotics-specific notations: robot task, robot skill, robot primitive, HRC (human-robot collaboration) task, and one slightly changed notation--human task. It is similar to a user task in BPMN; however, here, it has a human icon and a color assigned to it. A \"task\" in BPMN is the lowest level of the process and cannot be broken down into finer details. It is considered an atomic activity within a process [3]. However, the term \"task\" in the robotics area is, rather, a subprocess in relation to BPMN. With different usages--a picking task, for example--it is not an atomic activity but consists of detailed steps such as to move to the right place, locate the gripper and grasp, etc. Therefore, we differentiate a robot task from a typical BPMN task. We define it as an activity consisting of a set of activities that corresponds to a special type of BPMN sub-process that deals with robot activities. The activities within the robot task are defined as skills and primitives. Primitives are atomic activities that compose robotic skills, and skills form robot tasks.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 4 Data Fusion by Platforms
### Aerial Platforms
The random field method is the fusion method in between spatial contextual data for crop classification. The Markov random field (MRF) method was initially tested for image restoration [175] and is now widely used in solving crop classification problems [176]. TheMarkovian support vector classifier (MSVC) is a new fusion technique that combines the support vector machine (SVM) and MRF methods and delivers high spatial contextual classification [177]. The SVM is a widely adopted method for the fusion of spatial information [178], which promisingly addresses the problem of noise during crop classification.