esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 3 Model and Control of the DFIG-Based WTG
### Wind Turbine Model  
The mechanical power captured from wind--which is a nonlinear function of the wind speed condition (\(v_{w}\)), blade profile, etc.--is defined as below:  
\[P_{m}=0.5\rho A\v_{w}^{3}c_{P}(\lambda,\beta), \tag{3}\]  
where \(\rho\) means the air density, \(A\) means the swept area of the wind turbine, \(\lambda\) indicates the tip-speed ratio, \(c_{p}\) represents the power coefficient, and \(\beta\) indicates the pitch angle.  
As in [22], \(c_{p}\) in (3) is represented as:  
\[c_{P}(\lambda,\beta)=0.645\bigg{\{}0.00912\lambda+\frac{-5-0.4(2.5+\beta)+116 \lambda_{i}}{e^{21\lambda_{i}}}\bigg{\}}, \tag{4}\]  
where  
\[\lambda_{i}=\frac{1}{\lambda+0.08(2.5+\beta)}-\frac{0.035}{1+\left(2.5+\beta \right)^{3}}, \tag{5}\]  
and \(\lambda\) is given as:  
\[\lambda=\frac{\omega_{r}R}{v_{w}}, \tag{6}\]
# Recent Developments in the Field of the Metal-Insulator Transition in Two Dimensions
## 3 Influence of the Magnetic Field Parallel to the 2D Plane  
The magnetoresistance \(\rho(B_{\parallel})\) in magnetic fields \(B_{\parallel}\) parallel to the 2D plane is strong and positive [19; 20; 21] (examples are shown in Figure 2), but eventually saturates above some density-dependent magnetic field \(B_{\parallel}>B^{*}\). In sufficiently-thin quantum wells, \(B_{\parallel}\) has little effect on the orbital motion of the electrons and couples only to the electron spin; therefore, it is reasonable to attribute the saturation of the magnetoresistance to the complete spin polarization of the electron system [22]. Indeed, from the analysis of the positions of Shubnikov-de Haas oscillations in tilted magnetic fields [23; 24], it was concluded that the magnetic field \(B^{*}\) is equal to that required to polarize the electrons' spins fully.  
Another effect of the parallel magnetic field is that it wipes out the strong metallic temperature dependence of the resistance at electron densities just above \(n_{\rm c}\) and suppresses the metallic regime [7; 17; 19; 25; 26; 27]. In Figure 3, the temperature dependences of the resistivity in zero magnetic field (Figure 3a) are compared with those measured in a parallel magnetic field, \(B_{\parallel}=4\) Tesla, high enough to cause full spin polarization (Figure 3b). The dashed (middle) curves on both panels correspond to the critical electron densities, \(n_{\rm c}^{*}\), determined by the method of vanishing activation energy and vanishing nonlinearity of the current-voltage characteristics outlined above. The middle curve in Figure 3a, corresponding to \(n_{\rm c}^{*}=n_{\rm c}\), is flat below 1 K. In sharp contrast with the \(B=0\) situation, at \(B_{\parallel}>B^{*}\), not only are the \(\rho(T)\) curves non-symmetric about the middle curve corresponding to \(n_{\rm c}^{*}\), but all of them have negative \"insulating-like\" derivatives \({\rm d}\rho/{\rm d}T<0\) in the entire temperature range, although the values of the resistivity are comparable to those in the \(B=0\) case. Moreover, in a strong parallel magnetic field, there is no temperature-independent \(\rho(T)\) curve at any electron  
Figure 1: Resistivity as a function of temperature in a Si MOSFET **(a)**, \(p\)-type GaAs/AlGaAs heterostructure **(b)**, and SiGe/Si/SiGe quantum well **(c)**. Figures adapted from [15], [16], and [8], respectively.  
density [17]. Concerning the suppression of the metallic regime, it has been shown that the parallel magnetic field-induced increase in the critical electron density \(n_{\rm c}^{*}\) by the factor of about 1.4, observed in a strongly-interacting two-dimensional electron system, is caused by the effects of exchange and correlations [26].
# Dispensing of Prescribed Medicines in Swiss Community Pharmacies-Observed Counselling Activities
## 3 Results
### Counselling  
The PEs lasted on average 4.5 \(\pm\) 3.0 min (first 5.2 \(\pm\) 3.1; refill 3.9 \(\pm\) 2.7, \(p\) < 0.001), ranging from 1.0 to 23.0 min. In 106 PEs (19.1%), pharmacy staff offered counselling by asking if the patient already knew about their medicines, or if they had any questions regarding the use of medicines (general closed questions that were intended to verify patient need for counselling). During the 556 PEs, counselling was provided to 367 (66.0%) customers, to 249 with first prescriptions and to 118 with refill prescriptions (_p_ < 0.001). Of the 367 customers, 68 (12.2%) received counselling on one theme (out of the 34 counselling themes), 52 (9.4%) on two themes, 132 (36.0%) on three to five themes, and 115 (20.7%) on five to thirteen themes (Figure 1). Pharmacy staff did not provide any counselling in 169 refill PEs and in 20 first PEs. On average, customers were counselled on 2.9 \(\pm\) 3.1 themes per PE (first PEs 4.9 \(\pm\) 3.0; refill PEs 1.0 \(\pm\) 1.7, \(p\) < 0.001). Customers who refused counselling (148 PEs [26.6%]; 51 first PEs vs. 97 refill PEs, \(p\) < 0.001) were significantly more often approached for counselling at first PEs than refill PEs (3.7 \(\pm\) 2.9 theme vs. 1.7 \(\pm\) 1.9, \(p\) < 0.001).  
Table 2 presents the number of the counselling themes and their initiator. Pharmacy staff mainly counselled on administration (at first PEs 465 times and at refill PEs 73 times), dose (188; 46), and use (152; 36) and provided a label (189; 55). Of the 34 counselling themes, 8 were never addressed.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Prescription Encounter**} & **All** & **First** & **Refill** \\ \cline{2-4}  & **(n = 556)** & **(n = 269)** & **(n = 287)** \\ \hline
**Patient** & & & \\ Female n (\%) & 337 (60.6) & 162 (60.2) & 175 (61.0) \\ Mean age (years) \(\pm\) SD & 53.8 \(\pm\) 23.4 & 45.6 \(\pm\) 23.9 & 61.4 \(\pm\) 20.2 \\ Regular customer n (\%) & 523 (94.1) & 242 (90.0) & 281 (97.9) \\ Carer filled a prescription for a patient n (\%) & 105 (18.9) & 62 (23.0) & 43 (15.0) \\
**Prescription** & & & \\ Ambulatory n (\%) & 468 (84.2) & 212 (78.8) & 256 (89.2) \\ Hospital discharge n (\%) & 88 (15.8) & 57 (21.2) & 31 (10.8) \\
**Provider of counselling \({}^{*}\)** & & & \\ Pharmacist n (\%) & 149 (26.8) & 70 (26.0) & 79 (27.5) \\ Pharmacy technician n (\%) & 267 (48.0) & 124 (46.1) & 143 (49.8) \\ Aprentice n (\%) & 86 (15.5) & 45 (16.7) & 41 (14.3) \\ Pharmacist in training n (\%) & 13 (2.3) & 8 (3.0) & 5 (1.7) \\ Druggist n (\%) & 8 (1.4) & 1 (0.4) & 7 (2.4) \\ Combination of pharmacy staff n (\%) & 33 (5.9) & 21 (7.8) & 12 (4.2) \\ \hline \hline \end{tabular} \({}^{*}\) Definition of the different counselling providers in Switzerland: Apprentice is a pharmacy technician in their 3-year training; pharmacist in training is a student in her/his last year of the master in pharmacy curriculum; druggist accomplished a 4-year apprenticeship.  
\end{table}
Table 1: Patient, prescription, and provider characteristics.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Focus on Green Networking  
With the exponential growth of internet users worldwide, evolving smart cities with an increase of wireless sensor networks and internet-of-things devices, the energy consumption by ICT equipment is rapidly increasing, leading to increased carbon dioxide (CO\({}_{2}\)) emission. The volume of CO\({}_{2}\) emission produced by the ICT sector is estimated to be at least 2% of the world's total carbon footprint in 2020 [20]. The ICT sector is forecasted to contribute 1.3 Gt of greenhouse gas emissions in 2020 [21]. Global warming and the effects of climate change are more serious in the context of Nepal because it isa mountainous country. The rapidly retreating glaciers (average retreat rate of more than 30 M/year), erratic rainfall and frequent flooding, and severe drought leads to alarming situations for a developing country such as Nepal [22]. With the increasing network connectivity to rural Nepal covering the hilly areas, the effect of global warming will be alarming. With the increasing ICT infrastructure and users, the sector consumes more energy leading to a higher volume of CO\({}_{2}\) emission, and hence this scenario is expected to increase rapidly if no mitigation approaches via greening techniques are adopted. Greening of networks such as the use of renewable energy technologies to power the network equipment and the use of the latest energy efficient networking technologies like software defined networking (SDN) enabled IPv6 networks while deploying the connection would help to create sustainable societies, as well as to provide support to service providers' sustainability. Significant amounts of energy savings in an ISP's network would help to reduce the annual energy bill as a part of organizational OpEX [23]. Considering the greening approach by newer networking technologies like SDN and IPv6, we can find that there are several studies that contribute to green ICT with an energy aware routing scheme for energy saving and carbon footprint reduction to show that IPv6 and SDN have greater flexibility towards green networking [24; 25; 26; 27; 20].
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 1 Introduction
### Organization and Notations  
The rest of the article is structured as follows. Section 2 explains the system model and the related assumptions for the mathematical formulation of the optimization problem. CI-based power allocation schemes to solve the problem at hand are discussed in Section 3. Section 4 presents the simulation results, while Section 5 concludes the article. For convenience, a list of notations is provided in Table 2.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 3 Motivation, Background, and VDB Assessment
### Terminologies and Theoretical Foundations
#### 3.2.1 Cyber Fundamentals
As shown in Figure 3, any organization, regardless of size, could be able to possess one or more potential vulnerabilities in their assets that might be exploited by a threat and launch a potential attack. The exploitation of vulnerabilities may turn into a major risk assessed based on their impact and occurrence. The organization rates the risks and vulnerabilities' severity by using a risk assessment in a context-aware manner. It then elaborates a mitigation plan to reduce the risk impact regarding the CIA by implementing the necessary security measures. This step follows a risk management process, as shown below in Figure 4. In addition, residual risks may remain even after applying the necessary safety measures. This fact implies a continuous process of control and supervision to prevent further impacts [26, 27]. The result should be communicated for tracking and making timely decisions.
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 2 The Method Extension
The innovative element is in using RF test points, simple COTS connectors, not only to guarantee the functional performance of the receiver, but also to enhance the reliability of a device intended to be used in very severe environments. The method extends and improves the analytic approach reported in the conference paper [17] since it allows to complete the system model so that the results' accuracy is improved.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 2 Related Work
### Video Deblurring  
Video deblurring research initially focused on traditional image processing methods. [PERSON] et al. [33] proposed utilizing motion compensation between adjacent frames for deblurring by identifying and restoring sharp image patches. [PERSON] et al. [34] introduced a registration-based deblurring algorithm through patch-based motion trajectory modeling. [PERSON] et al. [35] designed a joint optimization framework for simultaneous motion estimation and sharpening, achieving good results in dynamic scenes.  
The rise of deep learning brought revolutionary changes to this field. [PERSON] et al. [36] first applied CNNs to video deblurring, implementing blur removal based on inter-frame information through a deep encoder-decoder network. [PERSON] et al. [37] further proposed a spatio-temporal CNN architecture, enhancing multi-frame information fusion capabilities. The subsequent STRCNN [38] and LSTM-based ESTRNN [39] effectively utilized long-term temporal dependencies in video sequences through recursive learning. [PERSON] et al. [40] further enhanced feature extraction through bidirectional propagation of features at different scales. The introduction of attention mechanisms marked a significant breakthrough in the field. [PERSON] et al. [41] proposed a spatially attentive patch-hierarchical network that employed spatial attention mechanisms to significantly improve detail restoration capabilities. ARVo [42] further optimized performance by constructing an all-range volumetric pyramid that learns spatial correspondences between blurred video frames. With the rise of transformers in computer vision, VRT [13] and VDTR [11] successfully introduced transformer architectures to video deblurring tasks, achieving more effective long-range dependency modeling. FGST [12] further explored the application of transformer architectures in video deblurring through flow-guided sparse attention mechanisms, achieving efficient feature alignment and aggregation.
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 2 Semianalytical Approach for the Determination of Modulus and Damping
### Numerical Solutions
#### 2.2.3 Numerical Analysis  
Several simulations were performed varying the input parameters describing the nonlinearity: \(\delta_{S},\gamma_{S}\) and \(\delta_{\lambda}\). For each simulation, the excitation source (simulating a PZT transducer) was chosen as a monochromatic forcing applied in \(x=0\):  
\[F(0,t)=B_{i}\ cos(\omega t) \tag{16}\]  
with varying amplitude \(B_{i}\) and frequency \(\omega\). Frequency was chosen close to the first linear resonance mode: \(\omega=25.82\) kHz. The corresponding output signals \(u_{i}(t)\) were recorded in \(x=L\), as required by the proposed approach and as done in experiments (using a PZT receiving transducer or laser vibrometer). To simulate noise, a white noise \(n_{i}(t)\) was added to the detected signals, with amplitude corresponding to 5% of the amplitude of the signal detected at the lowest amplitude of excitation (\(u_{1}(t)\)), corresponding to the order of magnitude of the signal-to-noise ratio usually observed in experiments.  
The numerical results were treated using the Modulus and Damping Nonlinearities Evaluation method (MoDaNE), previously discussed in this section, in order to derive the dependence of attenuation and velocity on amplitude from the recorded signals. To validate the approach, our aim was:  
* to verify the capability of the approach to separate nonlinearity in \(\alpha\) and \(c\), comparing Cases 1, 2 and 3;
* validate the possibility to reproduce qualitatively the expected functional dependences (quadratic or quartic plus quadratic) of \(c\) and \(\alpha\) on strain: comparison of Cases 3 and 4;
* verify that the approach allows to detect the effects of \(S/\lambda\) nonlinearities on \(c/\alpha\) nonlinearities: Cases 3 and C; and
* verify that, at least in some circumstances as shown below, the approach allows quantifying correctly the nonlinear parameters \(\delta_{c},\gamma_{c}\) and \(\delta_{\alpha}^{\prime}\): Cases 3, 5 and 6.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 5 Experiments and Results
### Data PreProcessing and Splitting  
The collected dataset [5] was preprocessed into Pandas DataFrame [35], addressing infinite and missing values, removing duplicates, converting categorical labels to numerical formats, and normalizing features. Features with zero variance were excluded, reducing the attribute count from 77 to 52, ensuring a suitable dataset for model training.  
To address class imbalances and reduce biases, we applied the standard SMOTE, standardizing each attack subclass to match the sample size of the normal class, as shown in Figure 5(a). This balancing improved the model's ability to generalize and enhanced the classification accuracy. The problem was reformulated as a binary classification task to distinguish between malicious (attack) and non-malicious (normal) network traffic, aiming to identify attacks accurately. Figure 5(b) shows the balanced distribution of the two classes--normal (0) and attack (1)--improving the model's reliability in real-world network security applications.  
Given the size of the dataset, we randomly selected 4000 records from each class (normal and attack traffic) for model evaluation. This subset enables us to effectively apply and evaluate XAI techniques while maintaining computational efficiency, aligning with our primary objective of developing interpretable and transparent models to enhance network security, rather than focusing solely on maximizing model performance across the full dataset. Table 1 presents the sample distribution before and after applying SMOTE, which was used to balance the dataset. Achieving this balance is essential for accurate threat detection and consistent model performance in realistic 6G network scenarios.  
Figure 6: Visualization of class categories and their counts after using SMOTE.
# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
###### Abstract  
This paper addresses the critical challenge of optimal allocation and scheduling of Internet of Things (IoT) tracking devices for patient monitoring in healthcare facilities, where limited resources must be efficiently distributed to minimize cost and maximize care quality. We formulate this healthcare management problem as a specialized variant of the Resource-Constrained Scheduling Problem that incorporates patient-specific factors such as duration of stay and priority. After establishing the computational complexity of the problem, we propose a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to address the complex problem of balancing multiple objectives: cost minimization and quality of care maximization. Our approach offers a set of optimal trade-offs, enabling informed decision-making to select the best final solution. Computational experiments conducted on both simulated scenarios and real-world healthcare facility datasets demonstrate that our approach outperforms existing methods, achieving between 1.2 and 3.0 times more solutions than the state of the art. Moreover, in comparison to manual scheduling by medical center managers, our method achieves cost savings of up to 12% (with an average of 6.3%) and quality improvements of up to 20% (with an average of 10%) across the tested experiments. The proposed method scales effectively to realistic healthcare settings with varying numbers of patients and tracking devices, maintaining solution quality while keeping computational time within practical limits for daily operational use. Our findings contribute to both healthcare operations research and clinical practice by providing an efficient methodology for optimizing the use of limited monitoring resources while prioritizing patient safety.  
a Article  
1
## 3 Results and Discussion
Furthermore, the study in [27] introduces a digital solution, D-SORM, for e-health remote monitoring, focusing on arm rehabilitation and human activity recognition through wearable sensors and deep learning techniques. This system has significant applications in telerehabilitation and elderly care, with the potential to improve healthcare outcomes for individuals with mobility impairments. An exhaustive literature search yielded many studies that provide a strong background for integrated technology proposals. The most outstanding advances are described as follows. SeisMote is a wireless system designed for the monitoring of cardiovascular function through sensorized nodes that enable ECG, acceleration, rotational velocity, and PPG measurement. It operates in three modes: real-time mode, offline mode, and Bluetooth real-time mode [28]. Meanwhile, a smart mattress system can measure patients' respiration and heart rate by detecting slight body movements, with a sensor that can distinguish between different sleep states and measure the respiration and heart rate in various positions and postures [29]. An affordable and precise fall detection system has been proposed, using a 24 GHz continuous-wave Doppler radar, chosen for its ability to track human movements effectively, penetrate obstacles, and function regardless of the lighting conditions to enable the monitoring of indoor human activities and identify falls [30]. Moreover, DeFall is an environment-independent indoor fall detection system that uses WiFi signals to detect falls based on distinctive speed and acceleration patterns, achieving high accuracy without prior training in new environments [31]. A pioneering telehealthcare system has also been designed to monitor the psychoneurological conditions of elderly and disabled individuals. The system combines Kinect sensors with ECG, EEG, EMG, and PPG sensors for the comprehensive  
Figure 4: Distribution of studies by country of origin in the Scoping Review. monitoring of brain activity, body movement, and physiological signals [25]. A novel health detection system utilizing millimeter-wave radar technology integrates non-contact and contact mechanisms. Alongside monitoring vital signs like breathing and heartbeats, the system gathers essential data, including human movement, blood oxygen levels, and body temperature, through diverse IoT node devices [32]. Another group has introduced a cyber-physical system-based home robotic system combined with artificial intelligence (AI), intelligent wearable sensors, and remote sensing. It was implemented using a microcontroller board and sensing modules to measure patients' SPO2, heart rate, and body temperature, with features like fall detection and GPS location detection [33].
# On End-to-End Intelligent Automation of 6G Networks
## 4 6G End-to-End Network Automation Challenges
### Multi-Stakeholder and Multi-Tenant Orchestration  
The third challenge facing 6G network automation and orchestration is the multi-stakeholder and multi-tenant orchestration. This stems from the fact that operators want to have a generic and open platform that combines all the assets and capabilities, including existing ones as well as new ones introduced by 6G networks. This is because large-scale service providers (such as Netflix and IBM) are adopting a \"design-once-deploy-everywhere\" approach [94]. This allows them to have their assets and capabilities consistently available across networks and technologies. As a result, new initiatives, such as the Open Platform Group (OPG) initiative launched by the GSMA alliance, have emerged to support this concept [95; 96]. These lead to new services and value chains being created by the collaboration and cooperation of different stakeholders (e.g., communication service providers, over-the-top service providers, local and global cloud providers, private network operators, etc.). However, this brings a major concern in terms of orchestrating this cross-stakeholder collaboration. This is because the orchestration process has a direct impact on the non-etization and federation opportunities of these stakeholders' capabilities. Moreover, 6G orchestration frameworks are expected to provide automated, secure, and trustworthy coordination and access to the stakeholders' capabilities and available resources. This includes resources available at the different portions of the network (i.e., cloud, edge, and extreme edge).  
In addition to the multi-stakeholder notion, another related challenge is multi-tenant orchestration [97; 98]. More specifically, ensuring that different network slices as well as different virtual network operators are logically separated is a challenging task. This is due to the fact that this separation between the different tenants and slices needs to be guaranteed. This can only be done through the continuous and effective monitoring and allocation of the available resources [98].