esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Reinforcement Learning
#### 2.2.3 Learning and Optimization  
The critic network and actor network structures are shown in Figures 3 and 4, respectively, and the implementation of the algorithm is illustrated in Figure 5. Weights are initialized using variance scaling for Tanh and Sigmoid, Xavier for ReLu, and elu, whereas layers with the linear activation function are initialized from a uniform distribution. The mean output layer \(\mu\) is initialized with \(\mu\) = 0 and \(\sigma\) = 0.5. The vehicle body velocity \(\dot{x}_{s}\) is utilized to represent the state of the suspension system and used as the input to both networks. The loss functions for both networks are minimized using the adaptive learning rate algorithm ADAM optimizer, which can be found in [19]. The learning rates for the critic network are chosen to be higher than the actor network such that the critic learns faster; thus, it produces accurate values for the states, which then help the actor learn and generate better actions. The learning rates are 0.01 and 0.001, respectively, and after a marked learning and improvement, the learning rates are decreased to 0.001 and 0.0001, respectively. In many cases, the experience \((s_{t},a_{t},r_{t},s_{t+1})\) is stored in a memory with a pre-defined size, and the networks' parameters are updated by computing the average gradient over sampled transitions at every time step. While this approach solves the problem of correlated states, however, it is not considered in this paper. Computing the gradients at each time step for the single experience was faster and more efficient since the road profile changes every 1.5 s. The value of the discount factor \(\gamma\) was chosen to be 0.99, and the activation functions used throughout our study are summarized in Table 2.  
Unfortunately, there is no standard method of choosing the number of neurons, the number of hidden layers, the types, and the order of activation functions. Therefore, we built and ran various models of the actor and critic multiple times until satisfactory performance was achieved. The best performing neural networks in this study are summarized in Table 3. The algorithm was implemented in Python 3.7 and trained using TensorFlow libraries. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Function** & **Derivative** \\ \hline Linear & \(f(x)=x\) & 1 \\ Tanh & \(f(x)=\frac{2}{(1+e^{-x})}-1\) & \(1-f(x)^{2}\) \\ Sigmoid & \(f(x)=\frac{1}{1+e^{-x}}\) & \(f(x)(1-f(x)\) \\ ReLu & \(f(x)=x\ for\geq 0\) & 1 \\  & \(f(x)=0\ for<0\) & 0 \\ elu & \(f(x)=x\ for\geq 0\) & 1 \\  & \(f(x)=\alpha(e^{x}-1)\ for<0\) & \(f(x)+\alpha\) \\ Soflmax & \(f(x_{i})=\frac{e^{x_{i}}}{\sum_{i=1}^{k}e^{x_{i}}}\) & \(f(x_{i})(\delta_{k}-f(x_{k})))\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of activation functions.
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 6 Asymptotic Performance Analysis  
In order to analyze the diversity order of the considered asymmetric dual-hop MIMO mixed RF/FSO DF relaying system, we need to further study the asymptotic behavior when \(\tilde{\gamma}_{RF},\tilde{\gamma}_{FSO}\rightarrow\infty\).  
For the RF link, the exponent function can be expanded into series form. We can rewrite the CDF of \(\gamma_{\text{RF}}\)  
\[\begin{split} F_{\gamma_{RF}}(\gamma_{RF})&=\exp \biggl{(}-\frac{\gamma_{RF}}{\tilde{\gamma}_{RF}}\bigg{)}\bigg{[}\exp\biggl{(} \frac{\gamma_{RF}}{\tilde{\gamma}_{RF}}\bigg{)}-\sum_{u=0}^{N_{\text{t}}N_{ \text{r}}-1}\frac{1}{u!}\bigg{(}\frac{\gamma_{RF}}{\tilde{\gamma}_{RF}}\bigg{)} ^{u}\bigg{]}\\ &=\bigg{[}\sum_{u=0}^{\infty}\frac{1}{u!}\bigg{(}-\frac{\gamma_{RF}}{ \tilde{\gamma}_{RF}}\bigg{)}^{u}\bigg{]}\bigg{[}\sum_{u=N_{\text{t}}N_{\text{ t}}}^{\infty}\frac{1}{u!}\bigg{(}\frac{\gamma_{RF}}{\tilde{\gamma}_{RF}}\bigg{)}^{u} \bigg{]}.\end{split} \tag{26}\]  
We neglect the higher order terms. The asymptotic outage probability of the RF link can be derived as  
\[F_{\gamma_{RF}}(\gamma)\approx\Bigg{[}\frac{(N_{\text{t}}N_{\text{r}}!)^{-N_{ \text{t}}N_{\text{r}}}}{\gamma}\tilde{\gamma}_{RF}\bigg{]}^{-N_{\text{t}}N_{ \text{r}}}. \tag{27}\]  
For the PSO link, the series representation of the Meijer's G function is given by ([16] [Equation (07.34.06.0006.01)]) into  
\[\begin{split}& G_{p,q}^{m,q}\bigg{[}z_{1}^{\underline{a_{1}}, \ldots,a_{n_{\text{r}}},a_{n_{\text{r}}+1},\ldots,a_{p}}^{\underline{a_{1}}, \ldots,a_{n_{\text{r}}}}\bigg{]}\\ &=\sum_{k=1}^{m}\frac{\prod_{j=1,j\
eq k}^{m}\Gamma(b_{j}-b_{k}) \prod_{j=1}^{q}\Gamma(1-a_{j}+b_{k})}{\prod_{j=n+1}^{p}\Gamma(a_{j}-b_{k}) \prod_{j=m+1}^{q}\Gamma(1-b_{j}+b_{k})}z^{\underline{b}}(1+O(z)).\end{split} \tag{28}\]  
Considering that \(\alpha_{\text{s}}>\beta_{\text{s}}\), the asymptotic outage probability of the FSO link can be obtained as follows  
\[\begin{split}& F_{FSO}(\gamma_{FSO})\approx\frac{2^{\alpha_{\text{s}} +\beta_{\text{s}}-2}}{\pi\Gamma(\alpha_{\text{s}})\Gamma(\beta_{\text{s}})} \frac{\Gamma(\frac{\alpha_{\text{s}}-\beta_{\text{s}}}{2})\Gamma(\frac{\alpha_{ \text{s}}-\beta_{\text{s}}+1}{2})\Gamma(\frac{\beta_{\text{s}}}{2})}{\Gamma( \frac{1+\beta_{\text{s}}}{2})}\\ &\times\bigg{(}\frac{\alpha_{\text{s}}\beta_{\text{s}}}{4}\bigg{)}^ {MN\beta}\bigg{(}\frac{\tilde{\gamma}_{FSO}}{\gamma_{FSO}}\bigg{)}^{-MN\beta/2}. \end{split} \tag{29}\]The asymptotic approximation of the end-to-end OP is given by  
\[P_{out}\leq(\gamma_{RF})^{-N_{t}N_{r}}+(\gamma_{FSO})^{-MN\beta/2}. \tag{30}\]  
Therefore, the diversity gain of the considered asymmetric dual-hop MIMO mixed RF/FSO DF relaying system \(d=\min\{N_{t}N_{r},MN\beta/2\}\).
# Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit
## 2 Problem Statement and Equations of Motion
### Parking Orbit  
In designing the multiple-object flyby trajectory, several prerequisites should be clarified. First of all, let us determine the type of the parking orbit in this multiple-SSO-object flyby mission. This paper identifies the following requirements in selecting the parking orbit:  
1. The parking orbit should be long-term stable.
2. A small velocity increment is demanded for the spacecraft to transfer from the parking orbit to flyby multiple SSO targets.
3. The apogee of the parking orbit should be large so that it would be easier for the spacecraft to adjust the orbital inclination, which is more favorable for multiple-object near-coplanar flyby missions.  
Within the cislunar space, typical potential candidates include Geosynchronous Earth Orbit (GEO), Distance Retrograde Orbit (DRO), Polar Lunar Orbit (PLO), Near Rectilinear Halo Orbit (NRHO), Lagrange point 1 (L\({}_{1}\)) halo orbit, Lagrange point 2 (L\({}_{2}\)) halo orbit, and so on.  
Table 1 compares the velocity increments for transferring from LEO to different target orbits. Note that without considering the perturbations, the velocity increment transferring from LEO to a designated target orbit is nearly identical to that transferring back. As shown in Table 1, the minimum velocity increment for transferring from LEO to the 2:1 DRO using lunar gravity assist and weak stability boundary is 3.1934 km/s. This is a rather small number compared to the velocity increment budgets transferring to GEO, PLO, NRHO, L  
halo orbit, and L\({}_{2}\) halo orbit. Therefore, this paper chooses DRO as the parking orbit for the spacecraft.  
This section does not specify specific parameters of DRO in this multiple-debris flyby mission. This problem will be discussed in the following sections.
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Operational Parameters
#### 3.2.3 Operating Pressure  
The opening of the cavern implies a modification of the natural tensional state, inducing tensions that must be regulated with the pressure of the stored gas. To achieve the geomechanical safety of the cavern, the pressure of the gas inside the cavern must remain between maximum and minimum pressure values. If the pressure rises above the maximum pressure, fractures may appear in the rock with gas migration channels. If the pressure drops below the minimum pressure, dilatation phenomena occur with the opening of microfractures that increase the volume and permeability of the rock, decreasing the volume of the cavity and endangering its stability.  
The establishment of working pressure limits is based on conventional criteria and geomechanical studies of the site. Values of 85% and 80% of the lithostatic pressure are proposed for the maximum pressure, whereas the values of 30% and 24% of the overburden pressure are proposed for the minimum pressure [7; 11; 12].  
Adopting the most conservative criterion, the maximum and minimum operating pressures will be, respectively (Equations (6) and (7)):  
\[\mathrm{P}_{\mathrm{max}}=0.8\times\mathrm{P} \tag{6}\]  
\[\mathrm{P}_{\mathrm{min}}=0.3\times\mathrm{P} \tag{7}\]  
For the pressure calculated above, a maximum value of 15.84 MPa (158 bar) and a minimum of 5.94 MPa (59 bar) is obtained. These pressures are within the usual operating ranges 100-270 bar for the maximum and 35-90 bar for the minimum [11; 23; 25].
# Multifunctional Modifying Systems Based on Ionic Liquids for Epoxy Resin Systems and Composites
## 3 Results and Discussion
### Fire Performance  
#### 3.4.1 UL-94 and LOI  
The final stage of the research was to evaluate the ability of the produced ionic liquid-based modification systems to flame retard the epoxy resin. For this purpose, the UL-94 test was first performed and the LOI was determined, the results of which are summarized in Table 7. Tests were conducted for resin samples without reinforcement. The obtained results allow us to assume that at a concentration of 10% by weight in the epoxy material, the tested modifying systems do not show significant flame-retardant properties towards the resin, which, considering the actual amount of phosphorus and silicon introduced into the system, was an expected result. Nevertheless, some dependencies are observed between the modifying system and the flame-retardant properties. Although, according to the classification, all materials received the HB rating, the LOI value increased to 30 V/V%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **E’ at 28 \({}^{\circ}\)C** & **E’ at 75 \({}^{\circ}\)C** & **\(\Delta\)E’** \\  & **[MPa]** & **[MPa]** & **[MPa/\({}^{\circ}\)C]** \\ \hline R & 14,971 & 12,285 & 9.8 \\ A & 6144 & 5699 & 15.2 \\ B & 8502 & 7817 & 55.4 \\ C & 7139 & 6639 & 10.9 \\ D & 11,677 & 7140 & 104.0 \\ E & 6721 & 6391 & 7.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Storage modulus E’ for carbon-reinforced composite samples of the reference and samples modified with ionic liquids.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **T\({}_{\text{g, DSC }}\)*** [\({}^{\circ}\)C]** & **T\({}_{\text{g}}\) from E’ [\({}^{\circ}\)C]** & **T\({}_{\text{g}}\) from tan \(\delta\)** [\({}^{\circ}\)C]** & **T\({}_{\text{g}}\) from E’ [\({}^{\circ}\)** [mol/cm\({}^{-3}\)]** \\ \hline R & 166 & 68 & 94 & 89 & 1.2 \\ \hline A & 89 & 102 & 118 & 109 & 3.8 \\ \hline B & 84 & 99 & 113 & 114 & 2.3 \\ \hline C & 98 & 104 & 120 & 110 & 3.5 \\ \hline D & 112 & 74 & 84 & 71 & 2.1 \\ \hline E & 109 & 119 & 131 & 127 & 3.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Glass transition temperatures and crosslinking density of the reference and samples modified with ionic liquids. Glass transition for pure epoxy resin with ionic liquids determined with DSC. Glass transition temperatures and crosslinking density for carbon-reinforced composite samples determined with DMA.  
for the E system. The result is also interesting because it is the E system that contains the smallest total amount of phosphorus and silicon. It is assumed that in the case of the tested systems, the oxygen index was influenced not so much by the content or presence of silicon and phosphorus, but by the extensive structure of the phosphonium ionic liquid molecule with numerous long alkyl chains.
# Fatigue Crack Propagation under Christmas Tree Load Pattern
## 2 Numerical Model
### Load Patterns  
Four different load patterns were applied to the specimen, covering both constant and variable amplitude loading, as shown in Figure 2. Both the Christmas Tree (15-9) and the Christmas Tree (9-3) patterns are variable-amplitude loading patterns, where high cyclic loading is embedded with many small cycles, ranging from 6 N to 60 N. The load pattern Christmas Tree (15-9) is defined by increments of +15 N and \(-\)9 N during the global increase in the load. During the global decrease in the load, the pattern contains increments of \(-\)15 N and +9 N. On the other hand, the load pattern Christmas Tree (9-3) is defined by increments of +9 N and \(-\)3 N during the global increase in the load. During the global decrease in the load, the pattern contains increments of \(-\)9 N and +3 N. The difference between these two loadings is that Christmas Tree (15-9) has steeper unloads (see Figure 2). In addition to the variable-amplitude loading patterns, two different load patterns with constant amplitude were also evaluated. The low-frequency loading pattern ranges from 6 N to 60 N, which are the limits of both Christmas Tree patterns, as shown in Figure 2. The load in the high-frequency pattern ranges from 6 N to 21 N, which represents the maximum load range of each load cycle of the Christmas Tree (15-9) load pattern.  
Both the Christmas Tree patterns and the low-frequency loading pattern require 17 s to define the pattern (load block), while the high-frequency pattern only requires 1 s (see Figure 2). Nevertheless, considering each load cycle applied in the patterns, the load frequency is \(f=1\) Hz for all load patterns, except for the low-frequency loading pattern which has a load frequency of \(f=0.058\) Hz. Considering constant-amplitude loading conditions, the FCG rate is usually defined by the calculation of crack growth per cycle. Nevertheless, a time-based formulation is more suitable for general variable random loading [26]. The basic concept of the time-based FCG rate is \(da/dt=(da/dN)f\), with \(a\) being the crack length and \(N\) the number of load cycles.  
Figure 2: Four different load patterns used in the numerical simulation (CA: constant amplitude; VA: variable amplitude).
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 7 RAC1B as a Prognostic Marker in Cancer
### Thyroid Cancer  
RAC1B is expressed in thyroid and overexpressed in 46% of papillary thyroid carcinomas (PTCs). RAC1B overexpression was significantly associated with both _BRAF-V600E_ mutation and poor clinical outcome. Whereas _BRAF-V600E_ alone did not associate with patient outcome, the association of RAC1B overexpression with _BRAF-V600E_ was overrepresented in the group with poorer clinical outcome. These data suggest that RAC1B and B-RAF-V600E may interact to worsen the prognosis of PTC patients [72; 73].  
In follicular thyroid carcinomas (FTCs) RAC1B was found to be overexpressed in 33% of carcinomas while no RAC1B overexpression was documented among follicular adenomas. RAC1B overexpression was significantly associated with both the presence of distant metastases and poorer clinical outcome, suggesting that RAC1B overexpression in FTCs (as in PTCs) is associated with poor outcomes. Furthermore, the lack of RAC1B overexpression in follicular adenomas reveals its potential as a molecular marker for preoperative differential diagnosis of thyroid follicular lesions [72].
###### Abstract
Another essential quality parameter for maize, which is affected by drying, is the grain colour. The colour of maize is evaluated using the CIE Lab colour space. It was shown that slight changes in maize colour could occur during an extended drying time for maize [16]. Maize producers in the tropical and subtropical regions face difficulty in drying the harvested maize to the desired moisture content for storage [17]. High humidity and frequent rainfall during the harvesting season delay the drying process and can result in a loss of quality by enhancing mould infestation [7; 18]. A moisture content of <14% is necessary to keep the \(a_{w}\) of maize grain below the safe storage level of 0.6 [19]. In humid and sub-humid tropical regions, maize is harvested with a moisture content around 30% w.b., necessitating drying before safe storage. In the district of Wakiso, conventional sun drying is a common practice for maize drying, which is done by spreading grains on a mat to expose them to sunlight [20]. The shortcomings of this method are an uneven moisture removal; exposure to insects, rodents, birds and dust; and failure to reach a safe moisture content due to a high dependency on the weather conditions [21]. An alternative and affordable method for smallholder farmers is the use of solar dryers. There are different types of solar driers used to dry cereals in Africa, but due to economic and technological constraints, their application has been obscured [22]. The solar tunnel dryer is a forced-convection direct- type solar dryer that was developed at the University of Hohenheim [23]. It was first applied to dry fruits and showed advantages by significantly reducing the drying time and mass loss while protecting the crops from dust, rain, animals, and insects [24]. Its application in cereal drying, initially with paddy rice, was promising, but improvements were needed, such as the ability to mix the grains to achieve a homogeneous drying process [25]. The inflatable solar dryer was developed in collaboration with the International Rice Research Institute (IRRI) as an improved version, which is adapted to cereal crops in tropical regions [26; 27]. [PERSON] et al. [28] conducted experiments on the use of an inflatable solar dryer to dry maize. Assuming a thin layer drying model, they proved that the diffusion approach fitted their results better. The objective of this study was to assess the performance of an inflatable solar dryer (ISD) compared to open sun drying, referred to as direct sun drying (DSD), for maize drying with regard to the product quality. The quality parameters assessed were the moisture content, mycotoxins (total aflat), yeasts, and moulds, as well as maize colour. The results of this research allowed us to draw conclusions on the use of an inflatable solar dryer in the postharvest handling of maize and recommendations on the future actions for maize handling to avoid contamination in the value chain.
## 4 Radiometric Processing Workflow
### Scene Reflectance Generation
#### 4.3.3 Atmospheric Correction  
Section 4.3.1 described how ARTMs can be used to simulate the irradiance to calculate reflectance. Additionally, ARTMs are widely used for correcting multispectral and hyperspectral imagery satellite and airborne data [102; 159] and high-altitude UAV images [125; 126] for atmospheric influence on the path from the object to the sensor. Recent modeling studies suggest that atmospheric correction is important for very precise radiometric measurements, e.g., to estimate solar-induced chlorophyll fluorescence [160]. For reflectance studies, a detailed analysis is still missing. Thus, atmospheric influences should be considered in each application [27; 131]. An easy approach to normalize the influence of the atmosphere is to use the ELM.
## 6 A Method to Calculate \(\boldsymbol{k_{b/m}}\)
### Condition 2: \(p_{i}\leq 1\)  
Combining Equation (22) with \(p_{i}\leq 1\) leads to:  
\[\frac{\hat{\phi}_{b}\cdot\hat{n}_{b}+\hat{\phi}_{m}\cdot(2-\hat{n}_{m})}{2\cdot \left(\hat{\phi}_{b}+\hat{\phi}_{m}\right)}\leq 1 \tag{35}\]  
Looking at Equation (28), we can write:  
\[\hat{\phi}_{b}\cdot\hat{n}_{b}+\hat{\phi}_{m}\cdot(2-\hat{n}_{m})\leq 2\cdot \left(\hat{\phi}_{b}+\hat{\phi}_{m}\right) \tag{36}\]  
Now, we substitute Equation (24) in Equation (36) to get:  
\[\frac{k_{b}\cdot MOS_{b}\cdot\hat{n}_{b}}{J_{b}}+\frac{k_{m}\cdot MOS_{m}\cdot (2-\hat{n}_{m})}{J_{m}}\leq 2\cdot\left(\frac{k_{b}\cdot MOS_{b}}{J_{b}}+ \frac{k_{m}\cdot MOS_{m}}{J_{m}}\right) \tag{37}\]  
Again, we multiply the whole inequation by \(\frac{1}{k_{m}}\) (\(\forall\ k_{m}\ \in\ \mathbb{R}^{+}\)) and rename \(\frac{k_{b}}{k_{m}}\) by \(k_{b/m}\):  
\[\frac{k_{b/m}\cdot MOS_{b}\cdot\hat{n}_{b}}{J_{b}}+\frac{MOS_{m}\cdot(2-\hat{ n}_{m})}{J_{m}}\leq 2\cdot\left(\frac{k_{b/m}\cdot MOS_{b}}{J_{b}}+ \frac{MOS_{m}}{J_{m}}\right) \tag{38}\]  
and removing \(2\cdot\left(\frac{MOS_{m}}{J_{m}}\right)\) from both sides, we reach to:  
\[\frac{k_{b/m}\cdot MOS_{b}\cdot(\hat{n}_{b}-2)}{J_{b}}\leq\frac{MOS_{m}\cdot \hat{n}_{m}}{J_{m}} \tag{39}\]  
Now, before we continue we will find out which is the sign of the expression \((\hat{n}_{b}-2)\). \(\hat{n}_{b}\) was defined in Equation (20) as the relation between the number of I+P frames received from the best path (\(n_{rb}\)) and the total number of I+P frames sent (\(n_{s}\)).  
\[\hat{n}_{b}=\frac{n_{rb}}{n_{s}}=\frac{n_{rb}}{n_{sb}+n_{sm}}\leq 1 \tag{40}\]  
since \(n_{rb}\leq n_{sb}\) (some I+P frames might have been lost through the best path) and \(min(n_{sm})=0\) (that minimum value is obtained when no frame was sent through the medium-quality path).  
Therefore,  
\[\hat{n}_{b}-2\leq-1 \tag{41}\]  
Finally, the second inequation to be fulfilled by \(k_{b/m}\) is:  
\[k_{b/m}\geq\frac{MOS_{m}\cdot J_{b}\cdot\hat{n}_{m}}{MOS_{b}\cdot J_{m}\cdot (\hat{n}_{b}-2)} \tag{42}\]
## 4 Simulation
### Numerical Results
The sizes of the positioning areas when using these methods--SLMAT, NHexCurve, and SCAN-ET--are very similar, because the three reference points in the triangulation method they selected are the vertices of an isosceles triangle. Similarly, the positioning size of the positioning areas of i-SCAN and MARS are very similar, because they use the CAPPS method to compute the size of the positioning area. CAPPS uses multiple reference points to prune the smaller overlapping coverage area in order to refine the positioning accuracy. The positioning size of the positioning areas of i-SCAN and MARS methods are 5.14 m\({}^{2}\)-7.9 m\({}^{2}\) and 4.31 m\({}^{2}\)-7.47 m\({}^{2}\), respectively. Their areas are about 53% to 74% smaller than when using the SCAN method. The positioning procedure in all of the compared methods is able to directly obtain the data for the reference points or positions. Thus, the time complexity is O(1). Although the ISP algorithm shows that each unknown node needs to scan the set of unpositioned nodes, the unknown node provides this information by exchanging it with its neighbors. Their differences are the procedure for scheduling routes in the deployment area. In an N \(\times\) N deployment area with a communication range R, SCAN, i-SCAN, SCAN-ET, and MARS need to scan \(\frac{\mathrm{N}}{\mathrm{\kappa}\mathrm{\sqrt{2}}}\) line segments, that is, O(N). SLMAT and NHexCurve consider the equilateral triangle, so they need to scan \(\frac{\mathrm{N}}{\mathrm{\kappa}\mathrm{\sqrt{2}}}\) line segments, which is also O(N).