esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 3 Results  
### Characterization of Drained Graywater  
#### 3.1.1 Volume of Drained Graywater  
The average volumes drained from the first treatment (GWT1), the second treatment (GWT2), and the third treatment (GWT3) were, respectively, 3.10 L, 2.41 L, and 1.96 L. With each treatment step, the average volume dropped significantly.  
The drained volumes (V\({}_{\rm n}\)) were compared to the initial volume (V\({}_{0}\)). At the first treatment stage, the volume of GWT1 was approximately 78% of the initial volume (RGW), GWT2 was about 60%, and GWT3 was approximately 49%. A highly significant correlation was observed between the volumes (Figure 2). The volume drained at each stage, compared to the previous one (Vn-1). It ranged from 78% to 81%, with an average of 79%. The volumes followed a geometric sequence, with each term obtained by multiplying the previous one by a constant ratio. The geometric sequence is as follows:  
\[\rm V_{n}=q^{n}\cdot V_{0}, \tag{1}\]  
where q = 0.79, which is the calculated average value. The values derived from the previous equation closely matched the measured values.
## 2 Materials and Methods
### Data Processing  
The kinetics and kinematic data were processed with a low-pass fourth-order zero-lag digital Butterworth filter at 40 and 12 Hz, respectively. The position and orientation data were synchronized with the GRF data. The joint angles were calculated as previously reported by [PERSON] and [PERSON] and [PERSON] (2018). To compare trunk inclination across the landing conditions, sagittal plane angles of the thorax and sacrum were calculated relative to the vertical axis of the global coordinate system and were defined as the thoracic and sacral inclination angles, respectively. The participants may have modified their sacral and thoracic inclination angles differently while landing due to different vertebral articulations in the lumbar spine. Therefore, both the sacral and thoracic inclination angles were examined to determine how the participants modified their trunk positions during single/double-leg landings. The thoracic and sacral inclination angles as well as the ankle dorsi/plantar flexion angles at initial foot contact were extracted to ensure that the participants successfully modified their landing styles across the three conditions. To test hypotheses of the current study, the peak vertical GRFs and peak plantar flexor moments as well as the times to these peaks were extracted. The peak knee extensor moment, sagittal plane hip and ankle moments, and knee flexion angles at the instant of peak knee extensor moment were subsequently extracted.
# Solar Irradiance Forecasting with Transformer Model
###### Abstract  
Solar energy is one of the most popular sources of renewable energy today. It is therefore essential to be able to predict solar power generation and adapt energy needs to these predictions. This paper uses the Transformer deep neural network model, in which the attention mechanism is typically applied in NLP or vision problems. Here, it is extended by combining features based on their spatiotemporal properties in solar irradiance prediction. The results were predicted for arbitrary long-time horizons since the prediction is always 1 day ahead, which can be included at the end along the timestep axis of the input data and the first timestep representing the oldest timestep removed. A maximum worst-case mean absolute percentage error of 3.45% for the one-day-ahead prediction was obtained, which gave better results than the directly competing methods.  
t 100 2022 20  
In contrast to the research [6; 7; 8], the attention mechanism is implemented here using a Multi-head Attention layer rather than an LSTM or Bi-LSTM layer for temporal context features' understanding. In the paper by [PERSON] et al. [9], a classical ANN model with fully connected layers is described, which does not learn to contextualize features in long time series, as opposed to, e.g., the attention matrix. The papers [10; 11; 12] show the potential to break down the learning process into sunny and cloudy days. The studies [13; 14] also show the advantage of using weather data as the input and describe the correlations between weather features and solar irradiance. The work [15] shows the possibility of segmenting clouds from an image and then expressing solar irradiance, thus opening the possibility of exploiting the very strong correlation between cloud cover and solar irradiance. The research [16] highlights several methods for processing satellite data to make very short-term forecasts of solar irradiance.  
The Transformer deep neural network model has been specially adapted here to process all of the following intrinsically linked signals from different domains: solar irradiation, weather conditions, and position on Earth. These innovative space-time links between features, as well as point and regional data signal processing, enabled us to achieve a better one-day-ahead prediction. This was expressed by the maximum worst-case mean absolute percentage error of 3.45%, which is substantially smaller than that of directly competing methods.
# Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm
###### Abstract
They applied the slime mold algorithms to solving multiobjective optimization problems and proved that the proposed algorithm was effective in solving complex multiobjective problems. [PERSON] et al. [17] proposed a hybrid algorithm based on a state-adaptive slime mold model and fractional order ant system (SSMFAS) to solve the traveling salesman problem (TSP). Experimental results showed that the algorithm had the competitiveness to find better solutions on TSP instances. By integrating chaos mapping and differing evolution strategies for overall optimization, [PERSON] et al. [18] devised an enhanced slime mold algorithm, which was applied to engineering optimization problems. The whale optimization algorithm and the slime mold algorithm were combined by [PERSON] et al. [19] to tackle a chest X-ray separation of images issue. [PERSON] et al. [20] used slime mold algorithms to optimize the optimal intelligent inverter control system of photovoltaic and energy storage systems to improve the photovoltaic carrying capacity of the distribution network. In this paper, an improved slime mold algorithm is considered to study the fair scheduling of multiprocessor and multitasking. Through in-depth research on slime mold algorithms, it was found that there were still certain limitations. For example, the population diversity is not rich enough, the convergence speed is slow, and it is easy to fall into a local optimal solution. In the standard iteration process of the SMA, the random initialization of the slime mold swarm reduces the potential for population diversity. It also lacks effective solutions when addressing population converged to local optima. The fixed boundary check strategy in the standard SMA makes it difficult to return to the better positions when slime molds exceed the boundaries. This paper makes multistrategy improvements to the standard slime mold algorithm. The main contributions of this paper are as follows:  
1. A reverse learning initialization population strategy based on Bernoulli chaotic mapping is introduced to increase the diversity of populations. 2. Cauchy mutations are introduced to help slime mold populations jump out of a local optimal solution. 3. A nonlinear dynamic boundary improvement strategy is introduced to accelerate the convergence rate of the population. 4. The IMSMA is applied to solving the fair scheduling problem on multiprocessors to minimize the average processing time on each processor. The article organization is as follows. Section 1 introduces the research about fair scheduling problems and the slime mold algorithm. Section 2 describes some relevant literature on fair scheduling. The conventional slime mold algorithm is presented in Section 3. Section 4 provides detailed improvement strategies for the improved slime mold algorithm (IMSMA). The simulation tests are presented in Section 5. Section 6 models the fair scheduling problem on multiprocessors and applies the IMSMA to solve it. Section 7 provides numerical experiments for fair scheduling on multiple processors. Conclusions are given in Section 8.
## 3 Results
### Real-World Application Results
#### 3.1.3 Memory Consumption  
The lowest graph of Figure 6 shows the memory consumption. Here, the OML approach also delivered results comparable to the basic batch approach. However, it should be emphasized again that the batch approach's memory consumption only took place during the training step, and the remaining consumption was negligible. This fact is also visualized by the graph. In the first evaluation step, the memory consumption for the classic batch method dropped towards zero. The shifting window and the landmark approach performed comparably poorly. This was mainly due to the generated model, which had to be built again in each iteration.  
Figure 5: The comparison between predicted and real values for a subset of the evaluation horizon. Each subplot is related to the predictions of one of the evaluation strategies. The Y-axis shows the vibration values, whereas the X-axis shows the evaluation steps. Please consider the different scales of the Y-axes.
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 3 Experiment  
### Dataset  
Regarding the dataset preparation, we constructed the noisy-clean image pairs by first collecting high-quality, noise-free SAR images from the SAR Ship detection dataset (SSDD), which is the first publicly available dataset dedicated to ship target detection in SAR images, both domestically and internationally. It can be used to train and test detection algorithms, allowing researchers to compare algorithm performance under the same conditions. The SSDD consists of 1160 images and 2456 ships, with an average of 2.12 ships per image, and the dataset will continue to expand in the future. In cases where real noise-free SAR images were not available, we generated synthetic noise based on established noise models. The types of noise are Gaussian noise, Poisson noise, salt and pepper noise, coherent noise, and stripe noise. The noises are added on SSDD and the dataset is denoted as SSDD-noise. As shown in Figure 5, specifically, we simulated speckle noise and additive Gaussian noise in accordance with the statistical properties typically observed in SAR imaging. This synthetic noise was added to the clean images to create realistic noisy counterparts. After denoising, take the average to calculate the BSF under different noise conditions. When calculating background clutter, set the object area to 0 based on the annotation results.
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 4 Attitude Point-to-Point Generator
### Final Strategy
#### 4.2.2 Combining the Manoeuvres  
In this section, the final maneuver strategy is presented. The total time of the guidance law presented could be shorter than the time at which the observation actually starts. This would seem to invalidate all the previous analyses. However, it is possible to implement a strategy to overcome the problem. Before showing it, some parameters must be introduced. The first is identified as \(\Delta t\) and represents the difference between the \(t_{start}\) and \(t_{0}\), i.e.,  
\[\Delta T=t_{start}-t_{0}, \tag{53}\]  
where \(t_{start}\) is the time when the target point acquisition should start and \(t_{0}\) is the initial time of the \(APPG\) maneuver. Hence, \(\Delta t\) represents the allowed maximum time to perform the maneuver. Since the strategy implemented has the purpose of obtaining a minimum time for each of the three steps, the total maneuver time \(\Delta T_{m}\) can be defined as the sum of the times of each step (\(T_{f_{k}}\))  
\[\Delta T_{m}=\sum_{k=1}^{3}T_{f_{k}} \tag{54}\]  
On one hand, maneuvers that do not fulfill the condition \(\Delta T_{m}\leq\Delta T\) must be discarded because they require a longer maneuver time than the time in which the acquisition must begin. Therefore, these maneuvers are not feasible. On the other hand, if the condition \(\Delta T_{m}\leq\Delta T\) is verified, it becomes useful to define another variable, named _waiting time_, representing the difference between the two total times:  
\[t_{w}=\Delta T-\Delta T_{m} \tag{55}\]  
the _waiting time_ represents a period in which the satellite could be in a stand-by mode before starting ground target acquisition. Thus, the _waiting time_ must be included in the guidance law. The solution could be to perform it either at the beginning or at the end of the rest-to-rest phase (Figure 8), this does not compromise the guidance law proposed since in that conditions the angular velocities and accelerations are zero.  
In the results section, the solution with the waiting phase at the end of the rest-to-rest maneuver has been chosen, but there would be no problem if the opposite solution were chosen. It is intended to emphasize the fact that this maneuver is certainly not possible without proper control to maintain the state of the satellite, which would otherwise tend to shift due to disturbances. However, this analysis is beyond the scope of this paper, since providing a control methodology is not part of the addressed problem. In fact, the proposed approach provides only the kinematic guidance of the satellite in terms of angular velocity and quaternion. Moreover, it is noteworthy that the choice of waiting without performingany action during the _waiting time_ is not the only one possible, as other activities could be done in the same time interval. For instance, the satellite could send data to the ground or place itself with the panels facing the sun, just by implementing a new _AGP_.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 2 Materials and Methods  
### Search Environment Modeling  
In this section, we will establish an anti-submarine sea area model for multi-UAV swarms, considering the existence of an unknown number of targets (including submarines, ships, aircraft carriers, etc.) in the sea area, as shown in Figure 1. \(N_{v}\) UAVs with detection sensors (visible light, magnetic detectors, etc.) are used to search for relevant targets within the mission area. During the search process, UAVs share information in the sea area through wireless communication. In the interest of ensuring safety, the goal is to complete the search task in the shortest time possible and at the lowest cost. The area is divided into grids, and the grid set describing the search area is defined as:  
\[E=\left\{(m,n)\big{|}m=1,2,\cdots,L_{x},n=1,2,\cdots L_{y}\right\} \tag{1}\]  
where \(E\) represents the grid set; \((m,n)\) represents the grid coordinates; and \(L_{x}\) and \(L_{y}\) represent the number grids along the \(x\) and \(y\) axes, respectively. With this task area, an association rule-driven target probability map model, an association rule-driven probability map update rule, and a UAV motion model will be established in sequence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Application** & \multicolumn{2}{c}{**Search**} & \\  & & **Map/Algorithm** & **Deficiency** \\ \hline [PERSON] [7] & Basic problem of search for stationary targets & Target probability map & Unexpanded moving targets \\ \hline [PERSON] [8] & Stationary targets with attack & Digital pheromone map & Does not consider evading attacks \\ \hline [PERSON] [9] & Moving target in search area & Extension of digital pheromone map & Target motion characteristics not considered \\ \hline [PERSON] [10] & Moving formation targets in search area & Target probability map and certainty map & Aggressiveness of targets not considered \\ \hline [PERSON] [4] & Moving target search problem with multiple searchers & Dynamic asset allocation & Not suitable for searching for submarine targets \\ \hline [PERSON] [12] & Retrieval of lost moving target problem & Motion-coding particle swarm optimization algorithm & Relevance characteristics of the target are not considered \\ \hline [PERSON] [13] & Target search problem in uncertain environment & Immune genetic algorithm & The movement characteristics and danger of the target are not considered \\ \hline [PERSON] [14] & Path optimization problem of UAVs & New bee colony algorithm & Does not take into account the aggressiveness and relevance of the target \\ \hline [PERSON] [15] & Cooperative search-attack mission planning problem for multi-UAVs & Improved ant colony algorithm & Correlation with aggressive target movement not considered \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related research.
## 3 Conventional Galerkin Element  
### Galerkin Finite Element  
In Galerkin FE analysis, the trial space \(S_{u,m}^{h}\subset H_{u}^{1}\) is formed by polynomials of degree \(m\) on each element, and the test space \(S_{v,m-1}^{h}\subset H_{v}^{0}\) is formed by polynomials of degree \(m-1\) on each element. Since the conventional element model is of the one-step type and the whole solution on the mesh can be obtained element by element in a recursive fashion, it suffices to consider only the first element \((t_{0},t_{1})\equiv(0,h)\) as the time domain [20].  
For convenience, the Legendre-polynomial-based hierarchical shape functions are employed in this paper with the trial function \(u^{h}\) of degree \(m\) and the test function \(v^{h}\) of degree \(m-1\) respectively expressed as  
\[u^{h}=\bar{N}_{1}(t)u_{1}^{h}+\sum_{k=2}^{m}\hat{N}_{k}(t)u_{k+1}^{h}+\bar{N}_ {2}(t)u_{2}^{h},\ v^{h}=\sum_{i=1}^{m}N_{i}^{*}(t)v_{i}^{h}. \tag{5}\]  
For the trial function \(u^{h}\), the shape functions of the element two end nodes are usual linear functions:  
\[\bar{N}_{1}=1-\frac{t}{h},\ \bar{N}_{2}=\frac{t}{h}, \tag{6}\]  
and the internal 'bubble' shape functions of degree \(k(\geq 2)\) are taken as  
\[\hat{N}_{k}(t)=P_{k_{0}}(x)-P_{k}(x),\ k=2,3,\ldots,m, \tag{7}\]  
where \(P_{k}(x)\) is the standard Legendre polynomial of degree \(k\) with \(x=-1+2t/h\) and  
\[k_{0}=\left\{\begin{array}{ll}0,&k=2,4,6,\ldots\\ 1,&k=3,5,7,\ldots\end{array}\right. \tag{8}\]To be specific, the expanded forms of the leading shape functions are  
\[\hat{N}_{2}(t)=3(1-x^{2})/2,\ \hat{N}_{3}(t)=5x(1-x^{2})/2,\ \hat{N}_{4}(t)=5(1-x^{2})(1 +7x^{2})/8,\ldots. \tag{9}\]  
For the test function \(v^{h}\), the shape functions simply adopt the Legendre polynomials as follows:  
\[N_{i}^{*}(t)=P_{i-1}(x),\ i=1,2,\ldots,m. \tag{10}\]  
Note that the test function \(v^{h}\) takes a polynomial of one degree lower than the trial function \(u^{h}\), and is independent on each element while the trial function \(u^{h}\) is continuous at common nodes, i.e., at mesh points. Thus, the conventional [PERSON] finite element solution is to find \(u^{h}\in S_{u,m}^{h}\) such that  
\[a(u^{h},v^{h})=(f,v^{h}),\ \forall v^{h}\in S_{v,m-1}^{h}. \tag{11}\]
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 5 Our Privacy-Preserving Swarm Learning
### Global Aggregation
#### 5.3.2 Homomorphic Aggregation  
In the \(t\)th iteration, the winner \(\textbf{MC}_{j}\) is responsible for aggregating all the encrypted gradients stored in the memory pool. \(\textbf{MC}_{j}\) decodes \(\{\mathbb{M}_{t}^{1},\mathbb{M}_{t}^{2},\ldots,\mathbb{M}_{t}^{K}\}\) and homomorphically aggregates encrypted gradients to generate the encrypted global model \([\bar{\textbf{g}}_{t}]_{pk_{FHE}}\) without the need for decryption by the following equation:  
\[[\bar{\textbf{g}}_{t}]_{pk_{FHE}}=\frac{1}{K}\sum_{k=1}^{K}[\bar{\textbf{g}}_{t }^{k}]_{pk_{FHE}} \tag{7}\]
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 5 Thermal Sensing Integration
### Heat-Point Triangulation  
Let \(\hat{\mathbf{x}}_{T}\) be a point on \(\pi_{T}\). Our aim is to calculate the 3D point \(\hat{\mathbf{X}}\) that gets projected into \(\hat{\mathbf{x}}_{T}\). To reconstruct the 3D position of a point in \(\pi_{T}\), we first need to find its corresponding point \(\hat{\mathbf{x}}_{D}\) on \(\pi_{D}\). From epipolar geometry, we know that such a point lies on the _epipolar line_, i.e., the projection on \(\pi_{D}\) of the optical ray through \(\mathbf{C}_{T}\) and \(\hat{\mathbf{X}}\). Given the fundamental matrix, F, obtained by calibration, the parametric representation of the epipolar line in homogeneous coordinates is  
\[I_{D}=\mathrm{F}^{\top}\hat{\mathbf{x}}_{T} \tag{5}\]  
For each point \(\mathbf{x}_{D}=(x_{D},y_{D},1)\) on line \(\mathbf{I}_{D}\), we can now invert the relation in (3),  
\[\mathbf{r}=\mathcal{K}_{D}^{-1}\mathbf{x}_{D} \tag{6}\]  
obtaining the directing vector of the optical ray \(\mathbf{r}=(r_{X},r_{Y},r_{Z})^{\top}\).  
Since each point in \(\pi_{D}\) is associated to a depth value \(d\)--equal to the distance of the 3D point from the center of projection--we are able to unequivocally reconstruct \(\mathbf{X}\)'s coordinates through trigonometric relations. From Figure 3b, one can derive the following identities:  
\[\tan\theta=\frac{r_{Z}}{\sqrt{r_{X}^{2}+r_{Y}^{2}}}\,\quad\tan\alpha=\frac{r_{Y} }{r_{X}} \tag{7}\]  
and hence the 3D coordinates of \(\mathbf{X}\) are given by  
\[\tilde{\mathbf{X}}=\begin{bmatrix}d\cos\theta\cos\alpha\\ d\cos\theta\sin\alpha\\ d\sin\theta\end{bmatrix}\,\quad\mathbf{X}=\begin{bmatrix}\tilde{\mathbf{X}}\\ 1\end{bmatrix} \tag{8}\]  
where \(\tilde{\mathbf{X}}\) denotes \(\mathbf{X}\) in Cartesian coordinates.  
Given the set of reconstructed \(\mathbf{X}\) points, it is possible to reproject them on \(\pi_{T}\) using (4), obtaining a set of \(\mathbf{x}_{T}\) points lying on the epipolar line \(\mathbf{I}_{T}\). By taking account for reprojection errors, we can finally select \(\hat{\mathbf{X}}\) among the reconstructed points as the one whose projection \(\mathbf{x}_{T}=\mathrm{P}_{T}\mathbf{X}\) minimizes the distance from the original point \(\hat{\mathbf{x}}_{T}\) on the thermal camera plane,  
\[\hat{\mathbf{X}}=\operatorname*{arg\,min}_{\mathbf{X}}\|\mathrm{P}_{T}\mathbf{X}-\hat\mathbf{{x}}_{T}\| \tag{9}\]  
Note that the total number of operations required by the algorithm is proportional to the number of points on the epipolar line, which is quadratically less complex than searching through the whole image. This feature helps speed up the triangulation of a 3D point dramatically, allowing a real-time response from the detection of a heat-point on the thermal plane to the reprojection and signaling of the same on the AR viewer.