esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Target Decomposition for DCP Data
#### 2.2.1 \(H/\tilde{\alpha}\) Decomposition  
We use the unexpanded 2 \(\times\) 2 coherency matrix to implement \(H/\tilde{\alpha}\) decomposition. With this approach, two eigenvectors corresponding to the first and second dominant eigenvalues can be extracted. The two-dimensional (2D) Pauli vector for circular polarization and the LHCP transmit case is expressed as [10]:  
\[\mathbf{k_{DCP}}=\left[\begin{array}{c}S_{LL}\\ S_{RL}\end{array}\right]. \tag{3}\]  
The coherency matrix is obtained via the Pauli vector in (3) as:  
\[\begin{split}\langle[\mathbf{T_{DCP}}]\rangle&=\langle \mathbf{k_{DCP}}\cdot\mathbf{k_{DCP}^{\dagger}}\rangle\\ &=[\begin{array}{cc}u_{1}&u_{2}\end{array}]\left[\begin{array}{cc} \lambda_{1}&0\\ 0&\lambda_{2}\end{array}\right][\begin{array}{cc}u_{1}&u_{2}\end{array}]^{ \dagger}.\end{split} \tag{4}\]  
Here, \(\lambda_{i}\) are the eigenvalues and \(u_{i}\) are the orthogonal eigenvectors of the unitary matrix  
\[u_{i}=e^{i\phi_{i}}[\begin{array}{cc}\cos\alpha_{i}e^{i\delta_{i}}&\sin \alpha_{i}\end{array}]^{\mathsf{T}}, \tag{5}\]  
where superscript T denotes the transpose operation. The \(H\) and mean \(\alpha\) (\(\overline{\alpha}\)) are then given by:  
\[H=\sum_{i=1}^{2}P_{i}(-\log_{2}P_{i}), \tag{6}\]  
\[\overline{\alpha}=\sum_{i=1}^{2}P_{i}\alpha_{i}, \tag{7}\]  
where scattering probabilities are:  
\[P_{i}=\frac{\lambda_{i}}{\lambda_{1}+\lambda_{2}}(i=1,2). \tag{8}\]  
The \(\tilde{\alpha}\) values for the surface, dipole, and double-bounce scattering mechanisms can be calculated from (4), (5), and (7). The corresponding values are shown below together with the FCP values for comparison.  
\[\text{surface (DCP)}:90^{\circ},\ \ \text{dipole (DCP)}:45^{\circ},\ \ \text{double (DCP)}:0^{\circ},\] \[\text{surface (FCP)}:0^{\circ},\ \ \text{dipole (FCP)}:45^{\circ},\ \ \text{ double (FCP)}:90^{\circ}.\]\(\alpha\) values of the DCP mode are shown to have symmetric values to those of the FCP mode about the \(\alpha=45^{\circ}\). Therefore, DCP \(\alpha\) values are obtained as \(\alpha^{\prime}=90^{\circ}-\alpha\) for comparison with the FCP mode in the results section.  
The obtained pairs of \(H\) and \(\bar{\alpha}\) values are then plotted on the \(H/\bar{\alpha}\) 2D plane with some boundaries to clarify the scattering mechanism and feasible region [17]. We perform the analysis on the \(H/\bar{\alpha}\) plane for displaying the polarimetric signatures of each rice growth stage. Besides, the FCP and DCP modes have different \(H/\bar{\alpha}\) plane plots because the boundaries of each zone differs [11]. Here, our main purpose is to compare the performances of the two modes, thus, we display the \(H/\bar{\alpha}\) plane with \(\alpha^{\prime}=90^{\circ}-\alpha\) condition for the DCP case. Figure 2 shows the \(H/\bar{\alpha}\) planes with different boundaries for each case.
## 2 Single-AGV Path Planning Algorithm
### Improved A* Algorithm
#### 2.2.2 Adding the A* Algorithm of the Turn Penalty Function  
Since the traditional A* algorithm solely considers distance as the selection criterion for determining the optimal path, it fails to account for the impact of turning, which can significantly reduce the AGV's running speed [26]. Consequently, this leads to low actual transport efficiency, increased transport time, and a higher likelihood of failure [27]. To address this issue, an improvement can be made through the incorporation of a turning penalty factor into the cost function formula. This modification ensures that each turn increases the objective function value accordingly. The enhanced cost function formula is presented as follows:  
\[\text{f}^{*}(\text{n})=\text{g}(\text{n})+\text{h}(\text{n})+\sum\text{ma} \tag{3}\]  
\[\text{m}=|\,\text{X}_{\text{n+1}}-\text{X}_{\text{n-1}}|\times|\,\text{Y}_{ \text{n+1}}-\text{Y}_{\text{n-1}}\,| \tag{4}\]  
\[\text{m}=\{0,1\} \tag{5}\]  
Here, m is the turning judgment function, a is the turning penalty factor, and when the AGV turns, it will increase the size of f*(n) to a certain extent. Equation (4) is the formula for determining the value of m, and n is the current grid cell where the AGV is located, with coordinates (Xn,Yn). Then, n - 1 is the previous grid cell on the AGV route, with coordinates (Xn-1,Yn-1). Cell n + 1 is the next grid cell on the AGV route, with coordinates (Xn+1,Yn+1) when the AGV turns at grid cell n, m = 1; otherwise, m = 0.  
Figure 5: A* algorithm diagram.  
Through the use of the improved cost function and incorporating it into the algorithm flow, a single AGV transport path with fewer turns can be obtained.
# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
###### Abstract
The Google Earth Engine (GEE) not only offers the parallelized processing of remote sensing data on a global scale, using the Google Cloud, but also provides several machine learning algorithms for improving the classification techniques of the imageries [20; 24]. Another advantage using GEE for image processing, GEE was first created and optimized for satellite imagery processing that can process petabytes of imagery data, both on large geographical scales and in long temporal coverages, hence it is a great tool for analyzing regional, national, continental, and global-scale applications [20; 25]. A machine learning algorithm, as a classifier feature in GEE, offers a huge application for mapping any earth surface, including forest environments [20]. The most applicable machine learning algorithm for mapping the forest environment, i.e., random forest, classification and regression trees (CART), and the support vector machine have been widely used [26; 27; 28; 29; 30]. Random forest formulates to construct assemblage-based decision trees which are independently sampled during the training process and improve the classification results, rather than a single decision tree model [27; 31]. Finding the best algorithm for GEE to detect burnt areas in Indonesia has never been achieved before so that in this study, we develop a rapid assessment of the burnt area detection method using GEE, and evaluate the performance of the available machine learning algorithm in GEE, for detecting the post-fire burnt area in Indonesia, using radar sensors. This research aims to quickly map the burnt areas caused by forest fires, using cloud computing of SAR image data with SVM, RF, and CART classification methods being considered, and the best result is selected to provide the forest fire impact map for the effective disaster mitigation efforts. The proposed method provides a faster mapping time, compared to the currently employed method, based on field report data, to enable a better and more efficient firefighting effort.
## 3 Results and Discussion
### Dimensional Accuracy
When printing at slow speeds, thick layers lead to a negative error, but when printing with speeds faster than 86 mm/s, thick layers lead to greater error than thinner layers due to the tension on the filament not allowing a good connection to the previous layer. Low temperatures combined with thick layers also indicate a negative error, as do thin layers and high temperatures. However, at high temperatures the outcome varies depending on the speed. When applying low speeds, the error  
Figure 8: Predicted effects of changes in process parameters on the error% for dimensional accuracy of 10 mm length in the Z direction. is negative since the liquid filament spreads out, which is enhanced when printing with thin layers. In comparison, at high speeds, the error is higher than 0.5%, which can be explained by the tension on the filament and inadequate bonding with the previous layer, which leads to a stacking of badly connected layers (see Figure 10). The effect of layer thickness and the resulting shrinkage problem, especially in the Z direction, was also found by [PERSON] et al. [34]. [PERSON] et al. [35] improved the dimensional accuracy in the Z direction by applying thin layers, which is in accordance with the findings of this study. To optimize the dimensional accuracy in the Z direction, a dense infill should be applied together with a high printing temperature. Thin layers and high printing speeds reduce percentage error. Although thick layers can, apparently, also achieve good accuracy, a layer thickness of 0.11 mm was chosen, since thin layers are still more accurate. The infill was set at 47%, printing speed at 95.8 mm/s, and printing temperature at 226 \({}^{\circ}\)C, which should produce a percentage error of 0.06%. However, when experimentally applied in a validation test, these values, produced a percentage error of 0.2%. Figure 10: Effect of printing temperature and printing speed on the dimensional accuracy in the Z direction. Figure 9: Effects of interaction of parameters on measured percentage error for 10 mm length in Z direction.
# Characteristics of Collimators Based on the Large-Mode-Area CMCF for Coupling Laser Beam
## 1 Introduction
A homemade single-mode collimator based on the large-mode-area concentric multilayer-core fiber (CMCF) is demonstrated, which could effectively improve the coupling loss. The theory and experimental measurements for the CMCF-based collimator are introduced, and some alignment processes are discussed and compared. This testing system was set up using an excitation laser as a light source and a pair of homemade collimators, and the coupling loss introduced by the angular deviation was calculated theoretically and measured experimentally. Satisfactory results were obtained through calculation and experiments, which improved the coupling efficiency by 20%, and thus significantly increased the stability of the optics coupling system.
## 1 Introduction  
With the rapid growth of global urbanization, urban population density is increasing dramatically. Landscape views in historic districts are blocked by modern tall buildings, and in the face of declining visual environment quality caused by uncontrolled rapid urban development for urban heritage protection, UNESCO has proposed the theory and methods of urban historical landscape [1]. The \"Macao Special Administrative Region Urban Master Plan (2020-2040)\" also mentions protecting world heritage as an important goal. In urban planning and design, the sustainable development of the overall urban landscape must be considered [2]. However, perception evaluations of important landscape views remain an urgent problem in the coordinated development and protection of historic cities. Constructing a visual environment aesthetic evaluation system with perception as the core has become a major research topic in the field of urban historical heritage protection and planning in recent years [3]. The use and satisfaction of urban landscape architecture physical spaces mediate the psychological health of the public [4]. Constructing an aesthetic evaluation system of the visual environment with perception as the core has become the core research issue in the fields of urban architecture, landscape architecture, and planning in recent years [5]. Visual perception accounts for approximately 80% of all types of human perception, and the visual system is the most accurate way to obtain external information [6]. The evaluation results of a study on urban streets and buildings provide a corresponding basis for the visual control of urban landscape architecture [7]. In a changing urban landscape, we need to understand which architectural elements can attract viewers' attention from viewing platforms and the psychological impact of their aesthetic evaluation of these visual elements on place identity.  
This study focuses on how modern high-rise buildings and key visual elements in urban landscapes affect the beauty and local identity of historical blocks; what methods can be used to construct a visual environmental aesthetic evaluation system based on perception; and how urban landscapes affect the public's mental health. We aim to construct a visual environment aesthetic evaluation system centered on perception to meet the challenges brought about by the uncontrolled urban development of historical blocks and evaluate the impact of modern high-rise buildings on their landscapes. This study explores the effectiveness of urban landscape perception assessment through eye-tracking and psychometric analysis; provides valuable insights for visual planning and protection in historic districts; and fills a research gap in assessments of spatial perception elements of historic urban areas based on place identity emotions.
# Quality Assessment of Organic Kefirs Made with Kefir Grains and Freeze-Dried Starter Cultures
## 2 Materials and Methods
### Methods
#### 2.2.2 Physicochemical Assessment  
In organic kefirs, the following parameters were estimated: pH (pH-meter Elmetron, Zabrze, Poland), titratable acidity (expressed as % of lactic acid), total solids content with the drying method [48], ethanol content, index of syneresis, texture parameters, and color and sensory assessment. Color was assessed with the same method as in milk. Ethanol, as the main volatile aroma compound in kefir, was measured according to the method described by [PERSON] et al. [47] through the titration of a 10 g sample with 0.1 M NaOH using phenolphthalein as the indicator. The syneresis index (_SI_) was determined using the centrifuge method [50] with modifications. A 20 g sample of kefir was weighed into 50 mL centrifuge tubes. Care was taken to disturb the curd as little as possible. Subsequently, the tubes were centrifuged at 3000 rpm for 20 min. The whey, separated above the curd surface, was gently transferred to a beaker and weighed. The syneresis index (_SI_) was calculated using the following formula:  
\[SI(\%)=\frac{\textit{mass of whey},\ \textit{g}}{\textit{mass of }\textit{kefir},\ \textit{g}}\times 100 \tag{2}\]
# Numerical and Experimental Analysis of the Oil Flow in a Planetary Gearbox
## 2 Materials and Methods
### Object of Investigation  
The object of investigation is the FZG internal gear test rig presented in Figure 1 (left). The test rig is composed of a planet carrier, three planet gears, and two ring gears, namely, a test ring gear and a drive ring gear. The planet carrier is driven by a continuously variable electric motor via a shiftable countershaft transmission. The three stepped planet gears of the test rig are arranged uniformly across the circumference and are engaged with both the test ring gear and the drive ring gear. The stages of the gearings exhibit the same gear ratio. The drive ring gear is float-mounted by spring elements which brace peripheral forces. Axial forces are supported by thrust washers mounted in the housing.  
The FZG internal gear test rig was designed to operate with oil injection lubrication. Thereby, temperature-controlled oil is injected evenly around the circumference from an oil supply unit via two separate oil distributor pipes, one at the front side and one at the backside of the test rig. The test rig was modified to optically access the internals of the test rig and to realize an oil dip-lubricated operation. Therefore, the front facing housing cover was changed by a modified acrylic exchange part. Instead of the original cylinder roller bearing mounted to the planet carrier, a ball bearing with compact profile was mounted to improve visibility. The front-facing distributor pipe was removed to further improve visibility and realize an oil dip lubrication. In combination with a throttled oil backflow by an adjustable valve, a quasi-stationary oil level is maintained. A controlled oil sump temperature is achieved by a constant oil circulation and flow heaters. Figure 1 (right) shows a rendering of the test rig configuration used for the investigations in this study.  
The gear geometry data are reported in Table 1. The mineral oil FVA3A (ISO VG 100) [26] was used as lubricant. The oil sump temperature was kept constant at 40 \({}^{\circ}\)C. In Table 2, the physical properties of the oil are reported. The camera used for the recordings is a Photron FASTCAM Mini AX200. A light source was exploited to produce high-intensity light directed towards the front cover.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Unit** & **Ring gear** & **Planet Gear** \\ \hline Center distance (a) & mm & & 59 \\ Normal module (m\({}_{\text{n}}\)) & mm & & 4.5 \\ Number of teeth (z) & - & 42 & 16 \\ Face width (b) & mm & 16 & 14 \\ Tip diameter (d\({}_{\text{a}}\)) & mm & 185.0 & 82.5 \\ Pressure angle (\(\alpha_{\text{n}}\)) & \({}^{\circ}\) & & 20 \\ Helix angle (\(\beta\)) & \({}^{\circ}\) & & 0 \\ Profile shift coeff. (x) & - & 0.1817 & 0.2962 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gear geometry.  
Figure 1: FZG internal gear test rig (**left**) and rendering of the modified FZG internal gear test rig with transparent housing cover (**right**) [25].
## 4 Results and Discussion
### Human Health Risk Index
According to the graph, the index had the highest value in Iasi, both for adults and children, of 0.04356 and 0.16107, respectively. For Timisoara, the lowest values were determined, with 0.02874 for adults and 0.10688 for children. The results of this work are also supported by other international publications. Even though the air quality indicators measurements are under the maximum allowed concentrations, according to the environmental standards, there are no completely safe limits, especially for children, who are much more vulnerable than adults [52; 53; 54]. As future work, from an immediate perspective, the results of this study will be the basis of a new calculation and the correlation of health indices considering various meteorological parameters as well. For example, the planetary boundary layer (PBL) majorly influences the cumulation and dispersion of pollutants at gro \begin{table}
\begin{tabular}{c c c} \hline \hline
**City** & **HI\({}_{\text{Adults}}\)** & **HI\({}_{\text{Children}}\)** \\ \hline Brasov & 0.03398 & 0.12590 \\ Cluj-Napoca & 0.03452 & 0.12749 \\ Iasi & 0.04356 & 0.16107 \\ Timisoara & 0.02874 & 0.10688 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Health risk index calculated for each city. Figure 9: Health risk index for adults and children for the main four cities of Romania. tion of the PBL behavior is obtained through its characterization as a mathematical object of multifractal type [55]. Therefore, the theoretical parameters of the PBL characterization validated by the experimental data will contribute to the realization of the correlations mentioned above, using the machine learning concept. Also, recent studies regarding the air quality in Romania have shown that due to the circulation of air masses that can transport, for example, dust over long distances, a pattern of optical characterization of aerosols has been created, over time, through modern monitoring remote sensing techniques such as Aerosol, Clouds and Trace gases Research InfraStructure--Romania (ACTRIS--RO) [56; 57]. Accordingly, considering the specific aerosol type at different scales (local, regional), the health indices presented in this paper will thus better emphasize the specific situations and vulnerability as similar studies applied in the case of water health hazards assessments [58].
## 2 The UAV VtolA7  
### Platform Design and Operating Principle  
The UAV VtolA7 shown in Figure 1, which is the subject of this study, is a lightweight compound-wing drone with eVTOL characteristics. It can be used as a test aircraft for validating control design, as well as a probe for future UAM vehicles. For example, it has a similar layout to the existing UAM vehicle ET120 (shown in Figure 2) [31], and can be used as a low-cost method for validation.  
As a compound-wing UAV, VtolA7 has conventional fixed-wing surfaces (ailerons, elevators, rudder), a front-mounted propeller, and a group of four vertical rotors arranged in an X pattern. Table 1 shows its general properties.  
The VtolA7 has two power systems: the hover power system and the propulsion power system. The hover system is powered by four rotor motors attached vertically on the wings. The propulsion system consists only of a single propeller mounted horizontally in front of the nacelle to provide cruise power. The VtolA7 has two attitude control systems:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Explanation** & **Value** \\ \hline \(S\) & Reference area & 0.46 m\({}^{2}\) \\ \(b\) & Wingspan & 2.286 m \\ \(c\) & Mean aerodynamic chord & 0.2 m \\ \(m\) & Mass & 6.2 kg \\ \(I_{x}\) & Moment of inertia around the \(x\)-axis & 0.5614 kg\(\cdot\)m\({}^{2}\) \\ \(I_{y}\) & Moment of inertia around the \(y\)-axis & 0.4479 kg\(\cdot\)m\({}^{2}\) \\ \(I_{z}\) & Moment of inertia around the \(z\)-axis & 0.87 kg\(\cdot\)m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: General properties of the research aircraft.  
Figure 1: Plan view of the research VtolA7 UAV.  
Figure 2: ET120, an existing UAM aircraft with a similar layout [31].  
a fixed-wing and a rotary-wing system. The fixed-wing system consists of three conventional fixed-wing rudder surfaces: the aileron, the elevator, and the rudder. The rotary-wing system can achieve attitude control through differential speed of the four rotors. Figure 3 shows its control mechanism.
## 3 Dataset and Experimental Setup
### Experimental Procedure
The mobile device was placed on an adjustable holder on the right side of the steering wheel and participants had the freedom to adjust the positioning of the holder at will, so that it fits their personal preferences. Thus, simulating a real-car setup as accurately as possible. 2. **N-Back Test--Cognitive Neutral**. The second distractor was the N-Back test. This distractor aimed to challenge exclusively the Cognitive capabilities of the subjects while driving. N-Back is a cognitive task extensively applied in psychology and cognitive neuroscience, designed to measure working memory [39]. For this distractor, participants were presented with a sequence of letters, and were asked to indicate when the current letter matched the one from n steps earlier in the sequence. For our experiments we set N = 1 and deployed an auditory version of the task where subjects had to listen to a prerecorded sequence of 50 letters. 3. **Listening to the Radio--Cognitive Emotional**. For this distractor, participants were asked to listen to a pre-recorded audio from the news and then comment about what they just heard by expressing their personal thoughts. As with the N-Back Test, this distractor challenges mainly the cognitive capabilities of the participant when driving but with one major difference. In contrast to the neutral nature of the previous distractor here the recordings were emotionally provocative hence, motivating an affective response from the side of the subject. In particular, the two recordings used as stimuli for this part were related to a) a potential active shooter event that took place in the greater Detroit area and b) reporting from a fatal road accident scene which took place in the area of Chicago. These choices were made to help the users relate better to the events described in the recordings. 4. **GPS Interaction--Cognitive Frustration**. At this step, we asked participants to find a specific destination on a 'GPS' through verbal interaction. The goal of this distractor was to induce confusion and frustration to the participant; emotions that people are likely to experience when driving, either by interacting with similar'smart' systems or through the engagement with other passengers or drivers on the road. In this case the 'GPS' was operated by a member of the research stuff in the background providing miss-leading answers to the participant and repeating mostly useless information until the desired answer was provided. Once the participants started driving they would not stop until the end of the recording. Thus, they did not experience any interruptions when switching from the 'free-driving' to the 'distractions' parts. For each of the distractors we had two similar alternatives, which we randomly switched between morning and afternoon recordings making sure that each subject would be exposed to a different stimuli each time they participated.