esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectral Line Reflectance and Fluorescence Imaging Device for Skin Diagnostics
## 4 The Device Test Results  
The device clinical validation was just started, and only two trials have been performed so far. Examples of some captured/extracted spectral line image/ autofluorescence image sets are presented in Figure 7. Depending on wavelength, the contrast in the spectral line images changes due to the different penetration depth in skin. In the case of dermal nevus (upper row)--a pigmented lesion located  
Figure 5: The user’s interface.  
Figure 6: The prototype device in operation.  
below the epidermal-dermal junction--notably increased melanin content is observed with slight changes in the concentrations of three other chromophores. Hemangioma (middle row) is a vascular malformation where an increased content of oxy-hemoglobin is accompanied with a decreased content of deoxy-hemoglobin (relatively to the surrounding healthy skin). Seborrheic keratosis (lower row) is a pigmented lesion that frequently is misdiagnosed as malignant basalioma or melanoma; it also exhibits increased melanin content. In contrary to the other skin malformations, seborrheic keratosis has a unique feature of brighter autofluorescence (under the 405 nm excitation) than that of the surrounding healthy skin [29]--this is clearly seen if the three presented autofluorescence images are compared.  
To conclude, the initial test data are in general agreement with the data obtained in previous multi-spectral skin imaging studies [5; 12; 23; 24] and confirm the potential of the proposed device for non-contact assessment of various skin malformations.  
Figure 7: Spectral line and autofluorescence images of three skin malformations taken by the prototype device with the correspondingly calculated chromophore distribution maps. Units of the concentration increase/decrease are milli-modes for the first three chromophores and moles for lipids.
## 3 Results
### Experimental Data Analysis  
#### 3.4.1 Feature Visualization of the Dataset  
As illustrated in Figure 6a, the dataset utilized in this experiment maintains an equal distribution between real and fake face images. This balance enables the detection model to capture more nuanced features, reducing the risk of bias toward either real or fake images, and ultimately enhancing both the accuracy and fairness of the model. Figure 6b depicts the spread of the central points of the forged components inside the manipulated face images, highlighting that the forged parts are primarily concentrated in the center region of the image. The analysis of the distribution pattern of aspect ratios of the manipulated components, as depicted in Figure 6c, provides valuable insights into their structural variations and patterns within the dataset. It is evident that the aspect ratios of the forged elements in the majority of fake face images tend to cluster into two distinct extremes: one with smaller aspect ratios and the other with larger ones.
# A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments
## 3 Results
### Deploy Components on Edge and Cloud  
Certain CMLs allow for specifying the location of hosts, whether in the cloud or at the edge. Other researchers have proposed languages enabling users to specify whether an application component should be deployed in an edge or cloud computing environment using CMLs. Viewed semantically, these functions express similar concepts, and their integration would improve the process of matching components with hosts.  
Languages designed for describing deployments of IoT application components across different environments appear to provide robust support for such functionalities. The research discussed in [36] centers around the utilization of the ADOxx Definition Language (Architecture, Design, and Organization) to determine the most suitable placement of IoT application components in fog and cloud environments. Another language, as described in [18], offers seamless deployment of IoT application components across both cloud and edge environments. Additionally, the research conducted by [PERSON] et al. [21] focuses on describing container deployments across edge, fog, and cloud nodes. In the research conducted by [PERSON] et al. [29], a method for automating container deployment in edge and cloud resources is introduced. This approach involves using a defined XML syntax that allows for users to specify in the execution environment element whether a container should be deployed on the cloud or the edge. Unlike other CMLs that describe the placement issue from the components' perspective, the SMADA-Fog deployment model [12] has the capability to specify the location of a host, whether it is on the cloud side or the edge side. In the context of the SMADA-Fog deployment model [12], the Task entity encompasses a property indicating whether the component should be deployed on an edge or a cloud host. It subsequently matches this requirement with a host characterized as either a cloud or edge-side resource within the model.  
Other researchers advocate for the use of graphical user interface (GUI) tools to facilitate the description of application placement. The research presented in [35] introduces a GUI tool enabling users to design models depicting communication among various physical devices, including smartwatches, sensors, edge nodes, and cloud resources. The goal is to develop a tool that models communication between physical devices and virtual machines hosting application components, aiming to deploy systems spanning the edge-cloud continuum. An example use case discussed in the research is a Patient Monitoring System, where sensors gather vital signs from patients, sending data to the nearest edge node for preprocessing, and then forwarding processed data to intermediate components hosted on cloud resources. Another notable example is the aforementioned research that employs the ADOxx Definition Language from [36]. This approach allows for users to create GUI modeling tools using a meta-model similar to UML.
## 2 Materials and Methods
### Methods
#### 2.2.4 Urban-Rural Fringe Identification  
The fusion of these diverse features enabled us to capture subtle variations that might be overlooked by unidimensional analyses. To integrate these multifaceted features, we constructed a 103-dimensional feature vector for each 250 m \(\times\) 250 m grid cell, represented as:  
\[F=F_{rs}+F_{ntl}+F_{building}+F_{poi} \tag{4}\]where \(F_{rs}\) is the 64-dimensional feature vector from remote sensing imagery, \(F_{ntl}\) is the 10-dimensional feature vector from nighttime light data, \(F_{building}\) is the 15-dimensional feature vector from building footprints, and \(F_{poi}\) is the 14-dimensional feature vector from POI data.  
To classify the study area into urban areas, rural areas, and the urban-rural fringe based on this high-dimensional feature space, we employed the K-means clustering algorithm with K set to 3 [52; 53]. We opted for K-means clustering instead of classification methods due to its unsupervised nature, which was well suited for identifying natural groupings in complex urban-rural transitions without relying on predefined labels. This approach also provided flexibility in defining boundaries between urban, rural, and fringe areas based on data-driven patterns. Moreover, with our focus on three distinct categories (urban, rural, and urban-rural fringe), K-means clustering with k = 3 aligned perfectly with our research objectives. The K-means algorithm partitioned the study area into three clusters by minimizing the within-cluster sum of squares:  
\[\text{argmin}_{S}\sum_{k=1}^{K}\sum_{x_{i}\in S_{k}}\parallel x_{i}-\mu_{k} \parallel^{2} \tag{5}\]  
where \(S=\{S_{1},\ S_{2},\ S_{3}\}\) is the set of three clusters, \(x_{i}\) is the feature vector of the ith grid, and \(\mu_{k}\) is the mean of points in \(S_{k}\). After clustering, we interpret the resultant three clusters based on their feature compositions and spatial distributions.  
While our methodology leverages the advantages of multisource geospatial big data and machine learning techniques, it is important to acknowledge several potential sources of bias and uncertainty. The quality and completeness of different data sources may vary across the study area, with POI data potentially showing sampling bias towards commercially active areas. The deep learning-based feature extraction from remote sensing imagery can be influenced by image quality variations and seasonal changes. Additionally, the K-means clustering algorithm, being unsupervised, may be sensitive to initial centroid placement. These limitations should be considered when interpreting the results and could be addressed in future research.
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### Using ROPP to Convert COSMIC-2 Excess Phases to Bending Angle Profiles and Refractivity Profiles  
We used the ROPP-9.0 [37] to convert the excess phase to a bending angle. For lower atmosphere open-loop datasets with multi-path effects using wave optics (WO) retrievals,ROPP uses canonical transform of the second type (CT2) [47] to derive the bending angle from the excess phase. The CT2 approach is different from the Full Spectrum Inversion (FSI) method. While the FSI assumes a circular orbit for the satellites [33], the CT2 method incorporates the numerical efficiency of the FSI. It provides the most accurate results compared to other available bending angle inversion methods [32].  
Note that because the ROPP-9.0 is not initially configured for COSMIC-2 data processing, we made some code changes to the package to include GPS and GLONASS for COSMIC-2 excess phase processing, with a similar configuration for applying ROPP to process COSMIC data.  
The Mass Spectrometer and Incoherent Scatter Radar (MSIS) climatology dataset is used with ROPP for statistical optimization, excess phase model calculation, bending angles, and refractivity conversion. The Linear Abel integral method is used in Abel conversion in ROPP, assuming a linear variation of bending angle and refractivity logarithm. The statistical optimization method is used to derive an optimal bending angle. The vertical resolution of the output bending angle is interpolated to 100 m from the raw resolution. We extend the inversion to 150 km and keep the bending angle, refractivity, and dry temperature at the exact top height. Other parameters are kept the same as the default settings in the ROPP preprocessing package for the COSMIC-1 processing. The cut-off limits from impact height are \(-\)2 km, for the bending angle is 0.1, and the straight-line tangent altitude (SLTA) is \(-\)250 km.  
In addition, we also modified the original ROPP codes to accommodate the COSMIC-2 high-rate occultation observations (100 Hz compared to COSMIC-1 50 Hz) and to include GLONASS observation signals. For GLONASS, the L1/L2 frequencies are different from GPS, and the navigation bit message 100 Hz is also different from GPS 50 Hz. This has been coded into the package. ROPP adopts a simple earth-centered inertial coordinate system, which only considers the mean sidereal time effects (Earth rotation). While the antenna velocity is provided with excess phase calculation, ROPP calculates the velocity internally from the position and time directly in ECI coordinates. We have generated the position and velocity data consistent with this ECI frame and input them into ROPP for bending angle inversion.
# MODIS Retrieval of Aerosol Optical Depth over Turboid Coastal Water
## 2 Data  
### MODIS Data  
MODIS is an earth-viewing sensor on board the Terra and Aqua polar-orbiting satellites, launched in December 1999 and May 2002, respectively. MODIS has 36 channels spanning the spectral range from 0.41 to 15 \(\upmu\)m with spatial resolutions of 250 m (2 channels), 500 m (5 channels), and 1 km (29 channels). Its 2330-km swath width enables it to provide near-global converge daily. Terra and Aqua cross the equator from north to south (descending node) at approximately 10:30 a.m. local time and from south to north (ascending node) at approximately 1:30 p.m. local time, respectively.  
Here, all MODIS data products are labeled as MxDNN, where x is substituted by O for Terra and Y for Aqua, respectively, and NN is the serial number of a specific product. [PERSON] et al. (1998) presented a general description of MODIS atmosphere data processing architecture and products. In this study, the MODIS-calibrated TOA reflectance product (MxD02) ([[http://mcat.gsfc.nasa.gov/content/11b-documents](http://mcat.gsfc.nasa.gov/content/11b-documents)]([http://mcat.gsfc.nasa.gov/content/11b-documents](http://mcat.gsfc.nasa.gov/content/11b-documents))), atmospheric profile product (MxD07), geolocation product (MxD03), and aerosol product (MxD04) ([PERSON] et al., 2016) are used for retrieving aerosol optical depth over turbid coastal water. TOA reflectance of 2.1 \(\upmu\)m with spatial resolution of 500 m from MxD02 is used to retrieve AOD through a lookup table (LUT) method. Gas absorption by water vapor and ozone column are corrected from the TOA reflectance with spatial resolution of 5 km from MxD07 product ([PERSON] et al., 2016). The MxD03 product has a spatial resolution of 1 km and is used to distinguish between water and land pixels. All other ancillary information from MxD04 aerosol product for each valid AOD retrieval (at spatial resolution of 10 km at nadir) are also used as input to the retrieval algorithm in this study; the information includes aerosol mode index, reflectance weighting parameter, and National Centers for Environmental Prediction  
Figure 1: Regional (**a**–**e** and **g**–**i**) and global (**f**) distribution of AOD unavailable ratio which is defined as the ratio between number of non-retrieval pixels and all pixels in cloud-free conditions. MYD04 product in 2016 are used to conduct the statistics. Zoom in on the red-box regions of (**f**) are shown in (**a**–**e** and **g**–**i**). Lime points in (**f**) are AERONET sites used for validation and more information is in Table 1. Numbers in the pink boxes are AOD unavailable ratio over coastal water in clear sky condition.  
(NCEP) analysis of wind speed (2 m above the surface). In addition, a cloud mask with a spatial resolution of 500 m from MxD04 aerosol product is also used to ensure only AOD during cloud-free conditions over the turbid coastal region is retrieved by this study.
## 6 Conclusions
### Contribution to the Field of Sports Science and Analytical Research  
This project significantly advances sports science and analytics by offering an in-depth analysis of factors influencing football players' performance and well-being. It achieves this through several key contributions:  
The research provides a holistic view by integrating and scrutinizing a variety of football-related variables such as player workload, fatigue, performance indicators, and personal attributes. This comprehensive approach yields a deeper understanding of the complex interactions among these factors. It directly impacts decision-making in sports, offering valuable insights for athletes, coaches, and teams. The study guides informed, data-driven strategies in team management, training, and injury prevention by elucidating the relationships between workload, player characteristics, and performance outcomes.  
This research adds depth to injury management by exploring variations in injury occurrences across different clubs and age groups. The insights gained enable the formulation of targeted injury prevention and management strategies, potentially reducing player downtime and enhancing overall team performance. For instance, the study's findings about the heightened injury risk in older players ('Veteran' category) suggest that teams should tailor their fatigue management approaches for this specific age group.  
Moreover, this study paves the way for future research in football analytics, especially in injury prediction and prevention. It encourages further exploration into multidimensional factors affecting player injuries, both on and off the pitch, such as medical support, squad rotation strategies, and mental fatigue.
## 2 Materials and Methods
### Characterization of SN-SV  
#### 2.2.1 Size, Surface Charge, and Morphology  
The particle size and surface charge (\(\zeta\)-potential) of SN-SV were measured using a Malvern Zetasizer nano 6.01. In contrast, the surface morphology was assessed using a scanning electron microscope (SEM) (JEOL JSM-550, Tokyo, Japan). Briefly, 20 \(\upmu\)L of SN-SV was dried on stubs, followed by sputter coating with platinum for 30 min. The stubs were then viewed under an accelerating voltage of 4-25 kV under argon gas [25].
## 3 Results  
### Influence on Stability by Integral Squeeze Film Bearing Damper  
According to the finite element model in Section 2.3, the cantilevered disk of the rotor system has an unbalanced mass and the system passes the first order critical speed. The influence of the four working conditions on the stability of the unbalanced single-side cantilevered rotor system is shown in Figure 11. The closer to the cantilevered disk the ISFBD is installed, the better the system stability. When there are more installed integral squeeze film bearing dampers installed, the system stability is improved.  
As shown in the curves of the rotational speed-logarithmic decay obtained in Figure 11a-d, there are actually four curves in each figure. The curves from the bottom to the top in each figure are the first-order forward precession, the second-order forward precession, the third-order forward precession and the third-order backward precession. Since the rotor systems only pass the first-order critical speed, we just look at the bottom curve. It can be known whether the logarithmic attenuation curve with a threshold less than 0 is the basis for judging whether the unbalanced single-side cantilevered rotor system is stable. And the greater the logarithmic attenuation value, the more stable the rotor system. Figure 11a has lots of discrete points indicating the instability of the rotor system. In addition, the logarithmic attenuations of Figure 11b-d are greater than 0, so they are stable systems, where the stability of (d) is better than (c), and the stability of (c) is better than (b).  
It can be seen from the simulation of the four working conditions that the single-side cantilevered rotor system is unstable when there is an unbalanced amount in the cantilevered disk, as shown in Figure 11a.  
However, when the integral squeeze film bearing damper is installed in the single-side cantilevered rotor system, the system is stable due to the stiffness and damping provided by the damper. It can be seen from Figure 11a-c that the position of the damper has an influence on the stability of the unbalanced single-side cantilevered rotor system. The closer the damper is installed to the cantilevered end, the better the stability of the unbalanced single-side cantilevered rotor system. It can be seen from Figure 11a-d that the quantity of bearing damper installations also has an effect on the stability of the system. When dampers are installed at both bearing housings, the stability of the unbalanced single-side cantilevered rotor system is better.  
Therefore, when the unbalanced single-side cantilevered rotor system is retrofitted, the integral squeeze film bearing damper should be installed as close as possible to the cantilevered end bearing housing or the quantity of dampers should be increased if possible. These two ways can effectively improve the stability of the unbalanced single-side cantilevered rotor system. When two dampers are installed, the system has the best energy distribution.  
Figure 10: Diagram of the experimental platform.
## 4 Experiments  
### Datasets  
To facilitate a fair comparison, we use the following five frequently adopted benchmark datasets:  
1. Kvasir-SEG [46]: This dataset, gathered by the Vestre Viken Health Trust in Norway, comprises 1000 colonoscopy video sequences showcasing polyp images, each accompanied by its respective annotations. The image resolution ranges from 332 \(\times\) 487 to 1920 \(\times\) 1072 pixels. Annotations are meticulously labeled by medical professionals and validated by seasoned gastroenterologists.
2. ClinicDB [47]: The collection of images within this dataset, totaling 612 and sourced from 29 distinct colonoscopy procedures, features a pixel dimension of 288 \(\times\) 384. This initiative was developed collaboratively with the renowned Hospital Clinic of Barcelona, located in Spain.
3. ColonDB [48]: The dataset encompasses 380 images of polyps, complete with their respective annotations, all captured at a resolution of 500 \(\times\) 570 pixels. These images are extracted from 15 distinct videos, with frames meticulously chosen by experts to showcase diverse perspectives. The annotations are manually crafted with precision.
4. CVC-300 [49]: The dataset includes 60 images of polyps, each with a resolution of 500 \(\times\) 574 pixels.
5. ETIS [50]: This dataset was released by the MIC-CAI Polyp Detection Subchallenge in 2017, encompassing 196 polyp images extracted from colonoscopy videos, each paired with its respective label. The images boast a resolution of 966 \(\times\) 1225 pixels.  
These datasets provide polyp images of varying quantities and resolutions, covering diverse scenarios and perspectives. All images have been manually annotated and verified by medical professionals, serving as benchmarks for evaluating algorithm performance and conducting fair comparisons.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 5 Development Trend of Water Quality Monitoring Based on UV-Visible Spectroscopy  
The commonly used water quality monitoring technology involves manually collecting water samples in the laboratory for analysis, which makes it difficult to use to meet the requirements of on-line real-time monitoring. Even the more advanced chemical analysis methods for water quality monitoring require at least 20 min to obtain the detection results, which cannot provide timely warning for the occurrence of water pollution events [73; 74].
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 5 Simulation Results
### Performance Analysis  
To analyze the performance of the ADR-based attitude tracking Controller and the quaternion tracking controller, the ISE index defined in (19) was used [58]:  
\[ISE=\int_{0}^{T}\tilde{\kappa}^{2}(t)dt \tag{19}\]  
where \(\tilde{\kappa}(t)\) represents a signal error and \(T\) the time horizon.  
One uses the axis-angle representation to analyze the attitude error, representing a rotation of angle \(\tilde{\beta}\) about a unit axis \(\tilde{\mathbf{e}}\) of the body-fixed reference frame \(\mathbf{E}^{b}\) relative to the desired reference frame \(\mathbf{E}^{d}\). Note that the integration of (18) drives \(\mathbf{E}^{d}\). Then, given a quaternion error, \(\tilde{\mathbf{q}}\), one can obtain the angle-axis representation corresponding to \(\tilde{\mathbf{q}}\), as follows (see Equation (3)):