esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Basic of GNSS
### System Architecture
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project/Program** & **Time Range** & **System Architecture** & **References** \\ \hline \multirow{4}{*}{APOLO} & \multirow{4}{*}{1998–2001} & GPS receiver in various operating modes (standalone, DGPS, EGNOS) + odometer + gyroscope + accelerometer + Doppler radar & [26; 52] \\ \hline \multirow{4}{*}{GADEROS} & \multirow{4}{*}{2001–2004} & GPS + EGNOS + digital track map (a configuration with virtual balise database, hybridizing GNSS, an odometer, and a gyrometer) & [53] \\ \cline{1-1}  & & GPS receiver + EGNOS + beacon + odometer & [54] \\ \hline \multirow{2}{*}{GaLoROI} & \multirow{2}{*}{2012–2014} & Galileo receiver + eddy current sensors + map + redundant channels & [25] \\ \hline \multirow{2}{*}{3 inSat} & \multirow{2}{*}{Completion 2016} & Multi-constellation GNSS receiver + EGNOS + odometer + IMU & [55] \\ \hline \multirow{4}{*}{NGTC} & \multirow{2}{*}{During} & NGTC is focusing on the application of satellite positioning functions within ERTMS/ETCS (virtual balise concept). & [56; 57] \\ \hline \multirow{4}{*}{Shift2 Rail—IP2} & \multirow{4}{*}{During} & IP2 aims to develop a new generation of signaling and traffic control systems, based on the current ETMS, to enable intelligent management of automated train traffic, optimize performance and reliability, and minimize operating costs. & [58; 59; 60; 61] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Projects/innovation programs in railways.
## 3 Results and Analysis
### Quasistatic Tensile Test  
Figure 4 shows the representative stress-strain curves for the different studied laminates. UD laminates loaded along the fiber direction show better stiffness and strength ([0\({}^{\circ}\)]s and [0\({}^{\circ}\)/90\({}^{\circ}\)]s) than the rest. On unidirectional fiber laminates, there is a minimum degree of waviness of the fibers due to the cross-stitched threads, and combined with the microfibril angle, this can trigger the rearrangement of the reinforcement and matrix during the loading process; therefore, the stress-strain curve is not perfectly linear as observed for [0\({}^{\circ}\)]s, which was reported and analyzed in the previous study ([PERSON] et al., 2018), and similar behavior was also found in flax/epoxy composites ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). On the other hand, since half the laminas are in the transversal direction on [0\({}^{\circ}\)/90\({}^{\circ}\)]s, progressive failure at the 90\({}^{\circ}\) lamina may happen; a pronounced nonlinear stress-strain curve is observed, especially over 0.0085 strain, which is the strain to failure reported for [90\({}^{\circ}\)]\({}_{4}\). When the fibers are transversally oriented to the loading direction, the fiber-matrix interface is the weakest area; therefore, sudden failure happens, which is observed on the strain curve (Figure 4). The laminate with a [+ 45\({}^{\circ}\)/\(-\)45\({}^{\circ}\)]s fiber orientation presents two clear stages, which can be delimited by \(\varepsilon_{\text{cr}}=0.011\). First, before reaching \(\varepsilon_{\text{cr}}\), stable fiber-matrix interaction would happen. Then, after \(\varepsilon_{\text{cr}}\), the fiber-matrix interface starts to slide (due to the dominant shear stress at 45\({}^{\circ}\)); a similar behavior was observed in Kevlar and carbon fiber composites (Kevlar and Karab, 2016; Kevlar and Karab, 2016).  
For basket weave laminates, the waviness of the fibers has a significant effect on the stress-strain curve; the degrees of crimp and angle of waviness are lower in the weft direction compared to the warp direction; therefore, higher stiffness and strength are expected along the weft direction, even though the fiber volume fraction is lower (Figure 4). Furthermore, since the angles of waviness are 19.38\({}^{\circ}\) and 12.35\({}^{\circ}\) for the warp and weft directions, respectively, a nonlinear stress-strain is observed. Table 2 summarizes the tensile test mechanical properties of the laminate under study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Volume Fraction (vf)** & **Standard Deviation (SD)** \\ \hline [0\({}^{\circ}\)/90\({}^{\circ}\)]s, [90\({}^{\circ}\)]\({}_{4}\), and [+45\({}^{\circ}\)/\(-\)45\({}^{\circ}\)]s & 0.3722 & 0.0148 \\ [0\({}^{\circ}\)]s & 0.4613 & 0.0309 \\ Basket weave warp & 0.3733 & 0.0285 \\ Basket weave weft & 0.2417 & 0.0173 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fiber volume fraction of the manufactured laminates.  
Figure 3: Micrography of basket weave laminates (a) transversal to the warp direction, (b) transversal to the weft direction.
# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
#### 2.2.4 Clearing

Clearing [19] is a niching technique which inherently prevents overcrowded regions. It consists of removing from the population search individuals which are too similar to maintain diversity and the discovery of multiple promising solutions. Upon formation of a population, sorting based on the fitness value is performed to identify the most dominant individuals in the search space. A clearing radius \(c_{r}\) is defined to penalise individuals near the dominant individuals, thus pulling them away from each dominant basin of convergence and ensuring that they explore other regions in the next generation. Algorithm 5 provides a pseudo-code of the clearing niching paradigm. ```
1 Let \(X_{0}=[x^{t},x^{u}]\);
2 Set pop size N, max_iter \(K\) and \(k=0\);
3 Generate the initial population: \(x_{i}\in X_{0}\);
4 whileallagents not stalled and iter \(<K\)do
5 sort fitness \(f(x_{i})\) descendingly;
6 fori = 1:Ndo
7 if\(f(x_{i})>0\)then
8 nb-winners = 1;
9 forj = 1 + 1:Ndo
10 if\(f(x_{i})>0\)and \(d(x_{i},x_{j})<c_{r}\)then
11 ifnb-winners \(<c_{c}\)then
12 nb-winners ++;
13 else
14\(f(x_{i})=0\);
15
16 end if
17
18 end for
19
20 end for
21
22 end for
23
24 end for
25
26 end for
27
28 end for
29
30 end for
31
32 end for
33 return niche heads;
```

**Algorithm 5**Typical clearing procedure. The authors of [58] proposed a differential evolution algorithm with topological clearing which uses topological heuristics [59] to determine the dominant population agents and clearing to penalise vectors which are suboptimal. The authors of [60] proposed a differential evolution algorithm with self-adaptive parameter estimation which uses clearing for niche formation. Table 4 gives an exhaustive overview of the surveyed methods. #### 2.2.5 Speciation

Speciation is an approach of forming multiple subpopulations which coexist with minimal to no social interaction. This technique aims to separate the population into species or subpopulations based on similarity. It proceeds by identifying species seeds (typical dominant individuals) around which other nearest individuals orbit, thus forming niches. Algorithm 6 provides a pseudo-code of the speciation niching paradigm.
# Long-Term Effects in Structures: Background and Recent Developments
## 3 Long-Term Effects in Concrete
### Aging of the Concrete  
Concrete, widely utilized in infrastructure such as buildings, bridges and dams, is subjected to various environmental and mechanical factors that contribute to its deterioration over time. As concrete ages, it undergoes changes in mechanical and physical properties that can impact its structural integrity. Over time, factors such as moisture ingress, chemical reactions and mechanical loading contribute to deterioration. Creep, a gradual deformation under sustained loads, increases with age, leading to potential structural deformation. Shrinkage, caused by moisture loss, can result in cracking and reduced durability. Additionally, the porosity of concrete may increase over time, making it more susceptible to moisture penetration and chemical attacks. These changes in mechanical and physical  
properties highlight the importance of proactive maintenance and rehabilitation efforts to ensure the long-term performance of concrete structures [59; 60; 61].  
While aging can lead to some deterioration in concrete structures, there are indeed positive aspects as well. With proper curing and aging, concrete can experience improvements in strength and modulus of elasticity over time. This phenomenon is often attributed to the ongoing hydration process, which continues even after the initial setting of the concrete. As hydration progresses, the cementitious matrix becomes more compact and dense, resulting in enhanced mechanical properties. Additionally, chemical reactions within the concrete can contribute to the development of additional bonding between particles, further increasing strength and stiffness. Therefore, aging can have beneficial effects on the mechanical properties of concrete under certain conditions.  
The existing evaluations on the influence of and aging on the mechanical properties focus on compressive strength, along with elastic modulus and splitting tensile strength. Estimating the elastic modulus and splitting tensile strength is crucial since stress calculations rely on the elastic modulus, while the initiation of cracks is assessed based on the tensile strength. Often, these models take temperature into account in addition to concrete ageing [62]. Prediction models for aging concrete, like most, exhibit several shortcomings: simplified assumptions, lack of validation, limited accuracy, difficulty in accounting for combined effects, and sensitivity to input parameters.  
Nowadays, many methods have been developed which involves establishing a functional relationship between concrete's mechanical properties and its age through data fitting from early experimental data. This method aims to derive a functional equation that enables the calculation of concrete's mechanical parameters at any given time [63].
## 2 Phased Array Radar (PAR)
### Radiation Pattern Control
#### 2.2.1. Phased Array (PA)
\[U_{dB}\left(\theta\right)=20\cdot\log\left(\frac{AF}{\max\left(AF\right)}\right) \tag{2}\]  
\[RSLL=20\cdot\log\left(\frac{A_{M}}{A_{S}}\right) \tag{3}\]  
The \(AF\) for a symmetrical linear array with an even number of elements (\(N=2M\)) is given by Equation (4), being \(A_{n}\) the excitation amplitudes, \(\beta\) the constant phase difference between adjacent elements (progressive phase), \(d\) the uniform spacing between elements, \(\lambda\) the wavelength and \(\theta\) the angle of propagation related to the array axis [7]. \[AF_{2M}\left(\theta\right)=\sum_{n=1}^{M}A_{n}\cdot\cos\left[\left(2n-I\right) \cdot\left(\frac{\pi d}{\lambda}\cdot\cos\theta+\beta\right)\right] \tag{4}\]  
If compared to other techniques, electronic control has more flexibility, durability and functionality, being built by power dividers and phase shifters. It is common to use amplitude to control the level of sidelobes and to use phase to control the main lobe direction, so that the adopted configuration depends on a balance between gain, sidelobe level and beamwidth [7,13]. Some conventional deterministic  
Figure 4: Linear array of M isotropic elements. methods for electronic control are the Uniform, Binomial and Dolph-Tschebyscheff, apart from probabilistic methods, such as GAs.
The results show that this method has better prediction performance than other common methods. [PERSON] et al. (2022) [18] used the LSTM neural network and Gaussian process regression (GPR) to predict the ship rolling angle and pitch angle. The model yielded reliable interval prediction results without reducing the forecasting accuracy of the LSTM model, which verified the effectiveness and advancement of the hybrid model. [PERSON] et al. (2021) [19] proposed a ship roll angle prediction method based on the bidirectional long short-term memory network (BLSTM)and temporal pattern attention mechanism (TPA). Compared with the LSTM model and the SVM model, the combined model had a significant reduction in errors. Later, [PERSON] et al. (2021) [20] proposed the single input single output (SISO) and multiple input single output (MISO) methods to predict ship motion based on deep learning. The methods achieved good prediction accuracy. Table 1 summarizes the relevant machine learning models for the short-term partition of ship motion. At present, most scholars used the LSTM neural network to predict ship motion in the very short-term, mostly based on the data of ship motion itself, while there are few reports on the research of ship motion prediction based on wave elevation data. In fact, in the process of ship operation at sea, especially at zero speed, waves are the main excitation of ship movement. The wave itself has the memory effect. If the ship movement can be predicted in the very short-term by using the wave elevation data as the input feature, the prediction duration and prediction accuracy can be effectively improved in theory. In order to verify the effectiveness of the scheme, this paper, based on the LSTM neural network model, combined with wave elevation and ship motion duration data to study the very short-term prediction of ship roll motion.
# The Strain Heterogeneity and Microstructural Shear Bands in AZ31B Magnesium Alloy
## 2 Materials and Methods
### Microstructure Observation  
To thoroughly analyze the crystal orientation of the sample, a technique combining Scanning Electron Microscopy (SEM) with EBSD was employed. The SEM was performed using a Sigma300 produced by the German Zeiss company (Baden-Wurttemberg, Germany).Prior to the EBSD scanning process, the surface of the sample underwent meticulous mechanical polishing, followed by electrolytic polishing or ion polishing, aimed at eliminating any residual surface stress. In this study, electrolytic polishing with a perchloric acid-alcohol solution (ratio 1:9) was adopted for stress relief. The polishing was conducted at a controlled voltage of 15 V, maintaining a temperature of \(-20\)\({}^{\circ}\)C for a precisely timed duration ranging from 10 to 30 s. During the EBSD scanning, the operating voltage was 20 KV, and the working distance was 12 mm. Furthermore, a scanning step size was carefully selected within the range of 0.05 \(\upmu\)m to 5 \(\upmu\)m to ensure comprehensive data acquisition. Once the EBSD scanning was completed, CHANNEL5 software (v.5.12.74.0) (EBSD analysis software of Oxford Corporation, Shanghai, China) was utilized to meticulously analyze the scanned images, and the key information about grain orientation and grain boundaries on the test surface area of the sample was obtained. Special emphasis was  
Figure 1: Sample orientation diagram.  
placed on examining the texture through EBSD measurements conducted on the RD-TD (rolling direction-normal direction) surface, as illustrated in Figure 2. It became evident that the majority of grains exhibited an average size of approximately 100 \(\upmu\)m. The analysis of pole and inverse pole diagrams indicated the presence of a typical basal texture within the material. Specifically, the \(<\)0001\(>\) orientation of the majority of the grains aligned predominantly parallel to the ND direction.
## 5 Evaluation
### CPCROK Implementation
#### 5.2.2 Model Setting  
Our RNN-FGTGM module utilizes the Adam Optimizer, and we have tested combinations of different numbers of neurons (16, 32, 64, 128) and different numbers of hidden layers (1, 2) to evaluate their impact on the success rate and the size of the loss function. Ultimately, we selected 64 neurons and one hidden layer as the hyperparameters, because this combination achieves the lowest loss function value while minimizing the attacker's success rate, all within the constraints of limited computational time. Other parameters are displayed in Table 1.
## 5 Performance Evaluation
### Recovery Latency  
To compare the recovery latency between the scheme with checkpointing and that without check-pointing, we deployed sensor nodes randomly, meaning that we assigned the distance between a head node and each member node (ranging from 1 to 15 km) arbitrarily. The recovery latency is affected by the number of messages sent during the recovery process. In the scheme without checkpointing, \(O(n^{2})\) messages are generated during the re-election process as the number of nodes increases in a cluster. However, our checkpointing scheme when applied generates only \(O(n)\) messages via the backup node; thus, the recovery latency with checkpointing increases linearly, as shown in Figure 13.  
Figure 14 shows the variation of recovery latency as the guard time increases range from 500 ms to 1,000 ms. The guard time that prevents the collision of acoustic signals affects to the recovery latency directly. The recovery latency with checkpointing scheme is more efficient than that of without checkpointing scheme even if the guard time is up to 50 s.  
Figure 11: The checkpointing interval.  
Figure 12: Comparison of the energy consumption values (randomized).
## 4 Experimental Results and Analysis
### Select Evaluation Index  
This study uses classification accuracy Equation (31), the macro-F1 Equation (32), the class balance accuracy \(CBA\), Equation (33), the total cost of misclassification Equation (34), and the kappa coefficient Equation (35) as the evaluation indices for the model. The accuracy and the macro-F1 are evaluation metrics for multi-classification problems, reflecting the accuracy of the model, the kappa coefficient demonstrates the credibility of the model, and the \(CBA\) is proposed for imbalanced data, reflecting the classification performance of the model for imbalanced data. In addition, this paper explores a cost-sensitive classification method, for which the total misclassification cost is used as an evaluation index, which can reflect the loss caused by misclassification in different ways.  
\[Accuracy=\frac{\sum\limits_{i=1}^{q}c_{ii}}{\sum\limits_{i,j=1}^{q}c_{ij}} \tag{31}\]  
\[Macro-F_{1}=\frac{2\times macro-P\times macro-R}{macro-P+macro-R} \tag{32}\]  
\[CBA=\frac{1}{q}{\sum\limits_{i=1}^{q}\frac{c_{ii}}{\max(c_{i,}c_{i.})}} \tag{33}\]  
\[\cos t=\sum\limits_{i=1}^{q}{\sum\limits_{j=1}^{q}{c_{ij}\times\omega_{ij}}} \tag{34}\]  
where \(macro-P=\frac{1}{q}{\sum\limits_{i=1}^{q}{precision_{i}},macro-R=\)\(\frac{1}{q}{\sum\limits_{i=1}^{q}{recall_{i}}{precision_{i}}}=\frac{c_{ii}}{c_{i}},recall_{i}=\frac{c_{ii}}{c_{i}} ;q\) is the number of categories; \(c_{ii}\) is the number of samples with actual labels of \(i\) and the classification prediction results are also \(i\); \(c_{i}\) is the number of the samples with actual labelsof \(i\); and \(c_{i}\) is the number of the samples with classification prediction results of \(i\); \(c_{ij}\) is the number of samples with actual labels of \(i\) and the prediction results are \(j\).  
\[Kappa=\frac{p_{0}-p_{c}}{1-p_{c}} \tag{35}\]  
where \(p_{0}=\frac{\sum\limits_{i=1}^{q}c_{ii}}{\sum\limits_{i,j=1}^{q}c_{ij}},p_{c}= \frac{\sum\limits_{i=1}^{q}c_{i,j}\times c_{i,j}}{\left(\sum\limits_{i,j=1}^{q }c_{ij}\right)^{2}}\).
# Single-Shot Temporal Contrast Enhancement Measurement of a Plasma Mirror by a Chirped Pulse
## 3 Experiment and Discussion
### Results and Discussion
DPM can achieve 10\({}^{4}\) enhancement, which is beyond the measurement ability of traditional methods. Moreover, it challenges the dynamic range of the spectrometer. After further improvement of the dynamic range, the SOCP is expected to be used for the single-shot measurement of DPM. Thirdly, the temporal measuring range is tunable by altering the chirp rate of SOCP, which is opposite to the temporal measurement resolution. The chirp rate of the SOCP should vary according to the different diagnostic purposes of the PM. In order to measure the temporal contrast enhancement of the PM, a hundred picosecond or nanosecond time scale is appropriate in the experiment. If aiming to determine the triggering time of the PM, the chirp rate should be altered. A higher resolution is needed to realize a more precise measurement on the temporal profile for revealing more information about the PM in the sub-picosecond scale. For example, if the chirp rate is 0.1 ps/nm, the duration of the SOCP is 100 ps, and the temporal resolution can reach 15.7 fs, which can provide more details about the generation process of the sub-picosecond-response PM. Moreover, the measurement capability of SOCP also significantly depends on the performance of the spectrometer. A high spectral resolution can enhance the measurement resolution, and a low noise spectrometer can improve the dynamic range and measurement accuracy of the SOCP.
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 3 Performance Test
### Specificity Analysis  
Since CS and PAA were the sensitive materials chosen in this experiment, the -NH\({}_{2}\) and -COOH in the molecule can form chelates with most metal ions. Therefore, the sensitive film can not only adsorb Hg\({}^{2+}\), but also other metal ions, such as K\({}^{+}\), Na\({}^{+}\), Cu\({}^{2+}\), Ag\({}^{+}\), and Fe\({}^{3+}\). In order to investigate the specificity of the fiber SPR Hg\({}^{2+}\) sensor, K\({}^{+}\), Na\({}^{+}\), Cu\({}^{2+}\), Ag\({}^{+}\), and Fe\({}^{3+}\) solutions with an ion concentration of 50 uM were prepared, and then solutions containing different ions with the same concentration were measured by the SPR sensor. The experimental results are shown in Figure 10. As can be seen from Figure 10a, with the increase of the detection time, the wavelength shift increases gradually with the detection time, and finally reaches stability. It can be seen from Figure 10b that the stable wavelength shift of Hg\({}^{2+}\) is obviously larger than that of the other ions. Therefore, this proposed SPR sensor shows good specificity to Hg\({}^{2+}\).
###### Abstract  
Applications involving graphene-related materials (GRMs) are becoming very common, raising concerns for their environmental impact. GRMs induce various effects on plants, but those on the sexual reproduction are still largely unknown. Here, the effects of graphene oxide (GO) and GO purified from production residues (PGO) were tested in vivo on the stigma of _Cucurbita peppo_ L. ssp. _pypo \"Greyzini\"_ (summer squash). Stigmas were exposed to GO or PGO for three hours and were then analyzed by environmental scanning electron microscopy to verify possible alterations to their surface. Stigmas were then hand-pollinated to verify the effects of the two GOs on pollen adhesion and germination on the stigma, and, subsequently, on the development of fruits and seeds. Severe damages to the stigma were not detected; nevertheless, both pollen adhesion and germination on the stigma decreased. Moreover, fruits developed defectively with signs of necrosis in the case of GO, whereas fruits did not ripen in the case of PGO and ovules did not develop seeds after both GOs treatments. These results highlight different mechanisms of interaction of the two materials with the pollen-stigma system, suggesting a possible negative impact of GO on the sexual reproduction of other seed plants.  
Keywords: fruitful development; flowers; nanomaterials; particulate matter; pollen germination; seed development; stigmatic surface +
Footnote †: journal: Applied Sciences