esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 1 Introduction  
The world's need is increasing every day to reduce dependence on the use of fossil fuels, so finding means, solutions, and alternatives for how to produce the required energy has become of paramount importance. Thus, the push to develop and produce renewable energy globally increases every year, and many countries have managed to develop renewable energy projects based on solar and wind energy on a large scale. This progress is essential to the plan to replace renewable energy sources that depend on fossil fuels and establish a solid foundation for a sustainable society [1]. Off-grid power generation is a viable option for supplying electricity to small communities in developing countries that do not have enough money to spend on a continuousconnection to the public electric grid, and places that are very remote and cannot be easily connected to the grid due to their distance from basic infrastructure. In such circumstances, the utilization of renewable energies can help these places develop more quickly [2]. The most common methods of generating renewable energy are solar and wind energy solutions. However, it often depends on the area to decide which resources will be used to get the best results. This could include hydropower and/or biomass energy as additional means of producing renewable energy. Hybrid Renewable Energy System stations are generally characterized as a combination of two or more various power sources to supply the electrical power required for the loads, and can be a mixture of either traditional and renewable sources, or only renewable sources [3]. An off-grid power generation system causes reliability issues because of an unavailability of electricity backup from the utility grid. Moreover, solar and wind energy's variable nature causes non-linear and erratic energy production, which leads to a power mismatch where the load requirements of the consumer are not satisfied by the capacity production [4]. To overcome this, a hybrid renewable energy system is used with an energy backup unit to meet consumer demand. Where the energy storage system consists of fuel cells (FCs), batteries (Bats), etc., thereby the wind and solar energy complimentary characteristics are integrated with the energy storage system backup unit to make the system credible and sustainable [5]. Several researchers have introduced popular software-based, classical, and meta-heuristic techniques for the unit sizing of hybrid renewable systems. One of the most known of these software programs used for the optimization process is the Multiple Energy Sources Hybrid Optimization Model (HOMER). The authors in [6] utilized the HOMER simulation to study the performance of six different configurations of hybrid systems based on a photovoltaic (PV)/wind turbine (WT)/FC/Bat model. This research paper aims to look at the energy production potential and creation of hydrogen using solar and wind power resources in various regions throughout Saudi Arabia, including Dhahran, Riyadh, Jeddah, Abha, and Yanbu.
# Field Emission from Carbon Nanostructures
## 2 Field Emission from Carbon Nanostructures
### Carbon Allotropes  
In 1991 a great breakthrough in the field of carbon science and technology occurred with the discovery of one-dimensional allotrope of carbon, the so-called carbon nanotubes (CNTs) by [PERSON] et al. [46]. Nowadays, known carbon allotropes (Figure 3) are diamond, graphite, graphene, fullerene and amorphous carbon. A CNT is a cylinder obtained by rolling up graphene sheets; a single walled nanotube (SWNT) is obtained by a single graphene layer forming a cylinder with a diameter of about 1 nm and length up to few mm [47; 48]; a multi-walled nanotube (MWNT) is a set of concentric cylinders with a total diameter up to 100 nm and a length of tens of microns. CNTs properties are strongly dependent on atomic arrangement, diameter and length.  
CNTs are being widely studied because of their unique structure and extraordinary properties. The mechanical properties and chemical inertness, associated with structure dependent electronic properties and high aspect ratio make CNTs suitable for several innovative technologies. CNTs have already been applied as field-effect transistors [49; 50; 51], memory devices [52], nano-tip for atomic force microscopy [53], hydrogen storage system [54; 55], active electrode in supercapacitors [56; 57; 58], transparent conductive thin films [59; 60], power transmission lines with high current-carrying capacity up to \(10^{5}\) A/cm\({}^{2}\)[61], etc.  
The application of CNTs as electron field emitters has been widely investigated ever since it was firstly demonstrated in 1995 [62; 63]. Indeed, the above recalled properties [64; 65] make CNTs excellent emitters, suitable for many applications such as high-resolution electron microscopes as well as electron beam lithography [66; 67], X-ray tubes [68; 69], and flat panel displays [70; 71].  
A detailed review of the CNT properties and synthesis methods is out of the scope of this review and this can be found in [72; 73].  
The high aspect ratio of CNTs is responsible for an extremely large field enhancement factor and provokes very high local electric field intensities around the nanotube apices. Moreover, even in the case of damaging due to arc discharge, the field enhancement factor typically does not result in much modification. The CNT emitters usually maintain high aspect ratios even after arcing or ion bombardment cuts, differently from Spindt-type emitters, for which important flattening occurs at the tips.
## 3 Simulation Results
The same figure is also useful to evaluate the number \(N=\theta(t_{f})/(360^{\circ})\) of complete revolutions during the optimal transfer. The number \(N\) can be considered as a sort of metric of the geometrical complexity of the low-thrust transfer trajectory. As expected, for the reflective sail case, the value of \(N\) increases with the \"distance\" of the target orbit with respect to the parking one; that is, \(N\) varies with the value of the ratio \(r_{f}/r_{0}\). However, for the diffractive sail case, in the studied range of variation of \(r_{f}\) and when \(a_{c}=1\,\mathrm{mm}/\mathrm{s}^{2}\), it turns out that \(\theta(t_{f})<\)360\({}^{\circ}\) (i.e., \(N=0\)), which is a value that indicates a less involved transfer trajectory when compared with the reflective counterpart. This aspect is also confirmed by the graphs presented in the next subsection, which reports the results of the trajectory optimization in three important mission scenarios consistent with a transfer toward Venus, Mars, and Jupiter. Figure 11: Final value of the polar angle as a function of the target orbit radius for an optimal transfer using an ideal and unconstrained reflective sail (red dashed line) or a Sun-facing diffractive sail with EOCPs (solid black line) when \(a_{c}=1\,\mathrm{mm}/\mathrm{s}^{2}\). Figure 10: Force bubble of a reflective sail without attitude constraint and a diffractive sail with a Sun-facing configuration.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 4 Validation of the Experimental Results With ECMWF and Sensitivity Analysis
### Sensitivity Analysis of Significant Wave Height Estimation
#### 4.2.2 Sensitivity Analysis of Significant Wave Height to Wavelength of Ocean Waves  
According to Equation (19), the significant wave height is proportional to the square of wavelength \(\lambda_{p}\) of ocean waves. When the wavelength \(\lambda_{p}\) of ocean waves error increases, the estimation error of significant wave height will also increase. In order to analyze the sensitivity of the significant wave height to the wavelength \(\lambda_{p}\) of ocean waves, it is necessary to assume that the azimuth cutoff \(\lambda_{c}\) and propagation direction \(\phi\) of ocean waves keep unchangeable. Due to \(\lambda_{c}=90\) m, the sensitivity is analyzed within \(\lambda_{p}\in[100\,\mathrm{m},400\,\mathrm{m}]\), and the sensitivity curve of significant wave height to wavelength of ocean waves is shown in Figure 13.  
Figure 12: Sensitivity curve of significant wave height to azimuth cutoff.  
Figure 13: Sensitivity curve of significant wave height to wavelength of ocean waves.  
It can be seen from Figure 13 that with the increase of the wavelength of ocean waves, the sensitivity of significant wave height estimation gradually decreases, which indicates that with the increase of the wavelength of ocean waves, the influence of the wavelength of ocean waves error on the significant wave height decreases.
## 4 Dataset and Experimental Setup
### Data Preprocessing Pipeline  
The preprocessing pipeline ensured uniform input formatting and stability during training and inference, as detailed below:  
* Normalization: All features were min-max scaled to [0, 1].
* Windowing: Sequential models (e.g., LSTM) were trained on 10-time-step sequences using sliding windows.
* Label Encoding: Multi-class labels were converted into binary (normal vs. anomalous) for unsupervised detection and multi-label classification.
* Dimensionality Reduction: Principal Component Analysis (PCA) was applied to reduce feature dimensionality from 80+ to 30 components for autoencoders.
## 3 Experiment Setup
### Training and Testing Database
#### 3.2.3 Data Augmentation  
The data augmentation method is an effective technique for expanding training datasets by generating new data from the original data, thereby balancing the data distribution by generating synthetic data for the minority classes. The strategy aims to create variations in existing data, allowing the neural network to be better generalized with new data and thus improve its classification ability. Several methodologies can be employed, such as adding noise to the temporal signals and applying rotation, mirroring and translation in the case of images, among others. In this study, the synthetic minority oversampling technique (SMOTE) [52] and a Wasserstein generative adversarial network with gradient penalty WGAN-GP [53,54] were used to increase the amount of data for the temporal segments of the ECG. The SMOTE was used to augment the data for class V, and, for class S, the WGAN-GP technique was used. Empirically, from the tests performed, it was observed that this combination provided better results for structure classification, where the use of the WGAN-GP technique specifically to increase the training data of the S class provided greater sensitivity for this class, which was more difficult to classify.  
Table 5 presents the new organization of the database for training and testing for the interpatient approach with data augmentation, and Table 6 presents the new organization of the database for the intrapatient approach with data augmentation.
## 3 Measurements
### Accuracy of Knee Joint Movement Measurement Under Variable Gait Conditions  
The accuracy of knee joint movement detection using the proposed hybrid system was evaluated under multiple gait conditions, including slow walking (0.8 km/h), moderate walking (2-4 km/h), and running (8 km/h). The Fiber Bragg Grating (FBG) sensor maintained a high resolution of 10 picometers across all tests, while the wireless inertial modules preserved an angular accuracy of \(\pm 1^{\circ}\).  
At slow and moderate walking speeds, the FBG and inertial systems exhibited stable performance, with smooth waveform signatures and a consistent correlation between angular deflection and strain-based wavelength shifts. At higher speeds (running conditions), the elastic knee band showed slight displacement and strain decoupling, introducing minor signal artifacts, particularly in the FBG data. This effect was more pronounced in volunteers with leg circumferences outside the optimal 35-38 cm range. Despite this, the inertial modules continued to deliver consistent angular measurements, suggesting a high degree of robustness.  
These observations highlight the system's suitability for clinical and sports applications under typical use conditions while also identifying improvement areas for high-speed movement scenarios, particularly the need for secure sensor integration and mechanical stability. During calibration, a near-linear relationship was observed between the Braggwavelength shift (\(\Delta\lambda\)) and the knee flexion angle (\(\theta\)), allowing for angular estimation from the FBG sensor alone. For example, under controlled conditions, a 1\({}^{\circ}\) change in knee angle corresponded to approximately X picometers of wavelength shift, establishing a sensitivity of \(\sim\)X p.m./\({}^{\circ}\). At higher walking speeds, the strain range and thus the \(\Delta\lambda\) amplitude increased due to greater joint excursion, enhancing sensitivity but also amplifying susceptibility to mechanical noise. The system's resolution, defined as the minimum detectable angular change based on the FBG resolution (10 p.m.), translates to approximately Y\({}^{\circ}\) under standard loading conditions. These quantitative relationships confirm that both sensitivity and resolution remain within clinically acceptable ranges, provided that sensor placement and mechanical stability are maintained.  
While the present version does not include a point-by-point numerical comparison between the FBG and IMU measurements, the observed alignment and signal behavior strongly support their agreement under controlled walking conditions. To address this limitation, future studies will focus on statistical validation using correlation analysis, root mean square error (RMSE), and Bland-Altman plots to rigorously assess the quantitative agreement between both sensing modalities.
## 3 Results
### Water Hyacinth Monitoring  
Water hyacinth was identified and mapped by computing NDVI values (0.6-0.95) derived from the Sentinel-2 Level-2 images using the Sen2 Cor products. Figure 6a indicates an example of a RGB composite scene on 17 January 2020 of the northeastern part of Lake Tana, whereas Figure 6b shows the water hyacinth coverage at 10 m spatial resolution on the same day, highlighting the accurate performance of these NDVI settings. NDVI maps showed that water hyacinth expansion varied both in space and time during the study period. The water hyacinth cover and biomass started to decrease in February 2020, reaching its lowest values in July 2020 (Figure 7g). From then onward, water hyacinth proliferated rapidly in the northeastern part of the lake and expanded continuously until the end of October 2020 (Figure 7j). Peaks in cover and biomass were observed in October, just a month after the peak of Chl-a and TSM concentrations observed in September (end of the main wet season) (Figure 8a,b). A significant positive correlation was found between the temporal series of water hyacinth coverage and Chl-a concentration (Pearson coefficient = 0.53; n = 12; \(p\) = 0.05). Both cover and biomass showed the same trend and started to decrease in November 2020. The average biomass ranged from 0.66 to 0.94 (Figure 8b). Overall, the one-year analysis results showed that water hyacinth cover and biomass were higher in the late wet season (spring) and lower in the dry and early wet season. The changes in biomass and cover went in parallel, i.e., the highest biomass (average NDVI) per pixel and total biomass were observed when the cover was also highest (Figure 8b). The greatest rate of decrease was observed in July 2020 in just over two weeks, whereas the highest increase was observed in August (both roughly 4% d\({}^{-1}\)).  
Figure 5: (a-l) Total suspended matter concentration (TSM, g m\({}^{-3}\)) in Lake Tana from January 2020 to December 2020 estimated from the Sentinel-2 satellites after the C2 RCC processor.  
Figure 6: (a) RGB (red-green-blue) band composite on 17 January 2020 indicating the water hyacinth area on the northeastern part of Lake Tana; (b) water hyacinth map extracted from the Sentinel-2-Level-2 satellite image (Sen2 Cor) using the NDVI values with the range from 0.6 to 0.95.  
Figure 7: (a–I) Water hyacinth expansion in cover and biomass for each month fro January 2020 to December 2020 during the study year in 2020 extracted from Sentinel-2 Level-2 satellite images (Sen2 Cor) using the NDVI.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 4 The Coverage Mechanism Based on Grouping
### Aggregation Behavior  
Aggregation behavior is to make the groups that have been divided together first to form a relatively regular pattern so that they can better move together in the way of the group to cover the area. During the aggregation process, the robot will have some status transformation, mainly including the following five status. (1) Approach status; (2) Stop status; (3) Pend status; (4) Join status; (5) Arrive status. The transition relationship of these five states is shown in Figure 7.  
* Approach status: It is the initial status of the normal robot. and the robot should gradually approach the leader robot.
* Stop status: This is to prevent the robot from colliding with the approaching the leader robot.
* Pend status:The Pend status also stops the robot's motion because there are robots with Pend status or Arrive status.
* Join status:The robot will approach the group and find a suitable position to join the formation.
* Arrive status:when the robot has found a suitable position, it will stop moving.  
The leader robot is in the Arrival status, and the ordinary robot is in the Approach status, which is constantly approaching the leader.  
In the Approach status, if the robot detects that there are other Pend, Join or Arrive status within a certain distance \(d_{safe}\), it will switch to the Pend status. The Pend status and the Stop status perform similar operations. In addition, if the robot in the Pend status receives the join instruction from the robot in the Arrive status and detects that it will not collide with the robot in the Join status, it will switch to the Join status.The robot should approach the formation until the distance between the robot and a certain Arrive status is \(d_{min}\). Then the robot keeps a distance of \(d_{min}\) from the formation and moves around until it reaches a suitable position, and then it switches to the Arrive status.  
On the one hand, the robot should detect whether there is still space in the \(d_{min}\) range around it for the other robot to arrive; on the other hand, the robot should detect the undetermined status robot in the \(d_{safe}\) range and sends the status switch instruction. The total flow of aggregation behavior is shown in Figure 8.  
Figure 7: Aggregation process status transition diagram.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 2 Modeling
### Rub Impact Fault  
The vibration amplitude in certain disks can surpass the initial gap between the rotor and stator, leading to rotor/stator rub impact failure. This is typically caused by an excessive imbalanced mass or failure of the rotor system. This section establishes a model for rotor/stator rub impact failure by analyzing the force state of the rotor.  
It is assumed that the rubbing occurs between the disk and the stator, so the rubbing force acts on the disk. When the radial rubbing occurs, the rubbing force is shown in Figure 10. Coordinate \(OYZ\) is the fixed coordinate system of the rotor system. \(\delta\) is the initial gap between the disk and the stator; \(F_{n}\) and \(F_{\mathrm{T}}\) are the radial and tangential component of the rubbing force, respectively.  
The stiffness of the case in this model is much smaller than the rubbing stiffness, so it is assumed that an elastic collision occurs. The rubbing force consists of a linear contact force and a Coulomb friction force. The expression of the friction force is [31]:  
\[\left\{\begin{array}{c}F_{n}\\ F_{\mathrm{T}}\end{array}\right\}=\left\{\begin{array}{cc}0&e<\delta\\ k_{c}(e-\delta)\left\{\begin{array}{c}1\\ \mu\end{array}\right\}&e\geq\delta\end{array}\right. \tag{19}\]  
where \(e=\sqrt{V^{2}+W^{2}}\) is the radial displacement of the disk, \((V,W)\) are the concentric coordinates of the disk, \(k_{c}\) is the radial stiffness of the case stator, and \(\mu\) is the Coulombfriction coefficient between the rotor/stator. Introducing the Heaviside function \(H(\cdot)\), the friction force is obtained by decomposing the force along the \(Y,Z\) coordinate axes:  
\[\mathbf{F}_{r}=\left\{\begin{array}{c}F_{x}(V,V)\\ F_{y}(W,W)\end{array}\right\}=H(e-\delta)\cdot k_{c}\bigg{(}\frac{\delta}{e}-1 \bigg{)}\left[\begin{array}{cc}1&-\mu\\ \mu&1\end{array}\right]\left\{\begin{array}{c}V\\ W\end{array}\right\} \tag{20}\]  
The physical meaning of the above equation is that when \(e<\delta\), no rubbing occurs and the force is 0; when \(e\geq\delta\), rubbing occurs to produce friction force.
## 3 Materials and Methods
### Coaxial Alignment Correction Algorithm Based on Optical Path
#### 3.3.2 Homography Matrix Calibration  
During the installation and movement of the device, errors are easily generated. Therefore, adjusting the light path of the camera and projector using a hot mirror often results in an optical path near alignment, but not coaxial. Although the error caused by the surface projection can be reduced, the non-coaxial light path still results in a slight deviation between the projected image and the actual object. To address this issue, we utilized a plane with a printed grid pattern and homography matrix to calibrate the projection. The formula for the homography matrix is as follows:  
\[\begin{bmatrix}\chi 2\\ y2\\ z2\end{bmatrix}=\begin{bmatrix}\text{h}1&\text{h}2&\text{h}3\\ \text{h}4&\text{h}5&\text{h}6\\ \text{h}7&\text{h}8&1\end{bmatrix}\begin{bmatrix}\chi 1\\ y1\\ z1\end{bmatrix} \tag{6}\]  
The homography matrix is a mathematical tool used to describe the relationship between two planar transformations. By identifying four non-collinear corresponding points between the two planes, both linear and non-linear transformations can be applied to the image. However, the projected image cannot be observed by the near-infrared camera, and the four corners of the projection need to be manually marked on the flat surface. By using a NIR camera, the positions of the four corners on a flat surface and a projected image can be obtained. Subsequently, the homography matrix is computed as follows:  
\[\begin{bmatrix}1.09&-1.62\times 10^{-2}&-7.92\times 10\\ 3.03\times 10^{-2}&1.07&-5.05\times 10\\ 3.72\times 10^{-5}&1.86\times 10^{-5}&1\end{bmatrix} \tag{7}\]  
The Frobenius norm of the homography matrix can be used to measure the size or scale of the matrix and is calculated to be 93.96. This value is used to quantify the deviation between the projected image and the object and assess the discrepancy between them. Then, the homography matrix is used to adjust the projected image to match the object. Images before and after adjustment are shown in Figure 7.