esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination +
###### Abstract
More specifically, the density-based algorithm of DBSCAN (Density-Based Spatial Clustering of Applications with Noises) is employed in combination with the partition-based K-means clustering algorithm. These algorithms differ in their approach and assumptions, and it is asserted that the proposed procedure may be efficient to detect and eliminate outliers from data with insufficient time resolution as exemplified in the Korean wind farms. After taking outliers out of the dataset, it is then necessary to fit the power curve with inlying data points for an effective operation of individual wind turbines by better representing their actual power output. Several parametric models widely employed in the literature, such as polynomial, Gompertz, Weibull, and logistic functions [3; 5], can be tested and compared for their fitness to the refined data points to derive the corresponding power curve. The overall procedure is demonstrated with the observations taken from WTs in a Korean wind farm site. The remainder of this study is organized as follows: First, a brief description of power curve is outlined and the behavior of outliers from actual observations is also discussed. Second, an overall procedure for power curve modeling is proposed by employing clustering-based machine learning algorithms and parametric curve fitting. The proposed procedure is then demonstrated with the data from a Korean wind farm site having the time resolution of 1 h. The concluding remarks follow in the last section.
# Natural Frequencies of a Tethered Satellite System
###### Abstract  
This study investigated the natural frequencies of a tethered satellite system to enhance stability and operational reliability. Tethered satellite systems provide many advantages for space missions but exhibit inherently complex dynamics due to the interaction between rigid-body motions and tether deformation. A dumbbell model was employed to analyze rigid-body dynamics, with eigenvalue analysis used to determine the natural frequencies of orbital and librational motion. Additionally, tether deformation was examined through simulations based on the absolute nodal coordinate formulation (ANCF) and a tensioned beam model, facilitating both analytical and computational assessments of transverse and longitudinal frequencies. The results show that orbital angular velocity and libration frequencies are highly sensitive to system parameters such as tether length, orbital radius, and satellite masses. Furthermore, the transverse and longitudinal natural frequencies of the tether exhibit distinct dependencies, providing critical insights for the design and control of tethered satellite systems. This work bridges a gap in understanding coupled dynamics and offers a systematic framework for calculating natural frequencies, supporting practical implementation in space missions.  
Keywords: tethered satellite system; rigid-body motion; tether deformation; natural frequencies; dumbbell model; tensioned _Article_
# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 2 Subpicture-Based Parallel Algorithms
Square-like tile partitions (\(2\times 4\) and \(4\times 2\)) have fewer CTUs affected than column-like (\(8\times 1\)) or row-like (\(1\times 8\)) tile layouts. The heterogeneous tile layout (\(4\times 2\) h) is affected in a similar way than the square-like tile layouts, but, obviously, this type of heterogeneous layouts should only be used in heterogeneous multiprocessor platforms, where the computing power differs between the available processors. Looking at the values in Table 2, we could think that the row-like layout may have a better R/D performance than the column-like layout, because the row-like layout has fewer CTUs affected by the absence of some neighbors for the prediction. However, we should take into consideration that the encoding performance decrease produced in each CTU depends on its position inside the slice or tile. In the intra prediction procedure, the neighbor CTUs used, if available, are the AR, the A, the AL, and the L neighbor CTUs (see Figure 6). Figures 6b,c show the available CTUs to perform the prediction. The number of available CTUs for the prediction procedure ranges from 0 to 4. As shown in Table 2, the number of CTUs affected in the row-like tile layout (\(1\times 8\)) is 210, whereas, for the column-like tile layout (\(8\times 1\)), this number rises up to 231. In the row-like tile layout (Figure 7a), most of the CTUs can perform the intra prediction using only 1 CTU neighbor (see \(O\) symbol in Figure 6b), whereas, in the column-like tile layout (Figure 7b), approximately half of the affected CTUs can use up to 3 neighboring CTUs (see \(\Omega\) symbol in Figure 6c). In Section 4, we will analyze the effect of tile layout partitioning in both R/D and parallel performance. Both slice and tile parallel algorithms include synchronization processes in such a way that only one process reads the next frame to be encoded and stores it in shared memory, which reduces  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Layout** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** & **Efficiency** \\ \hline \(1\times 8\) & 60 & 60 & 60 & 60 & 60 & 60 & 90 & 90 & 71\% \\ \(8\times 1\) & 51 &  
both drive disk accesses and memory requirements. Obviously, a synchronization process before writing or transmitting an encoded frame is also necessary. Therefore, both subpicture parallel approaches are designed for shared memory platforms, since all processes share both the original and the reconstructed frames.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 5 Outlook
**Acronyms**  
AR **Atmospheric River**  
AR4 **IPCC 4 th Assessment Report**  
AR5 **IPCC 5 th Assessment Report**  
ARC2 **Africa Rainfall Climatology 2.0**  
ASCAT **Advanced SCATerometer**  
CALIPSO **Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations**  
CAMELS-CL **Cathment Attributes and MEteorology for Large-sample Studies-Chile**  
CC **Clausius-Clapeyron temperature scaling**  
CCI **Climate Change Initiative**  
CDR **Climate Data Record**  
CGMS **Coordination Group for Meteorological Satellites**  
CHIRPS **Climate Hazards Center's Infrared Precipitation with Stations**  
CHIRTS **Climate Hazards Center Infrared Temperature with Stations**  
CIMR **Copernicus Imaging Microwave Radiometry**  
CMAP **CC Merged Analysis of Precipitation**  
CMIP5 **Coupled Model Intercomparison Project phase 5**  
CMORPH **CPC MORPHing technique**  
CPC **Climate Prediction Center**  
CPP **Cloud and Precipitation Process mission**  
DDWW **Dry regions to become Drier and Wet regions to become Wetter paradigm**  
DPFSCAT **Dual-Frequency Polarized SCATerometer**  
DNN **Deep Neural Networks**  
DOLCE **Derived Optimal Linear Combination Evapotranspirationpiration**  
DPR **Dual-frequency Precipitation Radar**  
EarthCARE **Earth Clouds, Aerosol and Radiation Explorer**  
EC **European Commission**  
ECE **Extreme Climatic Event**  
ECMWF **European Centre for Medium-range Weather Forecasts**  
ECV **Essential Climate Variable**  
EDO **European Drought Observatory**  
ENACTS **Enhancing National Climate Services**  
EPE **Extreme Precipitation Event**  
EPS-SG **EUMETSAT Polar System-Second Generation**  
ESA **European Space Agency**  
eTRaP **ensemble Tropical Rainfall Potential**  
EU **European Union**  
EUMETSAT **European Organization for the Exploitation of Meteorological Satellites**  
FEWS NET **Famine Early Warning System NETwork**  
FPIR **Fully-Polarized Interferometric synthetic aperture microwave Radiometer**  
GCOS **Global Climate Observing System**  
GEO **Geosynchronous Earth Orbit**  
GHCN **Global Historical Climatology Network**  
GLACE **Global Land-Atmosphere Climate Experiment**  
GLC **Global Landslide Catalog**  
GLDAS **Global Land Data Assimilation System**  
GLEAM **Global Land Evaporation Amsterdam Model**  
GMI **GPM Microwave Imager**
## 3 Conceptual Model Development  
### Conceptual Framework for Mobility Needs and Supply Options  
As shown in Figure 2, there were three conceptual elements when analyzing mobility in a certain region. These were the mobility needs or demands, the mobility supply or service options, and the related mobility environment.  
The first one was described by the population and the activities they performed. Moreover, the population was classified in different profiles or characteristics to quantify the mobility need. The mobility service suppliers depended on the alternatives provided and their characteristics as well as the interrelationships with other transport means as they could influence the demand by their mobility service choice. Finally, the factors of the related environment such as the economic situation or potential events such as pandemics or remote working influence mobility needs and the mobility options.
## 2 Materials and Methods
### Detecting River Pollution through Machine Learning
#### 2.4.3 Model Selection--Algorithm Selection for Multi-Pollutant Detection  
Given the diversity of pollution types and real-world complexities of the expansive Shatt al-Arab drone dataset, algorithm selection was critical for optimizing multi-class contaminant identification. Support Vector Machines (SVMs), K-Nearest Neighbors (k-NNs), and Random Forests (RFs) were chosen for the task of detecting river pollution in the study of the Shatt al-Arab River in Basra, Iraq, for multiple reasons. SVM excels at recognizing intricate patterns in complex data [21]. This is particularly crucial when identifying different types of river pollution, such as wastewater, oil spills, and debris.  
Another study [22] exhibited diverse and non-linear patterns. k-NN was used because it is highly suitable for spatial analysis. Pollution detection in a river often depends on the geographical proximity of data points. k-NN can effectively identify areas with similar pollution characteristics based on their spatial relationships [23]. RF was employed because it is known for its ability to handle large and noisy datasets. In this study, with over 1001 images and videos from aerial surveys, RF can provide reliable results by combining multiple decision trees to reduce overfitting and handle data noise effectively.  
All three models were chosen because they offer scalability and efficiency, crucial for processing a substantial amount of visual data from the aerial survey flights. They can efficiently handle large datasets, making them a practical choice. k-NN and RF were preferred for interpretability and visualization. They provide insights into the most influential features for pollution detection, helping researchers understand the causes of pollution and develop effective management strategies. The flexibility of parameter tuning for these models allows customization to the specific pollution detection task. Adjusting parameters in SVM, k-NN, and RF enables fine-tuning for optimal performance tailored to the dataset and research goals [24].  
In summary, SVM was chosen for its pattern recognition capabilities, k-NN for spatial analysis, and RF for its handling of large, noisy datasets. All three models were selected due to their scalability and efficiency in processing extensive visual data, while k-NN and RF were favored for their interpretability and customization through parameter tuning. These choices collectively provide a robust framework for identifying and addressing river pollution in the Shatt al-Arab River and its tributaries.
# Health IoT Threats: Survey of Risks and Vulnerabilities
## 4 Results
### Overview of IoT Security and Privacy in Healthcare
#### 4.1.2 The Network Layer
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Issue or Attack** & **Explanation and Highlight** & **Countermeasure** \\ \hline \multirow{8}{*}{DOS Attacks} & This type of attack involves & \multirow{2}{*}{Implementing network security} \\  & overwhelming a network with a & \\  & large amount of traffic, which can & \\  & result in the disruption of critical & \\  & services, such as electronic medical & \\  & records systems or medical device & \\  & networks [121]. & \\ \hline \multirow{8}{*}{IP Spoofing} & This type of attack involves & \multirow{2}{*}{Using strong authentication and encryption techniques, such as IPsec, to secure data transmission and prevent IP spoofing [127; 128].} \\  & This type of attack involves & \\  & forging ARP (Address Resolution & \\  & Protocol) messages on a network, & \\  & which can be used to redirect & \\  & network traffic and launch & \\  & man-in-the-middle attacks, such as & \\  & intercepting sensitive information & \\  & transmitted between medical & \\  & devices and servers [129]. & \\ \hline \multirow{8}{*}{DHCP Spoofing} & This type of attack involves & \multirow{2}{*}{Implementing DHCP spoofing protection measures, such as static IP assignment, to prevent unauthorized IP assignments [130; 131].} \\  & This type of attack involves & \\  & on a network, which can be used to & \\  & assign unauthorized IP addresses & \\  & to IoT devices and launch & \\  & man-in-the-middle attacks, such as & \\  & intercepting sensitive information & \\  & transmitted between medical & \\  & devices and servers [130; 131]. & \\ \hline \multirow{8}{*}{Sniffing Jamming} & This type of attack involves & \multirow{2}{*}{Using encryption techniques, such as SSL/TLS, to secure data transmission and prevent sniffing attacks [104; 132].} \\  & This type of attack involves & \\  & injecting malicious packets into a & \\  & network, which can be used to & \\  & launch attacks or disrupt services, & \\  & such as electronic medical records & \\  & systems or medical device & \\  & networks [104]. & \\ \hline \multirow{8}{*}{P Packet Injection} & This type of attack involves & \multirow{2}{*}{Implementing network security} \\  & infecting devices with malicious & \\  & software that can steal sensitive & \\  & information or disrupt device & \\  & operation [120]. In 2019, the & \\  & malware known as Ryuk was & \\  & found to be infecting hospital & \\  & systems, leading to widespread & \\  & disruption and ransom & \\  & demands (A.). & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Security and Privacy Challenges of the IoT Network Layer.
# A Two-Dimensional Non-Destructive Beam Monitoring Detector for Ion Beams
## 3 Experimental Results
### Radiation Tolerance Study  
A long-term irradiation was performed to study any radiation-induced degradation of the \(\pi^{2}\) response. The beam line setup was identical to the one used to study the linearity of the output signal (Figure 4a), i.e., the \(\pi^{2}\) was installed after the \((1\times 1)\) cm\({}^{2}\) square collimator, and upstream of the Faraday Cup. Images and current measurements were taken simultaneously at regular time intervals for the whole irradiation time. Figure 12, top, shows the instantaneous proton flux \(\phi\) (in units of cm\({}^{-2}\cdot\) s\({}^{-1}\)) measured by both the Faraday Cup and the \(\pi^{2}\) as a function of the integrated proton flux \(\Phi\) (in units of cm\({}^{-2}\)). The instantaneous proton flux from the Faraday Cup was calculated as the measured current (converted to number of charges per second) divided by the collimator aperture area. The proton flux from the \(\pi^{2}\) was obtained by converting the blue pixel value using the fit to the transfer function in Figure 6. The integrated proton flux \(\Phi\) was calculated from the proton flux measured by the Faraday Cup, as \(\Phi[n]=\sum_{i=0}^{n-1}\Phi[i]\cdot\Lambda t[i]\), where \(\Delta t\)[i] is the vector of time intervals between consecutive measurements. Figure 12, bottom, shows the ratio between the two flux measurements. At the start of the test, the proton flux was set as low as possible to observe changes in the response for low \(\Phi\) values. The proton flux was later increased in subsequent steps to reach as high a value of \(\Phi\) as possible. As seen in the bottom plot, the ratio between both measurements remained stable within the experimental uncertainty for the whole duration of the test, up to \(\Phi=3\cdot 10^{15}\) cm\({}^{-2}\), equivalent to 10.6 MGy Total Ionising Dose on silicon, thus showing no sign of any radiation-induced degradation in light yield.  
Figure 11: TRIM simulation of the beam scattering due to the \(\pi^{2}\) foil on a target placed 60 cm downstream the instrument. On the left, the trajectory of the point-like beam when the foil is not in beam. On the right, the scattering when the foil is in beam can be observed.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 4 Results and Discussion  
### GMDH Simulation  
In this section, the GMDH model was implemented for evaluating the slope deformation of earth dams. Of the data sets collected in this study, about 80% of the data were used to design and train models randomly. Different models were designed to obtain the least error model. Finally, the remaining data (20% of the total data) were used to evaluate the power of the models [87; 88; 89]. The code for the GMDH model was implemented in MATLAB software. According to the description given in the previous section, the GMDH model is designed by various components that control system performance  
In addition, models with higher neurons show less accuracy than this model; thus, it can be indicated that the model with four neurons is the optimal one.  
Based on the analysis of the previous step, in this step, the effect of the number of layers applied in the GMDH model is investigated. The difference between the number of layers of this model compared to other intelligent models is that the number of layers can be more than one depending on the problem. However, most artificial intelligence works have recommended a layer for solving most linear and nonlinear problems. The GMDH model, with more layers, can also provide convergence conditions to specific conditions. Hence, five layers (2,4,6,8,10) were evaluated. The results for the different sections are presented in Figure 6. As can be seen, the effect of the number of layers on the results is different. The best performance is the six-layer model, of which its results can be introduced as an optimized model.  
Figure 5: Impact of neuron number on the performance of GMDH models.  
Finally, the third important factor that influences the GMDH method is investigated by parametric analysis. Selection pressure is a criterion that allows the data of each layer to know what number of the previous section data can be used for continuing analysis. This criterion actually selects the number of data that best fits each step and sets it as the next layer input, repeating it until the final steps, so that the system achieves the lowest error and highest accuracy. In this case, one should choose the model that offers the best runtime and accuracy. The results of this study are shown in Figure 7. As can be shown, the best value for selection pressure is 40%. This also indicates the high accuracy of the model that was able to predict the data well. Finally, the final model for predicting the slope deformation of earth dams using the GMDH method is introduced at this stage. The best model was able to provide a system with R\({}^{2}\) = 0.8489 and 0.8458 accuracy for the training and testing sections, respectively. In the next section, the new MARS model is implemented for this problem.  
Figure 6: Impacts of the layer number on the performance of GMDH models.  
Figure 7: Impacts of various selection pressure on the performance of GMDH models.
## 4 Removal of Pharmaceuticals during Wastewater Treatment
### Sorption Process for the Removal of Pharmaceutical Products
#### 4.4.2 Clays  
However, clay minerals could be inexpensive and widely available materials for the removal of pharmaceuticals [82]. For clay materials, the most common types (illite, kaolinite, vermiculite, montmo-rillonite, bentonite, and sepiolite) have already been used for the removal of a variety of organic micro-pollutants. Montmorillonite is the most promising adsorbent for further investigations aimed at testing the practicability of a clay-based adsorbent for the removal of pharmaceuticals compounds [82]. The use of natural clay (montmorillonite (Mt), vermiculite (VER), bentonite (B), kaolinite (K)) and modified clay-based sorbents commercial acid-activated montmorillonitees K10 and K30, and two carbonaceous-mineral nanocomposites (MtG5%, BAlG3%C) for the removal of ibuprofen, diclofenac, ketoprofen, carbamazepine, bisphenol A, and triclosan, showed that vermiculite was the best adsorbent for the removal of all drugs. For the modified clay minerals, the best results were achieved for carbonaceous bentonite, and the two acids activated montmorillonites K10 and K30 [83]. Furthermore, the adsorption of gemfibrozil, metfenamic acid, and NPX using two different clays materials, exfoliated vermiculite and LECA, showed that both adsorbents were able to remove drugs from the aqueous medium, while vermiculite presented a high adsorption capacity up to 70% [84].
## 3 Dataset Description
### HFAVD Dataset Structure
#### 3.3.5 Head Pose  
The determination of the head pose, characterized by Euler angles (roll, pitch, and yaw), was carried out by using the methodology described in [44]. The approach involved the following steps: (a) Face detection using YOLO Tiny: Initially, the YOLO Tiny framework was utilized to detect the face in the input data. This allowed for the identification of the Region of Interest. (b) Three-dimensional face reconstruction for landmark alignment: Subsequently, a 3D face reconstruction technique was applied to align the facial landmarks. This process ensured accurate landmark detection even for those not directly visible to the camera. (c) Landmark analysis for Euler angle calculation: Once the facial landmarks were detected, the transitions and rotations between landmarks across successive frames were analyzed. This analysis enabled the calculation of the Euler angles, providing information about the head pose. The limitation of the article is that the model proposed can only estimate head angles up to a maximum value of 70 degrees. This limitation restricts its applicability in scenarios where larger head angles need to be accurately detected. Additionally, the model's performance is slower compared with the Dlib detector, which itself loses accuracy when the head angle exceeds 35 degrees. This slower performance could hinder real-time applications that require fast and accurate head angle estimation.
## 5 Innovative Approaches for Enzyme Immobilization
### Nanomaterial-Based Enzyme immobilization  
Nanotechnology has revolutionized chemistry, medicine, and pharmaceuticals by enabling advanced enzyme immobilization. Nanomaterials are ideal immobilization substrates because their high surface area maximizes enzyme loading, while their smaller size enhances efficacy by increasing binding sites, improving diffusion, and allowing functional customization [7; 17]. Unlike bulk porous materials, nanomaterials minimize mass transfer resistance by shortening the pore pathway [105] and providing superior chemical/thermal stability, biocompatibility, and higher kinetic efficiency [106].  
However challenges remain, including complex synthesis, stability, and specificity challenges [31], along with environmental concerns from toxic production chemicals [107]. Diverse nanostructures, such as particles, wires, tubes, sheets, and fibers, are employed, including carbon-based (graphene, carbon nanotubes), metals (titanium, gold, magnetite), and oxides (silica and alumina) [108]. Recent advances explore polymers, metal-organic frameworks, magnetic nanoparticles, and hybrid materials.