esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 4 Results and Discussion  
### Statistical Evaluation of GPPs  
#### 4.1.1 Seasonal (Rainy/Dry) and Annual Comparison  
The results from the analysis of the seasonal and annual variations of the precipitation of all of the GPPs and rain gauges at the grid scale for the period of 1998-2006 are shown in Figure 2. The results show an increasing trend in the precipitation from the northern part (upstream) to south-east part (downstream) of the MRB in all precipitation data for the dry and rainy seasons, as well as the entire year in general.  
Figure 2: Average annual precipitation (mm/year) at a spatial resolution of 0.25\({}^{\circ}\) derived from the Climate Prediction Center Morphing (CMORPH), the Tropical Rainfall Measuring Mission (TRMM) 3B42, the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, and the Global Satellite Mapping of Precipitation (GSMaP): Observations for the dry season (**a**–**e**), the rainy season (**f**–**j**), and the entire year (**k**–**o**) over the Mekong River Basin during the period from 1998 to 2006.  
Visual inspection of spatial distributions reveals that TRMM has the best agreement with the observation pattern (Figure 2b,g). The spatial distributions and precipitation amount of CPC and GSMaP are similar, but they are different to the observation pattern. Particularly, CPC and GSMaP show a distinct rainfall pattern with much lower precipitation over the northeast and mid-latitude regions (Figure 2c,d). This is possibly because the number of GTS gauges that provide data for CPC is insufficient to capture the precipitation pattern of these sub-areas. CMORPH reveals a relatively similar spatial distribution pattern as that of the TRMM 3B42 at the mid-latitude and low-latitude parts of the river basin, even though apparently lower precipitation amounts are observed. The spatial precipitation patterns in the upstream part of the basin (i.e., the northern part) for both seasons using CMORPH are very distinct from the observation pattern and the other evaluated GPPs--this product's precipitation amount is extremely low compared to the others. The performances of the GPPs at the annual scale exhibit a similar pattern to the results at the seasonal scale.  
It is clear that the spatial patterns of precipitation need to be considered when using these GPPs; TRMM 3B42 is able to capture the temporal (at both the seasonal and annual time scales) and spatial distribution patterns of precipitation better than the other evaluated GPPs over the MRB, and can be considered as a good alternative to rain gauges for hydrological research applications at these evaluated scales.
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Rule-Based Energy Management Strategies  
The rule-based energy management control strategy is also known as a logic threshold control strategy. The main idea is to determine the operating state of the powertrain by defining a set of rules for the operation of the vehicle's powertrain, usually in the form of a flowchart with a table of control parameters [56; 57; 58]. The two main types of control are deterministic rules and fuzzy rules. Neither method involves direct optimizing or minimizing control [59; 60]. The design of rules is often based on heuristic algorithms or engineering experience, and the effectiveness of control is dependent on the strength of the rule design [61].
# Performance Evaluation of the Current Birmingham PEPT Cameras
## 2 Simple Characterisation of PEPT Systems
For an off-centre source, this is no longer true, so the sensitivity falls: as shown in Figure 1, movement along the axis linking the detectors leads to a relatively small drop in sensitivitybut moving transversely, the sensitivity falls approximately linearly and reaches zero at the edge of the FOV. As activity increases, the linear relationship between activity and recorded true coincidence rate fails due to the increasing effects of deadtime. Furthermore, since the random coincidence rate is proportional to \(A^{2}\), the data include an increasing background of random coincidences. The maximum useful data rate for a system is determined by a combination of these two effects, both of which are directly dependent on the singles data rates of individual detectors. If the solid angle is divided into \(n\) independent identical detectors (for example the \"buckets\" of a PET scanner), the singles rate in each is equal to \(2\Lambda\Omega/n\) (the factor 2 arises from the fact that two photons are emitted in each annihilation), so that for a central source, the ratio of singles rate to coincidence rate is equal to \(2/n\). To maximise the possible count rate, it is necessary to use multiple detectors each with high efficiency. In summary, the key parameters of a PEPT imaging system are the intrinsic spatial resolution and the data rate. It is also important that the fraction \(f\) of useful events is as large as possible: for most systems \(f\) is indeed large for a low activity bare tracer, but reduces when the tracer is surrounded by material or for more active tracers when the proportion of random coincidences becomes significant. Since there are practical limits on the activity which can be generated in a small tracer particle, to achieve a high data rate requires high sensitivity, which implies large solid angle and also high efficiency detectors. Because of the dependence on singles rates, the ultimate data rate that can be achieved is particularly dependent on having high efficiency. In many cases, compromises are required in order to cover the required field of view. Often this involves spacing out detectors, so that they have gaps between them--it should be noted that this is equivalent to using detectors of correspondingly lower efficiency, with all the consequent drawbacks.
## 3 Characterization of Mechanical Properties of the Material
### Elastic Behaviour  
Dislocation pile-up can lead to non-linear hysteresis behaviour during unloading and loading cycles after plastic stretching of material [13]. Figure 3 shows a schematic of the behaviour of Inco718 in a loading-unloading test designed to characterize the material's non-linear behaviour.  
During this test, a standard tensile sample was stretched until a pre-defined pre-strain value (\(\epsilon_{\text{p1}}\)). The sample was unloaded until zero stress was reached and then loaded again up to the pre-strain \(\epsilon_{\text{p1}}\) value. Next, the sample was unloaded again, and the cycle was repeated, increasing the pre-strain level at each loop. A schematic representation of this process and the results obtained are shown in Figure 3a. As shown by an analysis of the elastic unloading-loading (Figure 3b), the unloading (and loading) path followed non-linear behaviour, creating a hysteresis loop. The modulus of the slope linking the start and finishing points of the loop was then measured (i.e., the chord modulus, \(E_{\text{ch}}\)).  
Usually, the value of the chord modulus decreases with an increase in the pre-strain of the loop. In the present study case, the value of the chord modulus in both sets of samples (AR and PSTT) decreased from about 176 GPa at the first loading until around 150 GPa after 7% to 10% of plastic strain. At least three samples were tested under each condition, and the average value is shown in Table 3. The strain measurement of the samples was conducted using high-elongation strain gauges glued to the centre of the tensile specimen.  
Figure 3: Schematic representation of the elastic modulus degradation testing in where: (**a**) loading–unloading test and (**b**) a hysteresis loop observed during the elastic unloading–loading step.  
Figure 4 shows the evolution of the chord modulus (with the deviation of the values as error bars) for both states (AR and PSTT).  
As shown in Figure 4, similar changes were observed in the chord modulus behaviour of the AR and PSTT samples. The latter may suggest that the annealing process completely dissolved dislocation and that dislocation pile-ups were responsible for the hysteresis loops in the initial material state, at least in terms of the material's elastic behaviour. However, the chord modulus, which is commonly used to denote the equivalent elastic modulus, rapidly decreased in the first 4% to 5% of pre-strain and reached an asymptotic value of around 150 GPa. It is worth noting that the initial equivalent elastic modulus (chord modulus of the first loading) was 20 GPa lower than that specified by the material supplier. This is consistent with previous observations on high-strength steel when measuring using strain gauges and taking into account the non-linearity of the first loading [14; 15]. This difference could have an impact on springback simulations [16].
## 4 Experimental Work  
### Embedded ANNs on PLCs  
Discretely trained artificial neural networks were generated using the \"gensim\" function in MATLAB and then merged into a single model after being transferred to a Simulink environment. Since the targeted displacement will be used as input in the controller design and the static force will be generated by the pressure required for displacement, the two networks cascade, as shown in Figure 11.  
Figure 10: Training and validation performance of neural networks: (**a**) pressure–force; (**b**) displacement–pressure.  
Figure 9: Feedforward backpropagated NN model for both displacement and static force.  
The neural networks were integrated into a subsystem that operates as a controller, with a pressure control signal and resultant force as the output and targeted displacement as the input. The subsystem was set up with the \"treat as an atomic unit\" preference, and the \"target IDE\" was adjusted to \"TIA Portal Double precision\" to create a Structured Control Language (SCL) output code that was used to create a function block for the Siemens S7-1200 PLC. The obtained SCL code was deployed as an external source code in the TIA Portal to generate the function block depicted in Figure 11(a). PLC tags were assigned long real numbers for the Human-Machine Interface (HMI) to display the displacement input and generated pressure signal output of the proportional pressure regulator, as shown in Figure 11(b).  
Without modeling a nonlinear system, a control algorithm was prepared with the ANN, the experimentally obtained data were trained, and the code of the ANN algorithm was then embedded in the PLC. Edge AI with transfer learning is embedded in the local PLC, prioritizing data processing at the source where the AI calculations are executed and control data are generated, thus avoiding the need for constant connectivity to remote servers.
# Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
## 5 Discussion  
This study sought to understand the enabling factors and challenges faced by M-SOCs in their quest to facilitate the timely detection of cyber events. The analysis of the results focused on the factors that make M-SOC operations different from traditional SOCs in other critical infrastructure sectors. These factors are the level of connectivity and the dynamic vessel states that influence contextual information. The subsequent discussion will therefore be guided by these factors. Additionally, the relevance and transferability of these findings for future ROCs will also be explored.
# Effect of Thermo-Mechanical Processing on the Corrosion Behavior of Fe-30 Mn-5 Al-0.5C TWIP Steel
## 1 Introduction
This work aims to reveal the influence of the thermo-mechanical process on the corrosion performance of high-Mn TWIP steels. To better understand the correlation between microstructure and corrosion mechanisms, a recrystallized high-Mn TWIP steel was also studied. Electrochemical corrosion behavior was investigated in a 1.0 wt.% NaCl solution and the corrosion products, alloy microstructure and surface composition and morphology were studied by electrochemical methods and surface analysis techniques.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 2 Materials and Methods
### Extending VRAE to Include Temporal Information
#### 2.2.3 VRAE with Embedded Re-Scaling
```
0:\(\vec{x}^{(i)}\)-- measurements of the time series \(\vec{\beta}^{(i)}\)-- delta times of the time series Output:\(\vec{\hat{x}}^{(i)}\)-- reconstructed time series
1:\(s^{(i)}\leftarrow\text{std}(\vec{x}^{(i)})\) // \(F\)-std
2:\(\vec{x}^{(i)}_{s}\leftarrow\frac{\vec{x}^{(i)}}{s^{(i)}}\) // Step-1
3:// Encode to distribution: Step-2
4:\(\vec{\mu}^{(i)}\gets f^{1}_{\phi}\Big{(}E^{1}(\vec{x}^{(i)}_{s},\vec{ \delta}^{(i)}),E^{2}(s^{(i)})\Big{)}\)
5:\(\vec{\sigma}^{(i)}\gets f^{2}_{\phi}\Big{(}E^{1}(\vec{x}^{(i)}_{s},\vec{ \delta}^{(i)}),E^{2}(s^{(i)})\Big{)}\)
6:\(\vec{\varepsilon}\sim\mathcal{N}(\vec{0},\vec{I})\) // Auxiliary noise
7:\(\vec{\hat{z}}^{(i)}\leftarrow\vec{\mu}^{(i)}+\vec{\sigma}^{(i)}\odot\vec{ \varepsilon}\) // Step-3
8:// Decode or Reconstruct: Step-4
9:\(\vec{\hat{x}}^{(i)}_{s}\gets g^{1}_{\theta}\Big{(}\vec{z}^{(i)},\vec{ \delta}^{(i)}\Big{)}\)
10:\(s^{(i)}\gets g^{2}_{\theta}\Big{(}\vec{z}^{(i)}\Big{)}\)
11:\(\vec{\hat{x}}^{(i)}\leftarrow\vec{\hat{x}}^{(i)}_{s}\cdot s^{(i)}\) // Step-5
```  
**Algorithm 2** Forward pass \(\textbf{S-VRAE}_{t}\). In any VAE model, the objective of learning the distribution of the latent variable is to obtain the more likely reconstructed input pattern. In our S-VRAE\({}_{t}\), the time series is reconstructed on the original un-scaled behavior \(\vec{\hat{x}}^{(i)}\), so we can consider it as a smoothing or denoising model in a data-dependent way (based on data behavior). As the model processes the \(F\)-_std_ inside it, it allows a robust \(F\)-_std_ reconstruction (based on the noise). For example, consider the following decomposition of a time series: \(\vec{x}^{(i)}=\vec{x}^{(i)}_{p}+\vec{\mu}^{(i)}\), with \(\vec{x}^{(i)}_{p}\), flux measurements with an isolated or perfect environment such as the one modeled by [3], and \(\vec{\pi}^{(i)}\) as the intrinsic noise of these measurements, both with zero mean. Based on this decomposition, we can express the noised and denoised dispersion, \(F\)-_std_, as \(s^{(i)}_{n}=\text{std}(\vec{x}^{(i)}_{p}+\vec{\mu}^{(i)})\) and \(s^{(i)}_{p}=\text{std}(\vec{x}^{(i)}_{p})\), respectively. The denoised \(F\)-_std_\(s^{(i)}_{p}\) could only be close-estimated by S-VRAE\({}_{t}\) in order to re-scale the denoised reconstructed time series \(\vec{\hat{x}}^{(i)}_{p}=\vec{\hat{x}}^{(i)}_{p,s}\cdot s^{(i)}_{p}\) (Step 5 of Algorithm 2). Other models, with a fixed re-scaling process, will always use the noised \(F\)-_std_: \(s^{(i)}_{n}\). This shows the capacity of S-VRAE\({}_{t}\) to learn to smooth an un-scaled time series.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 2 Methods  
### Graph Convolutional Network  
Although urban area functions influence population flow patterns, POI features cannot adequately reflect urban area functions since they are inherently complex and dynamic. Despite the great success of CNNs in dealing with Euclidean data, researchers have recently attempted to apply CNNs to non-Euclidean data with mixed results. Right now, the most successful deep learning approach to solve this issue is graph convolutional.  
Spectral and spatial methods are the two most popular strategies for graph convolution. The spectral method GCN is defined in the spectrum domain, and research into such methods were mostly based on [14]. After that, [PERSON] et al. [15] proposed the ChebNet variant method, which represents a new stride in the development of GCNs and uses Chebyshev extensions to reduce the complexity of Laplace computation. [PERSON] et al. [16] transformed ChebNet into a simpler version that showed the best performance in various settings. Subsequently, [PERSON] et al. [17] introduced the attention mechanism into GCN and proposed the graph attention network (GAT), which achieves improved prediction accuracy by assigning different weights to neighboring nodes. The spatial approach's goal is to map nodes in the sensory field to a fixed set, allowing convolution to be performed on various nodes with shared parameters. [PERSON] et al. [18] mapped nodes to a vector representation in which related nodes are placed similarly in space. However, GCNs are only weakly expressive since they do not consider neighboring nodes in the spatio-temporal graph.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 4 Data Analysis
### COTS within the Selection  
COTS are marked with a double asterisk (***)** after their name in Table 1 (ADS1281, AD7712, AD782T, MAX145, ADS1258, LTC1604, LTC1409, ADC124S101, AD7984, AD6640, ADS5483 and SPT7725); they are also easily recognizable therein since their temperature range is smaller than \([-55;125]\)\({}^{\circ}\)C. Two main reasons explain the presence of COTS in the _selection_: either they cover a range of sampling frequencies that hardened ADCs do not, or they offer a better FOM\({}_{W}\) compared to their surrounding hardened counterparts. If we consider the efficiency including TID performance, COTS are far from the envelopes in Figures 6 and 7. On the other hand, even if the best performing COTS are far from the most efficient specimen in Figure 8 (Boeing-1 pipelined ADC), they present good FOM\({}_{SET}\) compared to other hardened ADCs. In other words, COTS could have shorter lifetime due to TID compared to hardened ADCs; however, since soft errors are almost impossible to be totally mitigated (even in hardened ADCs), COTS usage could be worthy in short lifetime applications, where the power consumption and/or the cost are prioritized. HEP (where data acquisition systems can be replaced) and fleets of small satellites (where the viability depends on the price per satellite) are de facto target applications for COTS. \begin{table}
\begin{tabular}{c l l l l l l l l} \hline \hline \multicolumn{2}{c}{**Application**} & \multicolumn{1}{c}{\(\Delta\Sigma\)} & \multicolumn{1}{c}{**SAR**} & \multicolumn{1}{c}{**PIP**} & \multicolumn{1}{c}{**FIF**} & \multicolumn{1}{c}{**Flash**} \\ \hline \multirow{6}{*}{satellite communications} & \multicolumn{1}{c}{} & & \(\checkmark\checkmark\) & \(\checkmark\checkmark\) & & & \\  & discrete-time control & & & \(\checkmark\checkmark\) & \(\checkmark\checkmark\) & & & \\  & earth observation & & \(\checkmark\checkmark\) & \(\checkmark\checkmark\) & & & & \\  & house-keeping & & \(\checkmark\checkmark\) & \(\checkmark\) & & & & \\ \hline \multirow{2}{*}{satellite communications} & base-band & \(\checkmark\checkmark\) & \(\checkmark\) & & & & \\  & DDC & & & \(\checkmark\checkmark\) & & & & \\ \hline \multirow{3}{*}{sensor acquisition} & thermistor & \(\checkmark\checkmark\) & \(\checkmark\) & & & & \\  & gauge & \(\checkmark\checkmark\) & \(\checkmark\) & & & & \\  & magnetometer & \(\checkmark\checkmark\) & \(\checkmark\) & & & & \\ \hline \multirow{3}{*}{satellite communications} & star tracker & & & \(\checkmark\checkmark\) & & & \\  & sun sensor & & & \(\checkmark\checkmark\) & & & \\ \hline \multirow{3}{*}{ telemetry} & fast dynamics & & \(\checkmark\) & & & & \\  & slow dynamics & \(\checkmark\checkmark\) & \(\checkmark\) & & & \\ \hline \multirow{2}{*}{video} & & & & \(\checkmark\checkmark\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Suitability of ADC architectures for applications in radiation environments.
## 4 Multi-Objective Optimization of Frame Structures
### Design of Experiments
As shown in Figure 14b, the axial modal frequencies first increase and then decrease with the increasing mounting plate thickness P1 and the inner diameter P2 of the connecting tube. That indicates that increasing the structure's thickness has a limited effect on improving the rigidity. Increasing the thickness of the components in the structure always increases the stiffness first, thus increasing the axial modal frequencies. Then, the influence of the mass on the structure increases so that the axial modal frequencies decrease. The relationship between the maximum axial amplitude with the outer diameter of the connecting tube (P3) and the thickness of the motor plate (P6) is illustrated in Figure 15a. When the motor plate thickness (P6) is small, the maximum axial amplitude increases rapidly with the increasing outer diameter of the connecting tube (P3). It is attributed to two factors: firstly, the axial modal frequency range of the structure remains close to the excitation frequency, leading to resonance phenomena. Secondly, as the outer diameter of the connecting tube (P3) increases, the stiffness of the motor board relatively decreases, which helps to excite axial resonance. This effect diminishes as the thickness of the motor plate (P6) increases. With a sufficiently large motor plate thickness (P6), an increase in the outer diameter of the connecting tube (P3) results in a further difference between the axial mode frequency and the excitation frequency, preventing resonance. Consequently, the maximum axial amplitude decreases under these conditions. From Figure 15b, the axial mode frequency increases initially and decreases as the connecting tube P3 increases. The thickness P6 of the motor plate increases, and the axial modal frequency increases monotonically. Compared with the outer diameter P3 of the connecting tube, the influence of the thickness P6 of the motor plate on the axial mode frequency is more visible. That is because the motor plate causes axial resonance. From Figures 14 and 15: (1) the contour curves in the axial modal frequency response surface diagram and axial maximum amplitude response surface diagram are elliptical,  
Figure 15: Response surface of axial maximum amplitude (**a**) and axial modal frequencies (**b**) by P3 and P6 fitting. indicating that relevant parameters have strong interaction concerning the axial modal frequencies and the axial maximum amplitude. (2) Under interaction, the influence of any two variables on optimization objectives is nonlinear. (3) Resonance can be avoided if the difference between excited and modal frequencies outweighs 7 Hz.