esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### SLAM-Based Autonomous Positioning and Navigation System
#### 4.1.3 IMU Module  
A LiDAR sends out all data from the same frame simultaneously. However, the UAV is constantly in motion, causing the position of the LiDAR's coordinate system to change. This movement introduces distortions to the collected LiDAR data. Assuming the UAV undergoes uniform motion, the IMU module performs distortion correction on the LiDAR data. The IMU can provide angular and linear acceleration measurements. By considering the installation position of the IMU and LiDAR, the transformation relationship between the IMU and LiDAR coordinate systems can be determined. The point cloud is then transformed to the IMU coordinate system. The pose transformation between two consecutive IMU frames is represented by Equation (4) and denoted as \(T_{m}\):  
\[\left\{\begin{aligned} roll_{m}&=\int_{m}^{t_{m+1}} \Delta roll\\ yaw_{m}&=\int_{m}^{t_{m+1}}\Delta yaw\\ pitch_{m}&=\int_{m}^{t_{m+1}}\Delta pitch\\ v_{m+1}&=v_{m}+a(t_{m+1}-t_{m})\end{aligned}\right. \tag{4}\]  
The transformation of the LiDAR pose at time \(t_{q-1}\) relative to the LiDAR pose at time \(t_{q}\) is given by Equation (5):  
\[T_{q,q+1}=\prod_{q<m<q+1}T_{m} \tag{5}\]
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 1 Introduction
### C-ITS Services and Use Cases  
Numerous services and use cases have been specified across various stakeholder groups and projects in Europe. These specifications differ significantly in aspects such as application areas, naming schemes, and document structures. In the context of C-ITS, key sources of information include 5 GAA, C2C-CC, C-Roads, and ETSI. Based on an analysis of these sources, services and use cases can be clustered into two broad categories:  
* Cooperation mode: Sender and receiver of the messages (V2V, V2I, I2V)
* Day of deployment: Roadmap with services and use cases that are going to be deployed in subsequent phases, called Day 1, Day 2, and Day 3 [14; 15; 16].  
As there are no exact definitions of what service and use case are part of which deployment phase, the C2C-CC roadmap presents a useful example of a description of the phases.  
* Day 1 summarizes services relating to \"awareness driving,\" including basic in-vehicle warning applications or traffic information (e.g., road works).
* Day 2 comprises services referring to \"sensing driving,\" including vulnerable road user protection or semi-automated driving functions (e.g., emergency brake assistant).
* Day 3 concentrates on \"cooperative driving,\" including cooperative automated driving functions (e.g., cooperative lane merging).  
The services and use cases presented in Table 1 are always realized by a communication link among ITS stations as specified in the ITS-Station architecture of ETSI [9]. The messages used for information exchange in the Day 1 services are listed in the C-Roads document [17].
## 3 Results
### Total Phenolic Content and Antioxidant Activities Assay  
The total phenolic contents and the antioxidant activities of Aronia cell biomasses obtained in different light regimes (light cycles and darkness) are presented in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline
**Compound,** & **Light Cycles** & **Dark** \\ \(\upmu\)g DW & & \\ \hline Gallic acid & ND * & 32.84 \(\pm\) 16.56 \\ (+)-Catechin & 2140.32 \(\pm\) 527.61 * & 163.99 \(\pm\) 8.69 * \\ Chlorogenic acid & 1812.79 \(\pm\) 46.19 * & 86.96 * 12.68 * \\ Vanillic acid & ND & 64.83 \(\pm\) 2.06 \\ Caffeic acid & 48.26 \(\pm\) 15.28 * & 37.42 \(\pm\) 4.89 * \\ (\(-\))-Epicatechin & 1934.60 \(\pm\) 727.56 * & 154.22 \(\pm\) 11.43 * \\ p-Couramic acid & 18.81 \(\pm\) 4.39 * & 8.35 \(\pm\) 3.84 * \\ Salicylic acid & 686.39 \(\pm\) 299.58 & ND \\ Rutin & 18.74 \(\pm\) 0.61 & ND \\ Hesperidin & 81.09 \(\pm\) 16.62 * & 7.03 \(\pm\) 1.59 * \\ \hline \end{tabular}
* ND—Not detected; the means with different letters in rows are significantly different at \(p\leq\) 0.01.  
\end{table}
Table 1: HPLC analyses of phenolic compounds by _Aronia melanocarpa_ cell suspension culture cultivated under light cycles and in darkness for 7 days.  
Figure 2: Total anthocyanins content (**A**) and extracellular polysaccharides production (**B**) by _Aronia melanocarpa_ cell suspension culture cultivated under light cycles and in darkness for 7 days. The means with different letters differed significantly at \(p\leq\) 0.01.  
When cultured under illumination (16 h light and 8 h dark), the cell suspension culture accumulates a significantly higher amount of total phenolics when compared to the dark growing culture (8.17 \(\pm\) 0.04 mg GAE/g DW, compared to 1.58 \(\pm\) 0.03 mg GAE/g DW, respectively). In all used assays, the cells cultivated under light cycles showed significantly higher antioxidant activity (Table 2). Correlation analyses showed that total phenolic content has strong correlations with all used methods for antioxidant assay (Table 3).  
Figure 4: Score plot presenting samples discrimination by the first two principal components of phenolic compounds found in _Aronia melanocarpa_ cell suspension culture cultivated under light cycles (red) and in darkness (blue).  
Figure 3: Hierarchically clustered heatmap of phenolic compounds found in _Aronia melanocarpa_ cell suspension culture cultivated under light cycles (green) and in darkness (red). The Ward method with Euclidean distance was used for data clustering. The samples are organized in columns and the metabolites in rows. The highest (red) and lowest (blue) levels of the compounds are mapped.
# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
## 4 Conclusions  
This study proposed a novel method for estimating the ice accretion volume on wind turbine blades using stereo vision and improved image processing algorithms. Experimental results demonstrated that the method achieves measurement errors within 10% for both regular-shaped ice blocks and simulated blade icing, meeting practical requirements for blade ice detection and management. The proposed method offers the advantages of non-contact measurement, strong environmental adaptability, and ease of deployment, making it suitable for use on operating wind turbines without requiring physical modifications. These results establish a solid foundation for advancing wind turbine blade ice detection technology and improving operational efficiency and safety in cold climate regions.
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 2 Preliminary  
### Kernel Principal Component Analysis Method  
Kernel Principal Component Analysis (KPCA) is a kind of non-linear principal component analysis [32]. This method uses kernel functions to map non-linear data to high-dimensional linear space, and performs linear principal component analysis on the mapped data in high-dimensional space. KPCA can effectively eliminate redundancy among data and spatial correlation, non-linear feature principal components for extracting main data information.  
Let the data set \(=\left[x_{1},x_{2}\ldots,x_{n}\right]^{T}\in R^{n\times m}\), where \(n\) is the sample number and m is the observed variable. Formula (1) is the kernel function \(K\!\left(x_{i},x_{j}\right)\):  
\[K\!\left(x_{i},x_{j}\right)=\exp\left(-\frac{\|x_{i}-x_{j}\|^{2}}{2\sigma^{2}}\right) \tag{1}\]  
The flow chart of KPCA is shown in Figure 1:
## 3 Experiment Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Initial** \\ **Yielding** \\ **Point of a** \\ **Main Bar** \\ \end{tabular} & \begin{tabular}{c} **Initial** \\ **Yielding** \\ **Point of** \\ **Stirrup** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Loading** \\ **Point** \\ **(Positive)** \\ \end{tabular} & \
\begin{tabular}{c} **Concrete** \\ **Loading** \\ **Point** \\ **(Negative)** \\ \end{tabular} &
\begin{tabular}{c} **Final Step** \\ **(Strength \textless{}60\%)** \\ \end{tabular} \\ \hline \multirow{2}{*}{FF-15S-II} & \(\begin{array}{c}1.0\,\%\\ \text{(Dynamic)}\end{array}\) & 4.0\% & 3.0\% & \(-\)4.0\% & 3.0\% & 8.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (2 nd) & (1 st) & (1 st) & (1 st) & (2 nd) \\ \hline \multirow{2}{*}{FF-15S-III} & \(\begin{array}{c}-1.0\,\%\\ \text{(Dynamic)}\end{array}\) & \(\begin{array}{c}-4.0\%\\ \text{(3 rd)}\end{array}\) & 4.0\% & 4.0\% & \(-\)4.0\% & 3.0\% & 8.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (3 rd) & (1 st) & (1 st) & (1 st) & (1 st) \\ \hline \multirow{2}{*}{FF-15S-IV} & \(\begin{array}{c}0.7\,\%\\ \text{(Dynamic)}\end{array}\) & 5.5\% & 4.0\% & \(-\)6.0\% & 3.0\% & 8.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (1 st) & (1 st) & (1 st) & (1 st) & (1 st) \\ \hline \multirow{2}{*}{FF-15S-II} & \(\begin{array}{c}-1.0\,\%\\ \text{(Dynamic)}\end{array}\) & 4.6\% & 3.0\% & \(-\)3.0\% & 3.0\% & 6.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (1 st) & (1 st) & (1 st) & (1 st) & (2 nd) \\ \hline \multirow{2}{*}{FF-15S-III} & \(\begin{array}{c}0.8\,\%\\ \text{(Dynamic)}\end{array}\) & \(\begin{array}{c}-1.2\%\\ \text{(Dynamic)}\end{array}\) & 4.0\% & \(-\)4.0\% & 3.0\% & 6.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (1 st) & (1 st) & (1 st) & (2 nd) \\ \hline \multirow{2}{*}{FF-15S-IV} & \(\begin{array}{c}-0.9\,\%\\ \text{(Dynamic)}\end{array}\) & \(\begin{array}{c}-4.0\%\\ \text{(Dynamic)}\end{array}\) & 4.0\% & \(-\)6.0\% & 3.0\% & 6.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (1 st) & (1 st) & (1 st) & (2 nd) \\ \hline \multirow{2}{*}{SF-30S-II} & \(\begin{array}{c}0.9\,\%\\ \text{(Dynamic)}\end{array}\) & 2.6\% & 3.0\% & \(-\)3.0\% & 3.0\% & 4.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (3 rd) & (1 st) & (1 st) & (1 st) & (1 st) \\ \hline \multirow{2}{*}{SF-30S-III} & \(\begin{array}{c}-0.9\,\%\\ \text{(Dynamic)}\end{array}\) & \(\begin{array}{c}-2.6\%\\ \text{(Dynamic)}\end{array}\) & 3.0\% & \(-\)3.0\% & 3.0\% & 4.0\% \\  & \(\begin{array}{c}(\text{Dynamic)}\end{array}\) & (1 st) & (1 st) & (1 st) & (1 st) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance points of each specimen under different loading scheme.
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Physician/Emergency Room Scheduling
#### 4.2.2 Papers
To validate their findings they worked in collaboration with an ambulatory cancer treatment polyclinic in Montreal, in which they were able to greatly lower costs and improve schedules compared to what was in use prior. Additionally, they believe that their model is generic enough to be implemented in many different clinics. Physician scheduling papers also have a focus on attempting to optimize aspects of the schedule that are not the schedule itself. The probabilistic nature of the emergency room means that being able to predict aspects such as the number of patients, the downtime of the ER, the probability of an emergency, and other variables can allow for more flexible and efficient schedules. The number of patients coming into a department is an unknown that can be optimized. Again, with the onset of COVID-19, capacities have decreased, and knowing how many patients are expected allows the schedule to better accommodate them. [PERSON], [PERSON], and [PERSON] [23] compared the results from The Hospital for Sick Children in Toronto from 1 June 2018 to 30 August 2020 to analyze how many patients visited the emergency department. The machine learning model used the Gaussian Process Regression (GPR)algorithm and trained on this data, making a prediction every hour and retraining every midnight. It was found that the model was able to achieve an average recall of 50-75% at predicting exact change, and 80% for any positive change. The sensitivity of the model could land anywhere between 20 and 70%, determined based on how acceptable false positives were. This variation comes from the fact that the GPR model is a probabilistic model. Additionally, they tested it against an AR-Lasso model and found that GPR outperformed it on all metrics. In terms of scheduling, this allows the hospital to know how many physicians should be scheduled at any given time and also how many patients they will be able to fit in. Another paper that approaches the problem of patient arrival prediction is \"Developing Emergency Department Physician Shift Schedules Optimized to Meet Patient Demand\" by [PERSON] et al. [24]. Likewise to the previous paper by [PERSON], [PERSON], and [PERSON] [23] they used historical data from their regional hospital to try and find temporal patterns and use this information to make predictions. This time they got their data from the Thunder Bay Regional Health Sciences Centre (TBRHSC) ED. The data was analyzed by fitting a generalized additive model (GAM). It used a Poisson framework, as this distribution best represented the arrival of patients. Using this predictive model they then created a mixed-integer programming model that utilizes the data generated to determine shift start times. The schedule generated was compared with the current schedule, and the new version outperformed it by all measured metrics in all scenarios. When used in combination with adding an extra fast track physician, unmet patient demand was lowered by as much as 69%.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 1 Introduction
* To better handle objects of varying scales, we design an auxiliary dynamic loss function that adaptively adjusts the loss weights for different object scales, thereby optimizing detection across diverse scale variations. The structure of this paper is arranged as follows. Section 2 provides a review of existing studies on feature fusion and the utilization of contextual information in remote sensing. Section 3 introduces the proposed ProSAF-Net in detail. Section 4 presents the experimental results and discussions. Finally, Section 5 concludes the paper.
## 3 Results
CIRs are associated with HSSs that interact with slow-speed (-300-400 km/s) streams and are characterized by embedded and amplified Alfven waves. CIRs usually lead to weak or moderate geomagnetic storms, mainly in the declining phase of the solar cycle. Moreover, they tend to evolve gradually and recover in longer time scales than CME-driven events. It is suggested that the CIR storms are driven by the magnetic reconnection of the southward component of the interplanetary Alfven waves to the Earth's dayside magnetopause fields [6; 7; 8; 9; 10; 11; 12; 13; 14; 15].
## 4 Results and Discussion  
### Vibration Signal Extraction and Electromotive Force Calculation  
The displacement, velocity and acceleration time-history curves of the three nodes (node 1, node 2 and node 3) that were in the middle of the loading position at 0.3 s, 0.6 s and 0.9 s were recorded, as shown in Figures 10-12. The location of node 1 was x = 161 mm, y = 2 mm, z = 51 mm. The location of node 2 was x = 300 mm, y = 2 mm, z = 51 mm. The location of node 3 was x = 441 mm, y = 2 mm, z = 51 mm.  
It can be seen from the node displacement curve in Figure 10 that the node displacement of the fixed beam includes static displacement and dynamic displacement. When the load was directly above node 2, the static displacement peak appeared, and the dynamic displacement fluctuated within a smaller range on the basis of the static displacement. It can be seen from Figure 10 that the fluctuation of the total displacement was very small, basically between \(-\)2.5 mm and 0.5 mm. Therefore, when a fixed value is selected for the electromechanical coupling coefficient \(k_{u}\), the error will be very small.  
Figure 9: State-switched array energy harvesting circuit.  
Figure 11: Node velocity time-history curve.  
Figure 10: Node displacement time-history curve.  
As shown in Figures 11 and 12, from the velocity and acceleration curves, it can be seen that when the load passed over the node, the vibration frequency changed greatly, and the velocity and acceleration fluctuation also increased to a certain extent.  
The selected coil sizes were height \(h\) = 10 mm, inner diameter \(r_{1}\) = 10 mm, outer diameter \(r_{2}\) = 12 mm and wire diameter \(d\) = 0.22 mm, and the permanent magnet sizes were height \(h_{M}\) = 10 mm and radius \(r_{0}\) = 9 mm. The relationship between the calculated electromechanical coupling coefficient \(k_{u}\) and \(z_{c}\) is shown in Figure 13.  
If a group of generator arrays is placed at the position of each node, the electromotive force at each coil of the bridge distributed damping and energy acquisition system can be calculated according to the maximum electromechanical coupling coefficient and the velocity time series of each point obtained by a finite element simulation, as shown in Figure 14.  
Figure 12: Node acceleration time-history curve.  
Figure 13: Variation of electromechanical coupling coefficient with \(z_{c}\).
## 2 Three-dimensional Object Detection Techniques
### Projection of Point Cloud in Bird's Eye View (BEV) or 2D Plane  
Another commonly practiced method for 3D object detection is projecting the 3D point cloud to a pseudo-2D or Birds Eye View plane. After 2D view projection, a neural network is applied to extract features and generate object prediction. Some researchers have applied CNNs after converting the point clouds into 2D planes in their research [38; 39]. A similar conversion technique from 3D point cloud to 2D plane is found in [40; 41]. Afterthe perspective view conversion, CNN-based feature recognition network is applied in this research.  
[PERSON], [PERSON] et al. used a LiDAR point cloud in the form of VoxelNet or PointPillars and then applied 3D CNN in feature map. A heat map is generated where the local maxima is the object center. Identifying the center made the object tracking faster. The nuScenes and Waymo Open Datasets are used in their research to demonstrate the precision [42]. [PERSON], [PERSON] et al. (2019) created a Birds Eye View projection from a point cloud and then applied Complex YOLO following the YOLO V2 technique to predict the object class. After that, Euler Region Proposal was used to detect the orientation of the object [43].  
Figure 2: Simplified illustration of 3D object detection technique stages.  
In research of [PERSON], [PERSON] et al. (2019), the features of point clouds are stacked in the form of pillars and named PointPillars. A pseudo-2D image is then projected with the PointPillar features. They claimed that any 2D CNN-based network can be applied at this stage to detect the object. In this paper, they applied Single Shot Detector (SSD) [44]. Though research on projecting 3D points to 2D planes has successfully detected road scene objects such as vehicles or pedestrians, the data transformation to another plane may lead to information loss and cause additional computation loss.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of NFTs Formation
#### 2.3.1 Inhibition of Pro-Inflammatory Factors  
2.3.1. Monoterpenes  
ROS production in AD flies fed with (+)-limonene (**44**) is reduced significantly, which means that (+)-limonene (**44**) has antioxidant activity not only in vitro, but also in vivo. That antioxidative activity has anti-inflammatory effects and so there is a reduced inflammatory response in this _Drosophila_ model of AD after (+)-limonene (**44**) is ingested [77]. Linalool (**1**), existing in several plants and extracted by hydrodistillation, decreases the levels of ROS significantly when a concentration of 400 \(\mu\)M is used. It also decreases the levels of NO significantly [78; 79]. Thymouinone (**45**) extracted from _Nigella sativa_ L. [80], inhibits the production of TNF-\(\alpha\) and IL-1\(\beta\)[81]. It crosses the BBB smoothly [82]. Carvacrol (**46**), existing in several plants, improves the in vivo and in vitro neuronal impairment induced by ethanol via the antioxidative and antiapoptotic pathways [83]. 1,8-Cineol (**47**) and \(\alpha\)-pinene (**48**) are other monoterpenes that show an in vitro neuroprotective activity against the H\({}_{2}\)O\({}_{2}\)-induced oxidative stress in PC12 (rat pheochromocytoma) cells. Pretreatment with these monoterpenes was found to attenuate the loss of cell viability and the changes in cell morphology. Moreover, they inhibited the intracellular ROS production [78; 84]. \(p\)-Cymene (**49**), another monoterpene existing in several plants, decreases the lipid peroxidation and nitrite content of the hippocampus of Swiss mice [78; 85].  
The oxidative pathways seem to be regulated by the nuclear factor erythroid 2-related factor 2 (Nrf2) as an upstream mediator [78]. Carvacryl acetate (**50**), borneol (**51**), and geraniol (**52**), other monoterpenes existing in several plants, show neuroprotective effects against the cognitive deficits by enhancing the activity of antioxidant enzymes and upregulating Nrf [78; 86; 87; 88].
# Electron Transport in \(n\)-Type InSe van der Waals Crystals with Co Impurities
## 3 Results and Discussion  
### Crystal Structure, Surface Morphology and Optical Properties  
The \(\gamma\)-phase InSe (R3m space group) and Co-doped \(\gamma\)-InSe single crystals were synthesized using the Bridgman method of crystal growth from a polycrystalline melt of In\({}_{1.03}\)Se\({}_{0.97}\). Co-dopants were introduced during the growth at a nominal concentration of 10% (see Materials and Methods). The rhombohedral unit cell of \(\gamma\)-InSe has lattice parameters \(a\) = \(b\) = 4.002 A and \(c\) = 24.961 A. The primitive unit cell of this crystal consists of three van der Waals layers. Each of these layers has a thickness of \(L\) = 0.8320 nm (i.e., 1 stoichiometric layer) and contains four covalently bonded monoatomic sheets with the following sequence Se-In-In-Se. The atoms form hexagons within each plane. The layers interact by weak van der Waals forces leading to anisotropic electronic properties and can be mechanically cleaved. The performed X-ray diffraction measurements (Figure 1) revealed that the lattice parameters of the InSe samples were weakly modified by doping and intercalation with Co (Table 1).  
Our studies reveal that the optical properties of InSe are largely preserved in the samples with Co impurities. Namely, the energy peak positions of the room temperature (\(T=300\) K) photoluminescence emission for the intercalated and doped samples are centered at the same energy \(h\upsilon=1.25\) eV as for the pristine bulk InSe (Figure 2). As could be expected, the intensity of the optical signal in InSe samples with Co impurities is lower than in the pristine one and is nonhomogeneous over length scales of several micrometers, but, remarkably, this semiconductor material remains optically active even with a high concentration of impurities. These attractive optical properties of the InSe semiconductor with magnetic impurities can open up a new avenue in material design offering innovative solutions for optoelectronics.  
Freshly cleaved surfaces of InSe samples were imaged using scanning electron microscopy (Figure 3). Figure 3a,b show smooth and defect-free van der Waals surfaces of pristine and Co-intercalated InSe, respectively. In contrast to those, image of the cleaved surface of Co-doped InSe shown in Figure 3c reveals nonuniform surface over the scale of hundreds of nanometers, which indicates that the large amount of Co impurities in Co\({}_{0.12}\)InSe causes a slight distortion of crystal structure resulting in defects, such as regions with misoriented layer planes.