esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Measurements of Mat Pressure Sensor
#### 3.1.3 Third Experimental Test  
In the third test experiment, the measurements of the pressure sensor mat were carried out using the Rigol DM3058E 5\(\lx@math@degree\) Digit Digital Multimeter, the RS PRO Power Supply, and Force gauge FB200 200 N (20 kg) 0.05N (Figure 5a). The force meter is dedicated to measuring force while pressing, and its graphical display allows for easy to read indications or present measurement results on a histogram or chart. The force gauge is equipped with a socket for an external power supply unit, RS232C interface (RJ joint), USB interface and optional Bluetooth interface, or THR (thresholds) output. The terminal set of the force gauge, in particular, the tip (Figure 9c), made of steel, suitable for the measurement to be conducted on the mat surface, was designed in Autodesk Inventor (Figure 9b). The terminal end of the dynamometer has a surface area of 28.274 mm\({}^{2}\) and the technical drawing is shown in Figure 9a.  
To perform the measurements, the dynamometer was mounted on a support in the vertical position and properly calibrated. The force gauge was used to apply different loads and measure the pressure on the prototypes of mats with different geometries, as shown in Figure 5a. The matrix of the mat is a square arrangement of elements into rows and columns (Figure 3b). The columns and rows of the mat matrix are numbered with letters (A, B, C...) and numbers (1, 2, 3...), respectively. The pressure sensitive mat in Figure 1a consists of a matrix of 14 \(\times\) 14 sensing elements. The pressure sensitive mat in Figure 1b consists of a matrix of 8 \(\times\) 8 sensing elements. To measure the current and voltage, the multimeter was used, and a supply voltage is assumed of 5 V, 3.3 V, and 7.5 V for the mat with 14 \(\times\) 14 sensing active elements. The cell elements considered under the pressure of the force gauge are H14, N14, H10, L7, and G11, as shown in Figure 10. The variation in mat resistance (\(\Omega\)) in the function of pressure force expressed in Newtons for different conducted tests is reported in the Figure 10.  
The voltage \(V_{g}\) is assumed to be 5 V for a mat with 8 \(\times\) 8 active sensing elements. The cell elements under pressure of the force gauge are B8, H8, D3, B6, and F5, as shown in Figure 11.  
Figure 9: (**a**) Technical drawing of the terminal tip (set); (**b**) Terminal set of the force gauge designed in Autodesk Inventor; (**c**) Realized terminal set of the force gauge.
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 6 Summary and Prospect  
### Critical Analysis and Discussion  
To improve the accuracy of satellite network traffic prediction, this paper proposes and verifies a satellite network traffic prediction method based on GRU neural network and attention mechanism. Based on GRU neural network, the attention mechanism is integrated to pay attention to the importance of traffic data and hidden state, which not only learns the time-dependent characteristics of input sequences but also mines the interdependent characteristics of data sequences, thus improving the accuracy of traffic prediction. At the same time, the PSO algorithm is used to determine the optimal hyperparameter combination of the model, which makes the model have higher prediction efficiency when forecasting traffic. In this paper, the effectiveness of the proposed method is illustrated by comparing it with several popular traffic prediction algorithms. In terms  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction** & **MAE** & **RMSE** & \(\mathbf{R^{2}}\) & **FLOPS** \\
**Model** & 19.49 & 28.08 & 0.8499 & 28.61 G \\ \hline SVM & 22.68 & 32.25 & 0.8459 & 28.06 G \\ \hline FARIMA & 33.73 & 42.20 & 0.7142 & 55.62 G \\ \hline AT-GRU & 14.24 & 20.37 & 0.9552 & 25.03 G \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error comparison of different models.  
Figure 11: Model fitting effect.  
Figure 12: Model convergence performance diagram.  
of average absolute error, the error of the proposed algorithm is 26.9% lower than that of the GRU model, 37.2% lower than that of the SVM model, and 57.8% lower than that of the FARIMA model. In terms of root mean square error, the proposed algorithm is 27.5% lower than the GRU model, 36.8% lower than the SVM model, and 51.7% lower than the FARIMA model. Furthermore, the goodness of fit of the new model is better than that of the comparison models. Simulation results show that the proposed method can improve the accuracy of satellite network traffic prediction, reduce the prediction errors, and have the best fitting degree with real traffic data.
# AidA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV
###### Abstract  
Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.  
reinforcement learning; anytime path planning +
Footnote †: journal: Applied Sciences
## 3 Wind Farm Controller
### Wake Control Strategy
#### 3.1.2 Wake Redirection Control  
The objective of the WRC strategy is to redirect the wake away from the downstream turbine. In the intentional yaw misalignment method, the transverse force created with the incoming wind redirects the wake away from the downstream turbine [10]. As a result, the downstream turbine may generate more power, and the overall power output of the wind farm could be raised.  
Figure 11 illustrates a typical WRC strategy for a wind farm of two turbines [6]. The upstream turbine has a yaw offset such that the wake from the downstream turbine is redirected. The length of the arrow corresponds to the wind speed magnitude. The wake is redirected toward the yaw offset direction of the upstream turbine, affecting a portion of the downstream rotor. As depicted in the figure, the portion of the downstream turbine outside the wake experiences higher wind speed.
# Trends and Technological Challenges of 3D Bioprinting in Cultured Meat: Technological Prospection
## 4 Discussion
### Acceptance and Regulation  
In addition to the technical challenges to be overcome in the biofabrication of cultured meat, such as with respect to the appearance, structure, texture, flavor, and nutritional composition of cultured meat [132], cultured meat production must consider several social issues, including consumer appeal and acceptance [133; 134]. Acceptance of cultured meat has been the subject of studies in several countries [135; 136; 137]. [PERSON], [PERSON], and [PERSON] [138] performed a systematic review which aimed to identify, evaluate, and summarize the empirical results of published studies, providing a broad overview of recent empirical evidence on consumer acceptance of cultured meat. For the review, the authors considered the WoS, Science Direct, and Scopus databases throughout 2008-2020. After screening, 43 articles met the selection criteria defined by the authors. They concluded that the most important factors influencing consumer acceptance/rejection of cultured meat include public awareness, perceived naturalness, and food-related risk perception. Ethical and environmental concerns have made consumers willing to pay higher prices to purchase meat substitutes, but not necessarily cultured meat. In addition, food neophobias related to safety, health, and uncertainty also appear to be important barriers to wide acceptance of this technology.  
Another challenge facing the production of cultured meat that was evident in the present study (Figure 4) is the regulation of this product. Regulatory approaches differ substantially between countries and continents [139]. The Good Food Institute (GFI) has encouraged the development of alternative food sources among research groups and companies from different countries [140]. The GFI surveyed countries that are using existing food regulations or developing new regulations to evaluate novel products such as cultured meat [141]. Details of this survey by country and region are presented in Table 2.  
There is still no definitive regulation for the production and marketing of cultured meat. Many regulatory issues still need to be addressed through legislation and policies, such as food fraud and mislabeling. In recent years, administrative and public bodies have promoted debates and meetings with the aim of structuring appropriate regulation [142].
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Infrastructure Constraints  
The effective use of IoT technology in smart buildings is contingent upon the presence of a resilient infrastructure to provide connection and functioning. Current infrastructure may need substantial enhancements to accommodate the increased demand generated by IoT devices, which may be both expensive and logistically complex. The cost obligation of creating and maintaining the requisite infrastructure might hinder several enterprises seeking to adopt IoT technologies. Moreover, maintaining infrastructure in a current and operational state requires continual investment in maintenance and enhancements. Insufficient infrastructure may restrict the scalability and efficacy of IoT solutions, consequently obstructing the advancement of smart buildings and the attainment of their advantages.
# A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Turkiye
## 3 Ground-Motion Prediction Models (GMPMs)
### Regression Analysis  
We employed a two-stage regression method, starting with a linear mixed-effects regression analysis using the _lme4_ package in R [40; 41]. In the first stage, model coefficients were calculated, followed by a residual analysis with homoscedastic assumptions. In the second step, we relaxed the assumption of homoscedastic variance and applied a heteroscedastic variance model, where variance was dependent on magnitude.  
In the first stage, we regressed the coefficients of the model. The model included coefficients for earthquake event characteristics such as magnitude (\(c_{1}\), \(c_{2}\), \(c_{3}\), \(c_{4}\)\(c_{5}\)), depth (\(c_{6}\)), and style of faulting (\(c_{7}\), \(c_{8}\)). Terms for geometric spreading (\(d_{1}\), \(d_{2}\)), anelastic attenuation (\(d_{3}\)) to account for GMIM decay with distance, and site amplification (\(s_{1}\)) were also estimated using mixed-effects regression. Nonlinear site amplification terms were adapted from Ieen and Sandikkaya [19]. For distance scaling, we adjusted the \(R_{I\!B}\) parameter by adding a pseudo-depth term (\(h\)), set to 7 km for this analysis. Due to a strong trade-offbetween the magnitude-dependent geometric spreading term and the magnitude term, we first estimated the \(d_{2}\) term and constrained this coefficient. The regression was then repeated to estimate the style-of-faulting terms. Estimating these terms together led to unreliable coefficients due to tradeoff between magnitude and style-of-faulting terms. Once the style-of-faulting term was constrained, nonlinear site effects were excluded in this step. After constraining the \(h\), \(d_{2}\) and \(c_{7}\), \(c_{8}\) terms, we applied the nonlinear site amplification model from [PERSON] (19). High magnitudes tend to be underestimated because the database is dominated by low-to-moderate earthquakes. To address this, we first computed the \(c_{2}\) and \(c_{3}\) coefficients using the full database. Then, we computed the \(c_{5}\) coefficient using a limited database restricted to earthquakes with \(M_{W}>6\). Finally, we calculated the \(c_{1}\) coefficient using the full database. In this step, we represented the geometrical spreading and anelastic attenuation terms using the \(d_{1}\), \(d_{2}\), and \(d_{3}\) parameters. It is observed that distance scaling primarily affects shorter distances, where geometric spreading is more significant, while, at greater distances (\(R_{JB}>80\)), anelastic attenuation becomes more important. Anelastic attenuation is more prominent at higher frequencies and gradually decreases as frequency drops, eventually reaching zero.  
An initial analysis of variances under the assumption of homoscedasticity showed that ground-motion variability within and between events strongly depends on magnitude. To address this, a trilinear heteroscedastic variance model, which varies with both magnitude and period, was introduced to estimate ground-motion variability.
## 2 Methodology
### Forecasting Process  
According to the previous analysis, the accuracy of power grid investment forecasting will be affected by many factors, such as economic development, electricity demand, power grid scale, and power grid benefit. In order to achieve accurate forecasting of power grid investment, on the basis of the analysis of influencing factors for power grid investment forecasting, the DE-GWO-SVM forecasting model is proposed in this article. The steps are as follows:  
(1) Data Collection  
Collect sample data of 16 factors, including power grid investments over the years, GDP, industrial structure, population, urbanization rate, total electricity consumption, growth rate of electricity consumption, electricity sales, peak load of power grid, power transmission line length of 220 kV and above, power transmission line length of 110 kV and below, transformer capacity of 220 kV and above, transformer capacity of 110 kV and below, total profit of power grid enterprises, power supply load of unit power grid assets, electricity sales of unit power grid assets and electricity sales income of unit power grid assets. Deal the data with non-dimensional processing.  
(2) Influencing Factors Screening  
The grey relational degrees between the above 16 factors and the power grid investment are calculated, the main influencing factors for power grid investment forecasting are screened out according to the ranking of grey relational degrees, and the selected factors are used as the input of the forecasting model.  
(3) Power Grid Investment Forecasting Based on DE-GWO-SVM Model  
The selected influencing factors for power grid investment forecasting are used as the input of the model, and the DE-GWO-SVM model is used to predict the power grid investment. First, initialize the parameters and perform the mutation operation of the differential evolution algorithm for the population individual to produce the intermediate. Then perform the selection operation for initializing the population individual. The fitness values of grey wolf individuals are calculated and ordered to determine the positions of the grey wolf individuals with the first three fitness values. The distances between other grey wolf individuals and the first three grey wolves are calculated and the position of each grey wolf individual is updated. Update the value of the parameters and perform the crossover operation on the positions of the population individuals and preserve the better population individuals. Next perform the selection operation to produce new individuals and calculate the fitness values of all grey wolf individuals and update the positions of the grey wolf individuals with the first three fitness value. Repeat the above process until the number of iterations is reached. Finally, the optimized parameter values are assigned to SVM, and the forecasting model is established to predict the power grid investment.  
The specific forecasting process is shown in Figure 3.  
Figure 3: The flow chart of forecasting.
# Vision Transformer in Industrial Visual Inspection
## 4 Experiments and Results
### Results
#### 4.2.2 Sliding Window Approach  
In the sliding window version of the SM use case the DDETR outperforms the other models in all the evaluated performance metrics, followed by Retina Net in \(mAP_{50,test}\) and Retina Net, Retina Swin, as well as F-RCNN regarding \(F1\)-\(Score_{adjusted}\). The higher training batch size enables the DDETR to perform very close to 100%, while the other models do also show improvements they cannot match this. The DETR and Retina Next models also show strong improvements of their results of 30 to 40%p. There is also no performance gain in reducing the IoU threshold for DDETR, DETR, Retina Net and Yolo V3 in this use case. Compared to the SM use case with full images, the performance of all models improved significantly, because of the higher resolution input and increased pool of training samples.  
Retina Swin shows the best performance considering \(mAP_{50,test}\), \(F1\)-\(Score_{adjusted}\) in the sliding window version of the WLS use case with 0.7%p and 0.5%p better results than Retina Net. This indicates that for damage characterized by a wider color variety, such as on these wooden surfaces, the global feature relations modelling capability of the Swin transformer backbone is superior to the ResNet-50. Retina Next, as well as DETR, show significant improvements over their results on full images. DETR's convergence is still slower due to its lower inductive bias, compared to the sliding kernel of a CNN-like Retina Next, which assumes strong connections between local features.  
In the sliding window version of the CR use case, DDETR tests F-RCNN considering \(mAP_{50,test}\) by 5.5%p. Regarding \(F1\)-\(Score_{initial}\), the performances are very close, differingonly by 0.3%p between DETR and Yolo v3, while they are more distinct when looking at \(F1\)-\(Score_{adjusted}\) with 2.7%p difference. Retina Swin's performance is very similar to Yolo v3. The distinction between many and sometimes very similar letter shapes seems to be the strength of the DDETR detection head, possibly due to the different emphasis of the attention heads. As the inputs are greyscale images the seemingly limited feature extraction capability of the CNN backbones compared to Swin, noted in the sliding window version of the WLS use case, does not have negative impact here.  
Overall, the performance of all models is significantly improved by the sliding window approach, as to be expected with higher feature resolution and larger batch sizes due to smaller inputs, at the cost of additional computation. Additionally, there is less area of the inputs that does not contain any damages, which reduces the difficulty of the task.
## 2 Forward Model Development
### Diffuse Attenuation of Upwelling Radiance \(K_{Lu}\)
After inserting Equation (4) into Equation (2) and allowing for \(c=a+b\), the following is obtained:  
\[\frac{L_{u}(\theta_{s},\theta_{v},\phi)}{E_{od}}\cong\frac{\beta(\psi)}{a \big{(}1-\cos(\theta_{v})\Psi_{K_{Lu}}(\psi)\,\overline{\mu}_{\infty}{}^{-1} \big{)}+b-f_{L}(\theta_{s},\theta_{v},\phi)b_{f}}, \tag{5}\]  
\[\text{where }\Psi_{K_{Lu}}(\psi)=K_{Lu}/K_{\infty}=[1+F(\psi)]. \tag{6}\]  
The parameter \(\Psi_{K_{Lu}}\) is assumed spectrally independent with errors over the full range of possible water types estimated at \(<\)2% [40]. Dividing numerator and denominator of Equation (5) by \(b_{b}\), we obtain:  
\[\frac{L_{u}(\theta_{s},\theta_{v},\phi)}{E_{od}}\cong\frac{\beta(\psi)}{b_{b} }/\bigg{[}\frac{a}{b_{b}}\Big{(}1-\cos(\theta_{v})\Psi_{K_{Lu}}(\psi)\overline {\mu}_{\infty}{}^{-1}\Big{)}+f_{L}(\theta_{s},\theta_{v},\phi)\Big{(}1- \widetilde{b}_{b}{}^{-1}\Big{)}+\widetilde{b}_{b}{}^{-1}\bigg{]}, \tag{7}\]  
where \(\widetilde{b}_{b}=b_{b}/b\) is the backscattering ratio. For IOPs we now have the backward phase function in the numerator; in the denominator we have the recognizable \(b_{b}/a\), as well as \(\widetilde{b}_{b}\), an IOP that incorporates information on bulk particle composition (see Section 2.4; [41]), and is not typically associated with ocean color remote sensing vis-a-vis the common first order approximation of \(r_{rs}\) assumed proportional to \(b_{b}/a\).
## 2 Results  
### Improved Germination, Morphological and Physiological Traits  
The light quality positively influenced the seed germination and morphological and physiological traits (Figure 1A-D) of the studied plants. Among the four types of light treatment, the red and blue (1:1) light resulted in the highest germination percentage (Figure 2A). The combination of red and blue light also showed the highest performance in the fluorescence of photosystem II (Fv/Fm) compared to the single treatments with white, red, or blue lights (Figure 2B). The leaf greenness, measured as a SPAD score, was also the highest under the combined red and blue light (Figure 2C). The root and shoot lengths, as well as the seedling dry weight, showed significant differences among the four treatments, with the combined light exposure yielding the longest root and shoot lengths (Figure 2D-E). Similarly, the combined light treatment significantly enhanced the seedling dry weight (Figure 2F).  
Figure 1: Phenotypic variation in alfalfa seedlings under different light qualities. Alfalfa seedlings are grown under white (**A**), red (**B**), blue (**C**), and red combined with blue light (red+blue) (**D**). Photographs are taken after two weeks of seedling growth.