esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Space-Segment Optimization Aspects
### Applicable Optimization Methods for Space Segment
#### 6.8.2 Particle Swarm Optimization (PSO)  
PSO, originally introduced in [50], is another popular evolutionary algorithm that iteratively optimizes a problem by improving upon candidate solutions with respect to given metrics. The set of possible solutions are called the swarm and particular candidate solutions are called the particles, which have position and velocity vectors for each dimension of the particle (which can be defined as parameters of interest). Each particle's movement is influenced by both its individual best and the best within the swarm, which is expected to move the swarm toward an optimal solution.  
Algorithm 2 summarizes the PSO algorithm, and we provide the velocity update equation in Equation (14) and the position update equation in Equation (15), both of which are given for particle \(i\) and dimension \(d\). \(t\) refers to time (or iteration), \(w_{1}\), \(w_{2}\), and \(w_{3}\) are scalar weights, \(v\) refers to the vehicle speed vector in 3D space, \(\mathbf{x}\) refers to the current position (vector form), \(\mathbf{p}_{i}\) is the best known position of particle \(i\), \(\mathbf{p}_{g}\) is the best known position in the swarm, and \(||\cdot||\) is the Euclidean metric or the distance between vectors.  
\[v_{i,d}(t+1)=w_{1}||\mathbf{v}_{i,d}(t)||+w_{2}||\mathbf{p}_{i}-\mathbf{x}_{i, d}(t)||+w_{3}||\mathbf{p}_{g}-\mathbf{x}_{i,d}(t)|| \tag{14}\]  
\[\mathbf{x}_{i,d}(t+1)=\mathbf{x}_{i,d}(t)+\mathbf{v}_{i,d}(t+1) \tag{15}\]  
Similarly with the GA, the PSO also have variants; the straightforward one is the expansion to the multi-objective optimization MOPSO[54]. More complex improvements work by including extra heuristics or mathematical strategies to guide the particle's through the search space in a more informed manner; an example seen in [55] uses different re-sampling methods in a hybrid manner to achieve this, similar to particle filtering applications.  
```
Step 1: Swarm Initialization Initialize the swarm with \(N\) particles, based on problem range and constraints. Randomly initialize their positions and velocities within the search space. Step 2: Fitness Function Evaluation Evaluate the value of the fitness (objective) function for each particle in the swarm. Update the particle best and swarm best for each particle. Step 3: Position and Velocity Iteration Iterate the position and velocity of each particle according to the Equations (14) and (15). Enforce limits if any particle goes out-of-bounds of the search space. Step 4: Termination Repeat steps 2-3 until a termination criteria is met (i.e., total iterations, change in optimality metric, etc.).
```  
**Algorithm 2**PSO  
As another evolutionary algorithm, PSO has almost identical overhead properties as GA. However, while the traditional PSO is typically faster to converge to a solution compared with GA[148], it also has certain 'pitfalls' that can prevent or significantly alter the required number of iterations to reach a solution (i.e., particles getting stuck in the local minimum or maximum).
## 5 Conclusions  
To meet a high quality of tracking requirements, as well as attenuate the uncertainty and noise, of aircraft turbofans, a new tracking control design is developed in this paper. The tracking control system is modeled as three parts: the nominal system, the uncertainty and the reference model. The tracking performances, UTB and UUTB, are built. For this tracking control system, the robust control is proposed based on \(H_{\infty}\) control and the Leitmann approach. The significance of the work is two-fold. Firstly, the \(H_{\infty}\) control guarantees the baseline performances, namely the asymptotical stability and the noise suppression of the nominal system. Secondly, in the presence of uncertainty, the performances of UTB and UUTB are further achieved by the collaboration of the Leitmann approach and the \(H_{\infty}\) control. The new approach is applied to design the multi-variable controller for an aircraft turbofan. The computer simulations validate that: (1) when the dynamics of the controlled system varies in the flight envelope, the new approach could guarantee the robustness of the closed-loop system and the tracking bounds of \(N_{L}\) and \(\pi_{T}\) are 0.23% and 1.9%; (2) in comparison with the classical robust controls, \(H_{\infty}\) and \(LQR\), the new approach shows the best performances in the resistance of uncertainty and noise. The \(H_{\infty}\) control can realize stable robust control in all four operating points of the turbofan, but the bounds of the state responses are around 200% or even more than those of the new approach. The \(LQR\) control can only work at the parts of the operating conditions. At these working operating conditions, the state step responses show a limited ability at reducing the influences of uncertainty and noise. The results of this paper might encourage further hardware-in-loop simulations and physical tests of the new designed controlled turbofan system.  
This work was supported by (1) the National Natural Science Foundation of China (No. 51406084) and (2) the Jiangsu Province Key Laboratory of Aerospace Power System(NJ20160020).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived the new robust control approach and the control scheme. [PERSON] and [PERSON] designed the new robust controller. [PERSON] built the linear model of the turbofan engine and conducted the simulation. [PERSON] and [PERSON] analyzed the simulation results and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: Magnitudes in different conditions during \(\pi_{T}\) steps.
# Overexpression of _RrGGP2_ and _RrDHAR_ Increases Ascorbic Acid Content in Tomato
## 3 Results
### Analysis of Genes Expression in Transgenic Lines  
#### 3.3.1 Analysis of Genes Expression in Transgenic Tomato Fruits  
The overexpression of _RrGGP2_ and _RrDHAR_ affected the expression of a range of genes related to the biosynthesis, catabolism, and recycling of AsA in transgenic tomato line fruits. In four tomato lines overexpressing _RrGGP2_, the expression of _SIPMI_, _SIGME_, _SIGGP_, _SIAO_, _SIDHAR_, and _SIMDHAR_ were upregulated, and _SIGPI_, _SIPMM_, _SIGMP_, _SIGPP_, _SIGLDH_, and _SIAPX_ were downregulated; _SIGDH_ was significantly upregulated in three lines, while in line G7 it was significantly downregulated (Figure 4A). Furthermore, in  
Figure 3: Determination of AsA and DHA contents in tomato lines overexpressing _RrGGP2_ and _RrDHAR_. (**A**) AsA and DHA contents in transgenic _RrGGP2_ fruits; (**B**) AsA and DHA contents in transgenic _RrDHAR_ fruits; (**C**) AsA and DHA contents in transgenic _RrGGP2_ leaves; (**D**) AsA and DHA contents in transgenic _RrDHAR_ leaves. Three replicate experiments were performed; error bars represent standard error, means \(\pm\) SE; FW—fresh weight. The asterisks represent significant differences from WT (* \(p\) < 0.05; ** \(p\) < 0.01).  
the four tomato lines overexpressing _RrDHAR_, the expression of _SIGMP_, _SIGME_, _SIGLDH_, _SIAO_, _SIAPX_, _SIDHAR_, and _SIMDHAR_ was upregulated, while _SIGPI_, _SIPMI_, _SIGGP_, and _SIGPP_ were downregulated. The expression of _SIPMM_ and _SIGDH_ was not significantly different from the control (Figure 4B).  
Figure 4: _Cont_.
###### Abstract  
Landslides are a serious geohazard worldwide, causing many casualties and considerable economic losses every year. Rainfall-induced shallow landslides commonly occur in mountainous regions. Many factors affect an area's susceptibility, such as rainfall, the soil, and the slope. In this paper, the effects of rainfall intensity, rainfall pattern, slope gradient, and soil type on landslide susceptibility are studied. Variables including soil volumetric water content, matrix suction, pore water pressure, and the total stress throughout the rainfall were measured. The results show that, under the experimental conditions of this paper, no landslides occurred on a \(5^{\circ}\) slope. On a \(15^{\circ}\) slope, when the rainfall intensity was equal to or less than 80 mm/h with a 1 h duration, landslides also did not happen. With a rainfall intensity of 120 mm/h, the rainfall pattern in which the intensity gradually diminishes could not induce landslides. Compared with fine soils, coarser soils with gravels were found to be prone to landslides. As the volumetric water content rose, the matrix suction declined from the time that the level of infiltration reached the position of the matrix. The pore water pressure and the total stress both changed drastically either immediately before or after the landslide. In addition, the sediment yield depended on the above factors. Steeper slopes, stronger rainfall, and coarser soils were all found to increase the amount of sediment yield.  
landslides; artificial rainfall; grain size; rainfall pattern; pore water pressure Article The Effects of Rainfall, Soil Type and Slope on the Processes and Mechanisms of Rainfall-Induced Shallow Landslides.
# The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
## 2 System Models  
### Network Model  
We suppose that there are \(n\) nodes randomly distributed in a three-dimensional area with edge \(L=\Theta(n^{\frac{\gamma}{3}})\), where the network volume is \(A=\Theta(n^{\gamma})\), where \(0\leq\gamma\leq 1\) is the parameter of network size, if \(\gamma=0\), the network is a dense network, and \(\gamma=1\), the network is an extended network. Each node randomly chooses a destination to which it wishes to send packets. All nodes follow a heterogeneous distribution which is called the Shot Noise Cox Process (SNCP). The SNCP can be described briefly as follows: we assume there are \(M\) clusters randomly and uniformly scattered over the network area, where \(E(M)=m\). Let \(C=\{c_{j}\}_{j=1}^{M}\) be the center points of these clusters. Each center point independently generates a point process with a density of \(q_{j}k(c_{j},\xi)\) at location \(\xi\), where \(k(c_{j},\cdot)\) is a dispersion density function and \(q_{j}\) equals the average number of nodes contained by cluster \(c  
In the large scale wireless network, we assume the average number of cluster centers scale as \(m=\Theta(n^{\upsilon})\), with \(v\in[0,1]\). Then, the number of nodes in each cluster is \(\Theta(n^{1-v})\), i.e., \(q_{j}=\Theta(n^{1-v})\) for \(j=1,2,\ldots,M\), since \(\int_{A}k(c_{j},\xi)d\xi\) is finite.  
Let \(d_{c}\) be the average distance between two neighboring cluster centers. Then, we have  
\[d_{c}=\Theta\left(\left(\frac{A}{m}\right)^{\frac{1}{3}}\right)=\Theta\left(n ^{\frac{\gamma-\upsilon}{3}}\right). \tag{3}\]  
Notice that if \(\gamma<v\), the quantity \(d_{c}\to 0\) as \(n\) goes to infinity. This case is called the cluster-dense regime, as shown in Figure 1a. On the contrary, if \(\gamma>v\), the quantity \(d_{c}\to\infty\). This case is named the cluster-sparse regime, as shown in Figure 1b. The network with the cluster-dense regime appears slightly heterogeneous and its capacity is similar to the homogeneous Poisson process (HPP) network. In this paper, we study the effects of topology and heterogeneity on the throughput capacity. Thus, we focus on the cluster-sparse model, i.e., \(d_{c}\to\infty\). Correspondingly, let \(\bar{\Phi}\) and \(\underline{\Phi}\) denote the maximum node density and the minimum node density, respectively.
## 2 Graph-Based Smoothness Harmonic  
### Time-Varying Brain Activity as a Graph  
Before introducing the concept of smoothness harmonics, it is necessary to model time-varying brain activity in terms of a static graph. Let \(\mathcal{G}_{t}=(\mathcal{V},\mathcal{E})\) be an undirected graph on time \(t\), where \(\mathcal{V}\) represents the set of time-varying nodes, which can be represented as an \(N\)-dimensional feature representation. A graph signal can be represented by a function \(f:\mathcal{V}\rightarrow\mathbb{R}^{n}\) that assigns a scalar value to each node at time \(t\). The nodes are interconnected by a set of edges \(\mathcal{E}\subset\mathcal{V}\times\mathcal{V}\), which can also be characterized as an \(N\times N\) feature representation, i.e., \(\mathcal{E}\in\mathbb{R}^{N\times N}\).  
In time-varying brain activity, the nodes \(\mathcal{V}\) can be interpreted as the brain activation patterns in the region-of-interest (RoI), while the edges represent the connectome between RoIs (Figure 1). Thus, a graph signal \(f\) can be represented as an \([N,T]\) matrix to express the cortical dynamics over \(N\) RoIs throughout \(T\) time points. Importantly, we choose to utilize a static graph (\(\mathcal{E}\)) instead of a dynamic graph that assumes the graph structures evolve over time (\(\mathcal{E}_{t}\)) to represent time-varying brain activity. This preference for a static graph over a dynamic one arises for two reasons: Firstly, the validity and reliability of estimating dynamic functional connectomes are still under question, and it remains uncertain whether these estimated time-varying graph structures truly reflect the underlying neurophysiological processes [15; 16]. Secondly, from an application perspective, employing a static graph significantly reduces the computational cost compared to the heavy computational burden associated with dynamic graph analysis.
## 3 Implementation  
### Model Overview  
A prototype of the GenoMus framework (which is an open-source project freely available at [[https://github.com/lopezmontes/GenoMus](https://github.com/lopezmontes/GenoMus)]([https://github.com/lopezmontes/GenoMus](https://github.com/lopezmontes/GenoMus)) accessed on 15 August 2022), has been implemented by using JavaScript runtime Node.js(r) for the core code, connected with Cycling74 Max 8 to interact in real-time with the results of generative processes. The computer-aided composition package back for Max, by [PERSON], and [PERSON] [26], allows a simple and responsive visualization of experimental results as interactive music scores, which can also send data to synthesizers or music editors using MIDI, OSC, or any custom format. The data structure of the GenoMus phenotypes is close to the hierarchical structure of music employed by back package [27], based on scores, voices, events, and event multiparameters. Genotypes' functional expressions are displayed in an embedded text editor, similar to the system created by [PERSON] [28], also based on bioinspired music patterns evolution.  
From the Max patch, organized as shown in Figure 2, it is possible to control the generative and evolutive processes in many ways through these actions:  
* Display the generated music as an editable and interactive score, whose data can be converted to sound inside Max, or sent to external software via MIDI, OSC, or custom formats.
* Mutate the current specimen changing stochastically only leaf values of the functional tree, applying new seeds for random processes, replacing complete branches, etc.
* Display decoded genotypes as editable text, so they can be manually edited from scratch.
* Set constraints to the generated specimens, limiting the length, polyphony, depth of the functional tree, forcing the inclusion of a function, etc.
* Define conditions and limitations for the search process.
* Edit germinal conditions, including the graphical edition of the germinal vector.  
Figure 1: Mappings from germinal conditions to musical outputs. Double-ended blue arrows illustrate the retrotranscription feature of the \(tran\) conversion: any encoded genotype vector \(\vec{y}\in E_{gen}\) belongs to the germinal vectors set \(G_{conds}\) as well, as it autogenerates itself. Orange arrows show how the encoding of a decoded genotype \(\in D_{gen}\) generates a unique numerical representation, although different encoded genotypes may correspond to the same decoded expression, due to the readjustment of parameters needed to fit into valid ranges.  
Save and load specimens and germinal conditions.
* Export specimens as colored barcodes, according to the rules explained in Section 3.5.
## 4 Experimental Results
### Supervised Evaluation  
Our first experiment consists of training two models on the train split of our dataset and evaluating them on our labeled gold split. The model architecture is described in Section 2.6. The training was done for 10 epochs, with the best epoch on the validation split being used to provide the final results. This setting is based on the original [PERSON] paper [55]. These models are effectively a baseline designed to evaluate our dataset. The models differ only in the fact that one is trained on hard labels (0 or 1) created from applying the 0.5 threshold on the soft labels that the other model is trained on. The soft labels are the probability output of the pseudo-labeling classifiers described in Section 2.5. The results are shown in Table 8 with the corresponding hyperparameters shown in Table 9.  
The macro averaged scores for both models are essentially the same at 0.59 F1. The results appear to be in line with what a similar model would achieve when trained and evaluated on GoEmotions, which is annotated by crowd workers while our dataset was automatically annotated. This is a good outcome. We expected the use of soft labels to provide some regularization and improved results on the human-labeled data, but this was not the case. Additionally, we compare the results with different model sizes without changing the architecture. Table 10 shows the summary comparison of different encoder sizes. There was no significant difference between using the BASE encoder and the LARGE encoder when fine-tuned and evaluated on our dataset with similar hyperparameters. The DISTIL model, which uses the distilroberta-base encoder, had a slightly worse F1 score. When comparing our RoBERTa classifier with the more common BERT baseline, we see that RoBERTa outperforms BERT in both the DISTIL and BASE sizes. The results for the LARGE size are the same. We can also see that RoBERTa LARGE and BASE have the same score but are still better than the DISTIL size. This is a data point that indicates likely diminishing returns from increasing the model size and suggests that future work should focus on improving the dataset.
## 5 Discussion
In dry conditions, the Galvin model prediction agreed with that of the Galvin model. In water-saturated conditions, the predicted P-wave anisotropy of the modified model was higher than that of the Galvin model. the linear-slip model, and the modified model prediction agreed with the Thomsen model. The linear-slip model and Thomsen model yielded nearly the same dry P- and S-wave anisotropy parameters. In water-saturated conditions, the modified model predicted larger P-wave anisotropy than the Galvin model. That is mainly because the predicted P-wave anisotropy of \(C_{ij}^{hf}\) and \(C_{ij}^{lf}\) of the modified model was higher than the Galvin model. Table 3 lists the predicted P-wave anisotropy parameter in \(C_{ij}^{lf}\) and \(C_{ij}^{hf}\) of the modified model and Galvin model in sample 2#. In the Galvin model, \(C_{ij}^{lf}\) and \(C_{ij}^{hf}\) were calculated by the linear-slip model and [PERSON] (2003) model, respectively. In the modified model, the [PERSON] model yielded higher P-wave anisotropy than the linear-slip and [PERSON] (2003) models in high- and low-frequency limits, respectively. Figure 10: Measured anisotropy and theoretical predictions of the Galvin model (dashed curves) and the modified model (solid curves). (**a**) P-wave anisotropy; and (**b**) S-wave anisotropy. In the modified model, the predicted P-wave anisotropy was close to that of the measured data in water-saturated conditions but significantly higher than that measured in dry conditions. This might be due to the interaction between the fractures. In the Galvin model and the modified model, the interaction between fractures is neglected. For air saturation, the difference between fracture infill material (air) and background medium was large, and the interaction between fractures was strong. Numerical studies indicate that the interaction between fractures can reduce P- and S-wave anisotropy [41]. This gives rise to the overestimation of P-wave anisotropy in the modified model. For water saturation, the modulus difference between the fracture infill material (water) and the background medium was greatly reduced compared with the dry case, and the interaction between fractures was relatively weak. This resulted in a good fit between measured P-wave anisotropy and the prediction of the modified model.
## 3 Measurement Results and Discussion
### System Experiments
An additional HPF with 12 GHz cut-off is implemented in the envelope detector for optimal performance at 25 Gbps bitrates, which shifts the overall lower cut-off to higher frequencies. Further integration of TIA and envelope detector into a single chip is expected to provide improved performance. Figure 11: Evaluation board hosting the electrical receiver front-end (left) with magnified view of the die sub-mount consisting of a lensed PIN photodetector, the fabricated TIA chip, and a high-speed envelope detector. Both TIA and ED chips have been fabricated in the same technology. Figure 10: Single-polarization receiver setup used to evaluate the TIA performance. Measurements for the optical receiver sensitivity are reported in Figure 13, with marked lines representing the NG-PON2 forward error correction (FEC) limit (BER = \(1\times 10^{-3}\)) and the Ethernet FEC limit (BER = \(5\times 10^{-5}\)). The measurements were performed at 10 and 25 Gbps data rates. For the 10 Gbps case, an EML transmitter was used having an extinction ratio (ER) of 15.28 dB, emitted power of 0.44 dBm, and center wavelength at 1548.75 nm. In turn, the 25 Gbps signal was generated with an EML having an ER of 9.3 dB, emitted power of 2.9 dBm and center wavelength at 1561.48 nm. A sensitivity of \(-30.5\) dBm for the NG-PON2 FEC limit and \(-28.5\) dBm for the Ethernet FEC limit is obtained at 10 Gbps. The 25 Gbps BER curve shows an error floor around \(1\times 10^{-7}\), which was attributed to insufficient O/E bandwidth in the receiver. Nevertheless, the receiver can still perform below the FEC limits, achieving \(-26\) and \(-23\) dBm sensitivity for NG-PON2 and Ethernet FEC limits, respectively, thereby improving by 3 dB the results obtained in [11] at 25 Gbps back-to-back. Figure 12: Measured and simulated opto-electrical bandwidth of the receiver using a 3 Gbps NRZ data transmission with swept IF center frequency. The inset shows a schematic representation of the simulation setup. Figure 13: Measured BER as a function or received optical power (\(P_{in}\)) at 10 and 25 Gbps data rates. Line markers indicate the NG-PON2 and Ethernet FEC limits at BER = \(1\times 10^{-3}\) and BER = \(5\times 10^{-5}\), respectively. To conclude, Table 1 summarizes the main parameters obtained from the TIA characterization against the relevant state-of-the-art. The reported TIA shows an overall good performance, enabling a high receiver sensitivity in a quasi-coherent receiver setup.