esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quantification, Assessment, and 3D Visualisation of UES in Naples
### Operational Steps for 3D Modelling  
Three steps are used to visualise the 3D UES mapping. First, the geographic entities that represent the UES must be selected and georeferenced, as shown in Figure 3A; then, the application of the spatial AHP provides the standardised indicator values per cell on the geodetic grid, as shown in Figure 3B. Finally, 3D histograms, assigning the normalised indicator values to the cell z-values, show the spatial distribution of UES for the Naples LUZ. In particular, the ArcGlobe software (within the ArcGIS 10.3 platform) allowed us to visualise the last step, as shown in Figure 3C.  
Figure 3: (**A**) Information Services pre-processed map, (**B**) geodetic grid (\(500\times 500\) m) with standardised indicator values in the range of 0–1, and (**C**) 3D visualisation of Information Services.  
Afterwards, the 3D city model allows for a better understanding of the existing relationships between urban districts and the status of UES.  
The elevation data processed in this phase were derived from DSM and DTM models, provided by LiDAR, while the building footprints were within the ancillary data set of the Geofabrik service provider (which contained all the OSM data up to 2019-01-14T20:59:02).  
The computational process undertaken to develop the 3D city model followed four steps, aiming to:  
1. Perform a random point pattern within the polygonal footprints of the building shapefile. The maximum number of points per polygon within the random process was set as 50, depending on the features of the shapes and computational power. Points lying inside the boundaries of a building polygon had the same object identifier.
2. Assign surface information derived from DSM elevation data to each point pattern within the polygons through an average statistical interpolation.
3. Create a join table operation to arrange point surface information with respect to building polygons.
4. Use building z-values as extrusion values and show the elevation information (in metres above sea level) in ArcGlobe 10.3.  
Figures 4 and 5 highlight an excerpt of the 3D modelling for the focus area, zooming in on Naples city and overlapping the UES grid.  
The operational steps allowed us to enhance the spatial visualisation of the urban morphology and, simultaneously, overlay the UES indicator grid with 3D building neighbourhoods.  
Figure 4: Excerpt of the 3D city model with the Urban Ecosystem Services (UES) grid overlapped. The spatial relationships among building z-values and Regulation Services cell values are visualised.
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 2 Methodology
### Test Bed  
The simulation tests were carried out on a certified Polaris Navigation and Maneuvering Simulator by Kongsberg Maritime AS (Kongsberg, Norway), located at the Department of Marine Simulations at the Maritime University of Szczecin. The maneuvering simulator with an implemented simulation model of the vessel's movement was equipped, in addition to standard navigation and maneuvering devices and visual station for environment imaging, SDSS such as:The ECDIS decision support system in the form of the electronic navigational chart (ENC), 1:4000 scale (Figure 1a);  
The system of decision support with navigational display, with parallel top view projection, with a maximum contour of the vessel, was preliminarily called (Pmax) (Figure 1b);  
The system of decision support with navigational display, realized in the parallel top view projection, with the contour of the vessel on the theoretical plane of contact with the shore, labeled Pilot_precise (Pprecise) (Figure 1c);  
The spatial decision support system with the implemented display method in augmented virtuality technology, denoted by AV (Figure 1d), the construction of which has been described [46].  
The test stand used in the simulation studies is shown in Figure 2.  
Figure 1: SDSS used in simulation studies: (**a**) ECDIS, (**b**) Pilot_max, (**c**) Pilot_precise and (**d**) AV.  
Figure 2: The test stand used by navigators during simulation studies: (**1**) ECDIS, (**2**) Pilot_max, (**3**) Pilot_precise, (**4**) AV, (**5**) visual station and (**6**) other Integrated Bridge Systems.  
In order to eliminate errors in the indications of individual SDSSs, the process of calibration and verification of their indications in terms of mapping the shape and scale of objects as well as their position and orientation was carried out. This process consisted of exporting databases to one graphic environment, where the shape and scale of each object were verified. The re-exported objects were verified in terms of the correctness of their position and orientation, using the navigation tools built into individual decision support systems.
## 2 Methods
### Laboratory Experiments and Mie Scattering Calculations to Evaluate LISST-VSF  
In order to evaluate the LISST-VSF instrument, light scattering and beam attenuation measurements were made in the laboratory on samples of nearly monodisperse standard polystyrene spherical beads with mean nominal diameters of 100, 200, 400, 500, 700, and 2000 nm, which were suspended in water (Table 1). In addition to LISST-VSF, two other instruments were used in these experiments, a DAWN-EOS for measuring multi-angle light scattering and a dual beam UV/VIS spectrophotometer Lambda 18 (Perkin-Elmer, Inc., Waltham, MA, USA) equipped with a 15-cm integrating sphere (Labsphere, Inc., North Sutton, NH, USA) for measuring the beam attenuation coefficient of particles in suspension. The use of standard beads ensures that Mie scattering calculations for homogeneous spherical particles can be used to calculate the Mueller matrix elements for these particles to determine reference (expected) values of the volume scattering function and the degree of linear polarization. The comparison of measurements with such reference values allows for evaluation of performance of LISST-VSF instrument and formulation of calibration correction functions for improved determinations of the volume scattering function and the degree of linear polarization from this instrument. This type of approach, which combines measurements on standard well-characterized particles with accurate scattering calculations, has been previously used for the evaluation, calibration, and characterization of light scattering instruments [29; 43; 44; 50]. Although the evaluation results presented in this study are relevant to the specific version of the LISST-VSF instrument used in our laboratory, most methodological aspects are generally applicable to evaluation of other light scattering instruments.
## 3 Overview of 3 GPP AKMA
### AKMA Architecture and Operation
#### 3.1.2 Key Management Design  
When designing the 5G security architecture, it was anticipated that the master session key \(K_{AUSF}\) would be retained in the home network for potential future services. Following successful primary authentication of the UE, \(K_{AUSF}\) is stored both in the AUSF within the home network and in the UE. This concept allows for the reuse of \(K_{AUSF}\) for AKMA purposes, eliminating the need for a separate AKMA-specific authentication process.  
As depicted in Figure 3, AKMA brings two new keys into the 5G key hierarchy:  
* \(K_{AKMA}\), an intermediate anchor key derived from \(K_{AUSF}\).
* \(K_{AF}\), a key further derived from \(K_{AKMA}\) to be used between the UE and the AF.  
Due to the reuse of \(K_{AUSF}\), the \(K_{AKMA}\) and A-KID remain valid until a new primary authentication is completed and a new \(K_{AUSF}\) is generated. As an anchor key, \(K_{AKMA}\) has an implicit lifetime: it is replaced after a successful primary authentication or removed following an unsuccessful one. For seamless key provisioning for applications, the session key \(K_{AF}\) can remain valid until its predefined expiration, regardless of the status of \(K_{AKMA}\). This does not imply that \(K_{AF}\) has an unlimited lifetime; its duration is determined by configuration parameters or operator policy to minimize additional signaling for AKMA.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 3 Methodology  
Figure 1 shows the overall workflow of our proposed method. First, the time series data are preprocessed with data scaling, bandpass filtering, and data transformation based on the neural network architecture of choice (data preparation step). Secondly, an Autoencoder model is trained using normal (non-anomalous) time series data (model training step), which can finally be leveraged to obtain remapped time series predictions to compute the remapping error. If the remapping error is larger than a threshold, an anomaly is detected.
## 2 Autonomous Driving in Adverse Weather Conditions
### Autonomous Driving's Dataset Analysis for Adverse Weather Challenges
#### 2.5.1 Datasets for Adverse Weather Conditions
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Dataset** & **Number of Images** & **Weather Conditions** & **Annotations** & **Focus** \\ \hline IDD-AW & 5000 pairs & Rain, fog, snow, low light & Pixel-level segmentation & Safety in unstructured traffic environments, providing a variety of road scenes in adverse conditions [51]. \\ \hline SID & 178,000 pairs & Snow, rain, nighttime & Depth estimation & Designed for stereo vision tasks, emphasizing depth perception in low-visibility scenarios [52]. \\ \hline DAWN & 1000 images & Fog, rain, snow & Bounding boxes & Specialized in vehicle detection accuracy in low-visibility conditions, allowing models to improve object tracking and localization [53]. \\ \hline WEDGE & 3360 synthetic images & Simulated extreme conditions & Bounding boxes, SSIM metrics & Focuses on training models in rare and extreme weather scenarios like hurricanes, which are difficult to capture in real-world datasets [54]. \\ \hline nuScenes & 1.4 million images & Urban, night, rain & 3D bounding boxes, tracking & Provides multi-sensor data (camera, LiDAR, radar) for complex urban driving scenarios, making it suitable for sensor fusion research [55]. \\ \hline Waymo Open Dataset & 10 million images & Diverse geographical conditions & 3D object detection, trajectory & Offers high-resolution data with extensive labeling for objects, enabling long-range perception analysis [56]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of key datasets for adverse weather conditions in autonomous driving. The IDD-AW dataset is particularly valuable for training models that need to operate in less structured environments, such as rural or suburban roads, where conditions can vary significantly. In contrast, nuScenes and the Waymo Open Dataset provide comprehensive coverage of urban scenarios, including a variety of lighting conditions, from day to night. These datasets are especially suitable for training models that integrate multiple sensor modalities, such as cameras, LiDAR systems, and radar systems [55; 56].
## 4 Dynamic Simulation and Experimental Verification  
The simulated vibration signal is compared with the experimental measurement signal, and the dynamic simulation results are verified under conditions of a different faulty tooth number, and relative position of faulty teeth. The simulation parameters are detailed in Table 1.  
The gear test-rig consists of a controller, a three-phase motor, a test gearbox, and a load applied by a clutch, as shown in Figure 6. Table 1 indicated the performance parameters of the gear and bearing. The data acquisition system is B&K-3560C, and the experimental vibrations signal is measured along the meshing line of the gear teeth by the sensor mounted on the bearing pedestal. The sampling frequency is 65,536 Hz, and the sampling time is 1 s. Since the pinion is a faulty gear, the vibration signal \(a_{py}\) of the pinion in the direction of the meshing line is collected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Pinion** & **Gear** \\ \hline Number of teeth & 16 & 24 \\ Pressure angle (deg) & 20 & \\ Teeth module (mm) & 4.5 & \\ Teeth width (mm) & 14 & \\ Mass (kg) & 0.676 & 1.084 \\ Mass moment inertia (kg\(\cdot\)m\({}^{2}\)) & 0.000407 & 0.001168 \\ [PERSON]’s modulus (N/mm\({}^{2}\)) & \(2.06\times 10^{5}\) & \\ Poisson’s ratio & 0.3 & \\ Bearing damping (N-s/m) & \(4.86\times 10^{3}\) & \(6.16\times 10^{3}\) \\ Bearing stiffness (N/m) & \(3.5\times 10^{9}\) & \(3.5\times 10^{9}\) \\ Torque (N-m) & 23.5 & 35.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the dynamic simulation.  
Figure 5: Tooth error and base pitch error [50].  
Figure 6: The gear test-rig.  
The test-rig presented in Figure 6 is the standard FZG test-rig of STRAMA-MPS. This FZG test-rig consists of a controller, a three-phase motor, a test gearbox, and a load applied by a clutch. Table 1 indicated the performance parameters of the gear and bearing. The data acquisition system is BRUEL&KJAER-3560C, and the experimental vibrations signal is measured along the meshing line of the gear teeth by the sensor mounted on the bearing pedestal. The sampling frequency is 65536 Hz, and the sampling time is 1 s. Since the pinion is a faulty gear, the vibration signal \(a_{py}\) of the pinion in the direction of the meshing line is collected.
# The Potential of Cannabidiol in the Management of Oral Infections
###### Abstract  
Oral infections, caused by bacterial, fungal, and viral pathogens, are a significant source of dental morbidity and can lead to systemic complications, especially in immunocompromised individuals. Complex microbial interactions and host immune responses drive common conditions such as dental caries, periodontal disease, oral candidiasis, and herpetic lesions. Conventional antimicrobial therapies face limitations due to resistance and adverse effects, prompting interest in alternative treatments. Cannabidiol (CBD), a non-psychoactive compound derived from _Cannabis sativa_, has emerged as a promising candidate due to its antimicrobial, anti-inflammatory, and immunomodulatory properties. CBD targets various molecular pathways, including cannabinoid receptors, TRP channels, adenosine receptors, and PPARs, contributing to its multifaceted therapeutic effects. It has demonstrated efficacy against oral pathogens such as Streptococcus mutans, Enterococcus faecalis, and Candida albicans, disrupting biofilms and bacterial membranes. Additionally, CBD modulates inflammatory responses by reducing cytokine production and oxidative stress, particularly relevant in chronic conditions like periodontal disease. Emerging evidence also suggests synergistic effects with conventional antimicrobials and benefits in tissue regeneration. This review highlights the therapeutic potential of CBD in managing oral infections, offering a novel approach to overcoming current treatment limitations and guiding future research into safer and more effective oral health interventions.  
cannabidiol; oral infections; S. mutans; E. faecalis; C. albicans
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 2 Methods
### Automatic Offline Calibration Routine
To fulfill the above-mentioned requirements, the developed automatic capture routine moves the camera in discrete positions within a spherical surface centered in the calibration pattern center, as shown in Figure 5. The radius of the spherical surface corresponds, approximately, to the average operating distance. The camera rotations are such that the center of the pattern is kept in the center of the image plane. The described movement is equivalent to the rotation of the calibration pattern around its center in each of the three rotation dimensions (yaw, pitch, and roll) in a  
Figure 5: Camera movement in a dome around the centre of the checkerboard pattern seen from a pattern-centric perspective (**a**) and from a camera centric perspective (**b**). camera-centric interpretation of the problem. For the sake of simplicity, we calculate the desired camera-to-target transformations (\(T_{\text{CI}_{i}}^{K}\)) using the camera-centric interpretation:  
\[T_{\text{CI}_{i}}^{K}=T_{\text{CI}}^{K_{i}}=T_{\text{CI}}^{K_{0}}\ T_{K_{0}}^{K_{ i}}\,. \tag{3}\]  
The base-to-end-effector transformations that bring the camera to the desired calibration positions are calculated in the following manner:  
\[T_{W}^{TCP_{i}}=T_{W}^{TCP_{i}}\ T_{TCP}^{C1}\ T_{\text{CI}_{1}}^{K}\ T_{ \text{CI}_{i}}^{-1}\ T_{TCP}^{C1}\,, \tag{4}\]  
where \(T_{TCP}^{C1}\) represents a rough estimate of the hand-eye calibration. \(T_{W}^{TCP_{j}}\) represents the initial position reported by the robot's CS and \(T_{\text{CI}_{j}}^{K}\) represents the initial transforming between the camera and the target and is estimated from a single image based on nominal values of the camera calibration parameters. The calibration procedure runs automatically. All the operator needs to do is to move the manipulator so that the checkerboard is within the field of view of the camera for the initial pose. At the end of the procedure, the operator will receive a visual representation of the re-projection error indicating whether the procedure was successful. The procedure can be used to capture any desired number of images, and the steps within the range of movement are adapted accordingly. The captured images are used to calibrate the intrinsic parameters of the cameras and the extrinsic parameters in the case where a stereo pair is used and serve as data for a hand-eye calibration routine. A hand-eye calibration is required for this application in order to align the coordinate systems of the robotic manipulator and the vision system. The details of such a calibration are outside the scope of this paper, and are presented in detail in [39]. The calibration procedure shall be repeated as needed based on the volatility of the estimated parameters in the real operating conditions.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 5 Machine Learning for CML-Based Rainfall Measurement
### Potential of Deep Learning  
It can be found that DL algorithms, such as CNN, RNN, LSTM, and GRU, are widely used in CML-based rainfall measurement compared with the traditional machine learning algorithms. This is because traditional machine learning has limited ability to simulate high spatio-temporal dynamics events such as rainfall, while DL can capture high-dimensional and multi-modal data distribution, automatically extract spatio-temporal features, and better mine the internal logic of data [89].  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Algorithms** & **Function** & **Data Source** & **Results** \\ \hline
[65] & GRU-RNN & Rain rate estimation & A total of 1.4 M samples are from 40 full duplex links and 8 RGs in Swedish region, and 1.7 M samples are from 34 full duplex links and 9 RGs in the Israeli region & A total of 1.4 M samples are from 20\% as validation. & RMSE and bias are smaller compared with the traditional power-law-based algorithm, and the trade-off between performance and robustness of RNN methods can be controlled by introducing a TN layer. \\ \hline
[85] & SVC, ANN & Wet/dry classification, rain rate estimation & Measurement report (MR) data from TD-LTE networks, and RGs data and runoff data are used as references & 60\% of the wet/dry records are used as ANN training samples for classification, while the remaining 40\% are used as testing samples. & The performance of rainfall retrieval from MR data is in good agreement with RG measurements, and the accuracy is more than 80\% in the application of runoff simulation. \\ \hline
[86] & ANN, LSTM & Rain rate estimation & 3\(\times\)216480 RSL units and 2164800 target rain rate samples in Korea region, and satellite RSL data in Ethiopia region & Data are split into 85\% and 15\% for training and testing. & Rainfall retrieval performance of ground link is better than that of satellite link. Performance (RMSE, R\({}^{2}\), CC) of LSTM at 11 GHz ground link is better than that of ANN. \\ \hline
[87] & DT, PNN, GDA, LR & Rainfall cycles classification & 2475 samples of convective rainfall (31.3\%) and 5441 samples of stratiform rainfall (68.7\%) from March to November & 7916 total samples are divided into 5 groups on average, 4 groups are selected as the training set, and the remaining 1 group is used as the test set. & DT and PNN algorithms have better fault tolerant ability than GDA and LR, and the classification accuracies of tri-frequency models are higher than those of dual-frequency models. \\ \hline
[50] & LSTM & CML attenuation prediction & 17 CMs with the frequencies of 14-39 GHz & 1400 h of training time. & 16 h of attenuation values by LSTM during rainfall is greater than ARIMA. \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont_.
## 4 Discussion
### Health-Related Quality of Life  
Attempts to quantify health-related quality of life involve the use of dedicated questionnaires. In the case of temporomandibular dysfunctions, e.g., the Fonseca questionnaire, Oral Health Impact Profile-14 (OHIP-14), or Oral Behaviors Checklist (OBC-21) are used. They make it possible to determine ailments with output scores. However, these results are still an imperfect representation of the patient's subjective experience. In the case of a clinical trial, the results of the assessment carried out according to a given questionnaire are averaged for the group. In the next stage, the difference in means between the endpoints and the initial point of the study is determined, and in the case of controlled studies, between the endpoints of the study and control groups. The differences in means obtained in this way are synthesized in review works, including this one. Such highly transformed data blur the content of individual patients' complaints. Therefore, great caution should be exercised when interpreting health-related quality-of-life syntheses [39; 40; 41; 42]. The collected material shows that replacing the temporomandibular joint improves quality of life. The results of the primary studies, homogeneous in terms of the diagnosis of advanced TMJ degeneration, are consistent on this point. Therefore, it seems that in situations where the possibilities of conservative treatment and less invasive surgery have been exhausted (injections, arthroscopic procedures, open surgery with preservation of the TMJ structures), implantation of a prosthesis improves the comfort of functioning.
# Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
## 4 Experimental Setup and Result Analysis
### The Experimental Analysis
#### 4.2.5 Influence of Different Experimental Locations on Recognition Effect  
We collected data from different experimental directions to verify the influence of different locations on the experimental results. Experimental location refers to five different locations set in the experimental environment, which are used to collect gesture data of each location. Considering the same experimental environment, in experimental environment, the experimenter carried out experiments in the transmission and reception process and tested the influence of five different experimental locations in each experimental environment on the experimental results. As shown in Figure 9, the effects of E and D on the five locations are arranged from high to low. The distance between the sensor and the human body is set to 0.8 m, and the recognition results of the four environments are shown in Figure 16; from the overall results, it can be seen that the recognition results of different experimental locations are significantly different. The experimental results show that the recognition effect obtained by the experiment at the E location is the best.