esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Pull Force  
As can be seen from Figure 7, the strength of the Sn50 Pb49 Sb1/Cu solder joint attenuated with the increasing irradiation dose. After being irradiated for 960 h, the pull force of the solder joint decreased by 22%. Compared with the preliminary stage of irradiation, the graph reveals a downward trend in the reduction rate of the pull force under a heavy dose of irradiation. The formation and expansion of micro-voids and micro-cracks induced by \(\gamma\)-ray irradiation, which are the generation source of cracks, increased the fracture risk of the joint with outer stress, and hence reduced the strength of the solder joint. The increasing accumulation of irradiation dose amassed displaced atoms and lattice vacancies in the Pb-based solid solution and both types of point defects were supersaturated after long-term \(\gamma\)-ray irradiation. This may increase the formation difficulty of new micro-defects due to the rising inner stress and, therefore, lead to a slowdown in strength decrement after a heavy dose of irradiation, as shown in Figure 7. However, further research on the atomic scale is necessary to verify this discussion.
# An Access Control Model for Preventing Virtual Machine Hopping Attack
## 3 Preliminaries
### Access Control  
Access control consists of three entities: The subject (sending the access request), the object (being accessed), and the security access policy (the access rules of the subject accessing the object).  
Traditional access control has three access policies: (1) Discretionary Access Control [20] (DAC), which allows a subject to impose specific restrictions on access control. (2) Mandatory Access Control [21] (MAC), which does not allow subject interference to some extent. (3) Role-based access control policy [22] (RBAC), which assigns access rights according to user roles. With the rapid development of cloud computing, mobile computing, and other application scenarios, there is also an attribute-based access control [23] (ABAC).
## 7 Conclusions  
Our study sought to investigate the N concentration of grass species across the landscape; the study was limited in that the grass samples were not collected monthly and averaged. Furthermore, this study shows the efficiency of red-edge-based vegetation indices derived from Sentinel-2 MSI. These bands from satellites with a coarser resolution are-selfdom computed. Hence, grass N was better predicted by combining remote sensing data from the multispectral Sentinel-2 and red-edge-based vegetation indices (\(R^{2}\) = 0.85). The S2 MSI remains the ideal remote sensing tool for estimating foliar N as it incorporates the strategically positioned red-edge bands. Furthermore, red-edge-based vegetation indices have been reported to provide better estimates in other studies compared to traditional vegetation indices. The parametrization and optimization of remote sensing is essential for improving estimates of the biochemical and biophysical characteristics of vegetation. In mountainous areas, the inclusion of topographic data could be a boon for future research. Our research result can be used to estimate other vegetation types such as trees, shrubs, and crops, with high accuracy through optimization of input remote sensing variables.  
Conceptualization, S.A. and A.R.; Methodology, K.M.; Formal analysis, K.M.; Writing--original draft, K.M.; Writing--review & editing, S.A. and A.R.; Supervision, S.A. and A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Due to privacy restrictions, data will be unavailable at the moment.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Antibacterial Properties
These results highlight M4 as a promising candidate for further investigation, while M2 and M3 showed lower antimicrobial potential. The presence of silver colloids in hydrophilic membranes is crucial for combatting bacterial agents; however, the integration of AgNPs into these membranes also plays a significant role, as shown in Figure 11. Samples M3 and M4, which were prepared using post-electrospinning to integrate AgNPs, demonstrated the most effective antibacterial properties. Interestingly, even the pure PLA exhibited strong antibacterial characteristics owing to the solution casting process, which enhanced the surface hydrophilicity and improved the integration of AgNPs into the PLA matrix. Sample M4, featuring hydrophilic PLA with post-electrospinning modification, outper \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Samples**} & \multicolumn{4}{c}{**Inhibition Zone (mm) \(\pm\) SD, n = 3**} \\ \cline{2-6}  & **B.S** & **S.P** & **S.A** & **S.M** & **K.P** \\ \hline M1 & 6.63 \(\pm\) 0.80 & 6.57 \(\pm\) 1.02 & 6.83 \(\pm\) 1.16 & 6.69 \(\pm\) 1.43 & 6.75 \(\pm\) 0.84 \\ M2 & 13.25 \(\pm\) 0.85 & 12.26 \(\pm\) 0.44 & 13.51 \(\pm\) 1.74 & 12.49 \(\pm\) 0.91 & 10.36 \(\pm\) 1.55 \\ M3 & 16.91 \(\pm\) 2.02 & 16.83 \(\pm\) 0.70 & 18.54 \(\pm\) 1.77 & 17.99 \(\pm\) 1.10 & 19.22 \(\pm\) 1.51 \\ M4 & 19.30 \(\pm\) 2.63 & 20.11 \(\pm\) 2.02 & 20.09 \(\pm\) 2.10 & 19.09 \(\pm\) 2.95 & 16.86 \(\pm\) 2.04 \\ Kanamycin & 22.80 \(\pm\) 0.93 & 24.04 \(\pm\) 1.47 & 25.42 \(\pm\) 1.33 & 24.40 \(\pm\) 0.93 & 25.81 \(\pm\) 1.23 \\ Blank & 7.06 \(\pm\) 0.46 & 6.96 \(\pm\) 0.27 & 7.61 \(\pm\) 0.35 & 6.88 \(\pm\) 0.42 & 6.60 \(\pm\) 0.30 \\ \hline \end{tabular}
\end{table} 
Table 2: Antibacterial activity (inhibition zone) values of hydrophilic and hydrophobic electrospun NF fabrics against Gram-positive and Gram-negative bacterial strains. AgNPs were incorporated through pre-electrospinning. These findings suggest that the post-electrospinning method of AgNP incorporation is advantageous for enhancing the antibacterial properties of the nanofibrous membranes. Figure 10: (**A**) Antibacterial activity of the prepared electrospun nanofibrous fabric samples (zone of inhibition), (**B**) % inhibition by OD600 method, and (**C**) MIC values of samples M1–M4 and standard kanamycin.
## 2 Methodology
### Integration of PS/SDF/DS Based on SNR  
For joint treatment of the above CPs, the SNR of each pixel was calculated by time coherence coefficient to obtain the weighted average phase value of coincident coherence point targets. Specifically, temporal coherence (\(\gamma\)) can be used to reflect the interferometric phase noises and phase stability of a pixel. SNR is a universal evaluation index to describe noise and phase stability of interferometric phases of pixels. Therefore, the PS points, SDF points, and DS points with relatively low SNR were removed according to the SNR of pixels. The phases of CPs were further optimized to ensure that they had a relatively high SNR. where \(\gamma_{x}\) is the coherence value. The calculation formula of SNR is presented as [17]:  
\[\chi_{SNR}=\frac{1}{\gamma^{-1}} \tag{4}\]  
The \(\varphi_{PS}(x,y)\) and \(\varphi_{SBAS}(x,y)\) were considered as the wrapped phases of the point \((x,y)\) from the permanent scattering method and small baseline subset method.\(\varphi_{PS/SBAS}(x,y)\) was the wrapped phase value of point \((x,y)\) after integration of weighted average based on the SNR of point \((x,y)\)[17]. The interferometric combination of the permanent scatterer method is first restructured according to the small baseline subsets interferometric combination, and the equivalent interferometric phase of the PS point is calculated from the obtained recombined small baseline subsets combined interferometric phase. It should be noted that the equivalent interferometric phase of the PS point is different from that extracted directly from the small baseline set interferogram, as no spectral filtering was performed. Then, the DS points can then be integrated with the PS/SDFP points in the same way. It is worth noting that these integrated phase values are wrapped phase and the integration process described above can improve the spatial resolution of the interferograms while reducing the aliasing in phase unwrapping [24].
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 3 Noise in PLC Channels
### Corona Noise  
At operation, HV lines generate a strong electrical field in the vicinity of conductors due to nominal high tension. This field accelerates free electrons in the air surrounding the conductors, which collide with the molecules in the air, generating a free electron and a pair of positive ions. This process is repeated until it forms a flow, called a corona discharge. The induced currents are propagated toward both extremes of the line at a certain distance before becoming completely attenuated.  
As reported in [2], we will assume that the power of the corona noise on the line is equal to the sum of those generated by all the corona discharge along the electric line. Using modal theory, the reflection coefficients method, and the corona noise analysis described in [2; 14], the modal voltage \(\mathbf{V}_{M}^{k}\) on the \(k\)-th conductor of the line is established as follows:  
\[\mathbf{V}_{M}^{k}=\mathbf{Z}_{CM}\mathbf{T}_{i}^{-1}\mathbf{\Phi}_{k}G^{k} \tag{15}\]  
where \(\mathbf{Z}_{CM}\) represents the modal matrix impedance of the line, \(\mathbf{T}_{i}\) is the modal transformation matrix for currents, \(\mathbf{\Phi}_{k}\) the Maxwell's coefficients matrix and \(G^{k}\) the generating power function of the line.  
The squared voltage of the \(i\)-th conductor, produced by all the phase conductors, can be calculated as:  
\[V_{i}^{2}(\omega)=\sum\limits_{k=1}^{n}{V_{i}^{k}}^{2}(\omega) \tag{16}\]  
For the following analysis, the corona noise is characterized by means of its power spectral density (PSD), which is determined by the expression [2]:  
\[P(\omega)=\frac{\sum\limits_{k=1}^{n}{\left|{V_{i}^{k}(\omega)}\right|^{2}}}{Z}, \tag{17}\]  
for a specific value of frequency \(\omega\), with generating function \(G^{k}\) in per unit of frequency and \(Z\) representing the characteristic impedance parameter of the conductor being considered.  
Once the PSD of the corona noise is obtained, a corona noise generator is implemented via a stochastic process, which filters uncorrelated Gaussian samples in order to approximate the
## 5 Fair Training
### In Processing Fairness  
In-processing fairness refers to modifying the learning algorithm or objective function during model training to explicitly incorporate fairness constraints. These techniques aim to simultaneously optimize accuracy while reducing bias and promoting fairness. Their main approaches include adversarial training [172; 173; 174; 175; 176], adversarial debiasing [177; 178; 179; 179], equalized odds post-processing [177; 174; 114; 178], fair causal learning [181; 45; 182; 183; 184], and meta-fairness [185; 163; 186].  
Table 7 summarizes the comparison of the methods above. Adversarial training and adversarial debiasing introduce an adversarial component to the learning process, aiming to minimize the influence of sensitive attributes on model predictions. These methods have been applied across tasks like natural language processing, computer vision, and recommendation systems to enhance fairness and reduce bias. Causal learning methods focus on understanding causal relationships within data and addressing confounding factors that lead to biased predictions. This approach has been implemented in domains such as healthcare and criminal justice to ensure fairer and more interpretable outcomes. Meta Fairness involves learning a fairness-aware optimization algorithm that dynamically adjusts the balance between fairness and accuracy during model training. It is particularly valuable when fairness requirements vary across user groups or over time.  
\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
**Pre-Processing Fairness Method** & **Features** & **Pros** & **Cons** & **References** \\ \hline Re-sampling Techniques & Balance representation of different groups & Simple and easy to implement & May lead to loss of information and increased computation & [150; 151; 152; 153] \\ \hline Re-weighting Techniques & Assign higher weights to underrepresented groups & Does not alter the original dataset & Requires careful selection of appropriate weights & [154; 155; 156; 157; 158; 159] \\ \hline Data Augmentation & Generate synthetic data to increase representation & Increases the diversity of the training dataset & Synthetic data may not fully represent real-world samples & [160; 161; 162; 163] \\ \hline Fairness-aware Clustering & Cluster data points while maintaining fairness & Incorporates fairness constraints during clustering & May not guarantee perfect fairness in all clusters & [164; 165; 166; 167] \\ \hline Synthetic Minority Over-sampling Technique (SMOTE) & Generate synthetic samples for the minority class & Addresses class imbalance & May result in overfitting noisy samples & [168; 169; 170; 171] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pre-processing fairness comparison.  
Adapting to different biases and trade-offs between fairness and performance, these methods provide valuable tools for equitable AI. Choosing the appropriate method hinges on factors such as the application context and bias type.
## 4 Limitations
### Sensitivity to Variations in \(\hat{\mathbf{N}}\) for Structured Light  
The WAI normal is perturbed around the \(z\) axis. The direction vector of the reflected ray \(\hat{\mathbf{v}}\) is obtained by the law of reflection,  
\[\hat{\mathbf{v}}=\hat{\mathbf{v}}^{\text{P}}-2\hat{\mathbf{N}}\,\,(\hat{ \mathbf{N}}\cdot\hat{\mathbf{v}}^{\text{P}}), \tag{21}\]  
where \(\hat{\mathbf{v}}^{\text{P}}\) is the direction vector of the projected ray from the projected center through an arbitrary feature point. Substituting \(\hat{\mathbf{N}}=-\hat{\mathbf{z}}\) into Equation (21) yields the direction vector of the reflected ray, when the WAI is flat, \(\hat{\mathbf{v}}^{\prime}\). When the water is wavy, the reflected angle changes by  
\[\hat{\mathbf{v}}=\arccos\left(\hat{\mathbf{v}}\cdot\hat{\mathbf{v}}^{\prime}\right) \tag{22}\]The perturbation has two principal components. The first principal component is the meridional plane component, which is in the _xoz_ plane. In this component, the WAI normal \(\hat{\mathbf{N}}\) rotates around the \(y\) axis. Here, we analyze the sensitivity of structured light to changes in \(\hat{\mathbf{N}}\) by taking the projected ray along the optical axis of the system as an example. Assuming that \(\theta_{\text{pro}}\) is the elevation angle of the projector, and the unit vector \(\hat{\mathbf{v}}^{\text{P}}=\hat{\mathbf{v}}^{\text{P}}_{\text{opro}}=[\cos \theta_{\text{pro}},0,\sin\theta_{\text{pro}}]^{\text{T}}\), the corresponding normal is  
\[\hat{\mathbf{N}}=\hat{\mathbf{N}}_{\text{xoz}}=(\;\sin\theta\;,\;0\;,\;-\cos \theta\;)^{\text{T}}, \tag{23}\]  
where \(\theta\) is the inclination angle of the WAI. Another perturbation component is the sagittal plane, which resides in the _yoz_ plane. In this component, \(\hat{\mathbf{N}}\) rotates around the \(x\) axis; then,  
\[\hat{\mathbf{N}}=\hat{\mathbf{N}}_{\text{yoz}}=(\;0,\sin\theta,-\cos\theta\; )^{\text{T}}, \tag{24}\]  
Substituting Equations (23) or (24) into Equations (21) and (22), we derive the angular perturbation of the reflected ray. Sensitivity to perturbations is assessed by \(d\psi/d\theta\). Figure 8 shows the angle of deflection of the reflected ray as a function of the inclination of the WAI when \(\theta_{\text{pro}}=30^{\circ},\;45^{\circ}\), respectively. The results reveal that the sensitivity of the structured light to the variations in the WAI normal is a constant value, namely \(d\psi/d\theta=2\).
# Wearable Sensors for Vital Signs Measurement: A Survey
## 2 Classification of Wearable Medical Sensors
### Classification and Application of Wearable Medical Sensors  
Wearable medical sensors are classified according to their application; they acquire different organ or physiological parameters from the human body, such as respiratory  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scholars** & **Monitoring Signal** & **Channels** & **Signal Frequency/Hz** & **Acquisition Frequency/Hz** & **Signal Amplitude/mV** & **Record Duration** & **Clinical Application** \\ \hline [PERSON] et al. [10] & ECG & 1–12 & 0.05–150 & 250–1000 & 0.1–5 & 10 s–24 h & Heart disease monitoring, heartbeat classification, emotion recognition, sleep staging \\ \hline [PERSON] et al. [11] & EEG & 1–256 & 0.1–100 & 0.3–3000 & 0.1–100 & 0.5–24 h & Brain disease monitoring, emotion recognition, sleep transition detection \\ \hline [PERSON] et al. [12] & EMG & 1–32 & 25–5000 & 512–10,000 & 0.1–100 & 30 s–24 h & State recognition \\ \hline [PERSON] and [PERSON] [13] & EOG & 1–4 & 0.1–20 & 200 & 0.05–3.5 & 0.5–24 h & Sleep staging \\ \hline [PERSON] et al. [14] & PCG & 1 & 10–400 & 1–2000 & –2–2 & 0.5 s–24 h & Heart disease monitoring, heartbeat classification, emotion recognition, sleep staging. \\ \hline [PERSON] et al. [15] & PPG & 1 & 0.25–40 & 5–500 & \(-\)10–10 & 100 s–24 h & Heartbeat classification and sleep staging \\ \hline [PERSON] et al. [16] & BCG & 3 & 1–20 & 1–20 & \(-\)0.05–0.05 & 5 s–24 h & Heart disease monitoring, heartbeat classification, emotion recognition, sleep staging \\ \hline [PERSON] et al. [17] & EDA & 1 & 0.1–16 & 16–128 & 110 s–24 h & Emotion recognition \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of monitoring characteristics, relevant technical parameters, and clinical application of wearable medical sensors.  
Figure 1: Schematic diagram of different application classification of wearable medical sensors.  
physiological parameters, blood pressure physiological parameters, body temperature physiological parameters, and sleep physiological parameters. They are very important for knowing the vital signs and health in people's daily life [18]. The specific classification applications are as elaborated below.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Bidirectional RNN (BRNN)  
In general, Normal Recurrent Neural Networks (RNN) are limited in terms of their input data flexibility, since future input data are omitted from the current state. Ref. [109] developed the Bidirectional Recurrent Neural Network (BRNN) to address the limitations of RNNs. BRNNs can be trained using data from both the past and the future at any given time step, since they can receive input from both directions. Figure 8 illustrates the basic structure of the BRNN. The mathematical expression is presented in Equations (23)-(25) [110]:  
\[\overset{\rightarrow}{h}_{t}=f\bigg{(}W_{x,\overset{\rightarrow}{h}}X_{t}+W_ {\overset{\rightarrow}{h},\overset{\rightarrow}{h}}\overset{\rightarrow}{h}_ {t-1}+\ b_{\overset{\rightarrow}{h}}\bigg{)} \tag{23}\]  
Figure 7: The architecture of the GRU network [94].  
The input vector at time step \(t\) is signified by \(x_{t}\), the forward hidden layer activation vector at time \(t\) is signified by \(\overset{\rightarrow}{h}\), the backward hidden layer activation vector at time \(t\) is signified by \(\overset{\leftarrow}{h}\), the weight matrix is signified by \(w\), the bias is signified by \(b\), and the output probability is signified by \(y_{t}\). The probability of the output at time \(t\) is signified by \(y_{t}\). The activation function of each node in the hidden layer is signified by \(f\), while the SoftMax function is signified by \(g\).
# Wind Turbine Technology Trends
## 3 Discussion
### Onshore Wind Turbine Development
#### 3.1.4 The Wind Turbine Control System  
The wind turbine control system has the main task of maximizing the energy yield and reducing the structural dynamic loads. This system includes various subsystems, such as:  
* System for power and load optimization
* Condition monitoring system
* Ice detection and anti-icing system
* Shadow flicker control system
* Fire suppression system
* Bat protection system
* Lightning detection system
* Oil debris monitoring system.  
The system includes various sensors, actuators, cable connections, decentralized controls and special computers and software. Therefore, the design performance of the control system is divided into several functional units. It usually consists of a central computer located at the base of the tower, a control and switch unit in the nacelle, and several decentralized controls. This is where the default values for the functional assembly are generated and the remote monitoring system is connected. In the nacelle control unit the signals from the decentralized controls are processed and the results are transmitted to the central computer via the data bus system. The blade pitch control system, for example, is concentrated in the rotor head. Almost the entire control of the blade pitch, from the acquisition of the wind data to the control of the parameters, is carried out largely autonomously via so-called \"pitch boxes\". Control systems are becoming more advanced and efficient, and the influence of environmental factors on system operation is also being analyzed [27]. The control algorithms can be adapted to the specific requirements and the monitoring can be performed in real time.  
Improving the performance of a wind farm is a major challenge, especially when it operates under unstable weather conditions. Therefore, it is necessary to install a device that tracks the maximum power output expected from the wind turbine (MPPT). Approaches based on (hill climbing) have been applied to simulate maximum power trackers, but they have limitations in terms of tracking speed and efficiency. [PERSON] et al. [28] propose the latest efficient Archimedes optimization algorithm (AOA) approach for MPPT simulation. The proposed algorithm adjusts the inverter duty cycle to maximize the output power. Experimental test results confirm the robustness of the proposed approach in achieving the best performance of the wind power system.  
It should be noted that the performance of a wind turbine operating under transient conditions decreases with age. [PERSON] et al. [29] analyzed 13 years of operational data from Vestas wind turbines and concluded that wind turbine performance decreased by about 5% over a 10-year period, and the decrease in performance was not linear.