esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Position Algorithm Calculation
#### 3.1.2 Step 2: Positioning Process
When the least-squares positioning method is adopted, the vehicle receives signals from three or more base stations, and the vehicle coordinates can be calculated by establishing a set of equations of multiple equations. \[Ax=b \tag{7}\]  
Suppose \((x_{d},y_{d})\) is the vehicle positioning coordinate. The optimal estimation obtained by least-squares positioning is:  
\[A=\left[ \begin{array}{c}x_{d}\\ y_{d}\end{array} \right] \tag{8}\]  
where, the matrix forms of \(A\) and \(b\) are:  
\[A=\left[ \begin{array}{cc}(x_{1}-x_{k})&(y_{1}-y_{k})\\ \vdots&\vdots\\ (y_{k-1}-x_{k})&(y_{k-1}-y_{k})\end{array} \right] \tag{9}\]  
\[b=\left[ \begin{array}{c}x_{k}^{2}-x_{1}^{2}+y_{k}^{2}-y_{1}^{2}+d_{1}^{2}-d _{k}^{2}\\ \vdots\\ x_{k}^{2}-x_{k-1}^{2}+y_{k}^{2}-y_{k-1}^{2}+d_{k-1}^{2}-d_{k}^{2}\end{array} \right] \tag{10}\]  
The sum of squares of errors is minimized by:  
\[f(X)=(AX-b)^{2}=(AX-b)^{T}(AX-b) \tag{11}\]  
where \(r=Ax-b\) indicates the error vector. The minimum value of \(f(x)\) can then be obtained:  
\[x^{*}=\left(A^{T}A\right)^{-1}A^{T}b \tag{12}\]  
where \(x^{*}\) indicates the estimation of \(x\). 5. The centroid algorithm is one of the typical non-ranging positioning methods [49]. Its positioning principle continuously broadcasts its ID through multiple anchor nodes deployed in the area; the undetermined node receives the ID packet and uses its signal data to measure its position in combination with the position of the anchor node. The centroid algorithm indicates its advantages with the straightforward process and economic deployment. When the centroid algorithm positioning method is adopted, the vehicle coordinates \((x_{e},y_{e})\) can be obtained by:  
\[\left\{ \begin{array}{c}x_{e}=\frac{x_{1}+x_{2}+\cdots+x_{n}}{n}\\ y_{e}=\frac{y_{1}+y_{2}+\cdots+y_{n}}{n}\end{array} \right. \tag{13}\]
## 2 Modification of Anion-Exchange Membrane
### AEM Composite Membranes Incorporated with Inorganic Fillers  
There are studies on the addition of inorganic components to the organic matrix of polymers such as titanium dioxide (TiO2), zirconium dioxide (ZrO2), aluminum oxide (Al2O3), bentonite, and graphene oxide (GO) to improve the morphological stability of the membrane [27; 48; 49; 50; 51; 52]. The addition of inorganic components reduces the glass transition temperature and increases the amorphous phases of the polymer matrix, which increases its ionic conductivity [71]. For instance, [PERSON] et al. [27] added TiO2 filler into the quaternary polysulfone membrane, which has shown a reduction in membrane swelling and improve  
Figure 2: Crosslinking of PVBC-based AEMs with PPO-N3 by solution casting and UV irradiation methods. Reprinted with permission from [47]. Copyright Elsevier, 2017.  
ment in morphological stability without compromising ionic conductivity. The atomic force microscopy (AFM) revealed that addition of TiO\({}_{2}\) increases the surface roughness of the membrane that prevents fuel crossover and subsequently increases the current efficiency. In another study, [PERSON] et al. [53] introduced graphene oxide into Fumion(r)and found that the modified composite membrane with the loading of 0.50 \(w\)/\(\%\) graphene concentration increased the ionic conductivity to 113.27 mS cm\({}^{-1}\) at 80 \({}^{\circ}\)C, outperforming the as-received Fumion(r)that was thermally stable up to 60 \({}^{\circ}\)C only. [PERSON] et al. [33] have also shown that the incorporation of functionalized graphene oxide (FGO) into polyvinyl alcohol polydiallydimethylammonium chloride semi interpenetrating polymer networks (PVA/PDA/SIPNs) improve the OH\({}^{-}\) conductivity and thermo-mechanical stability under high relative humidity. However, the addition of inorganic components in excess into the polymer matrix may produce high numbers of pores, leading to a fragile structure and fuel crossover, and subsequently hindering the performance of the membrane.
## 7 Mechanisms of PGPB (Plant Growth Promoting Bacteria) towards Salinity Tolerance
### Indirect Mechanisms  
#### 7.2.1 ACC Deaminase  
Some microorganisms possess the enzyme 1-aminocyclopropane-1-carboxylate (ACC) deaminase that transforms 1-aminocyclopropane-1-carboxylic acid (the immediate precursor to ethylene biosynthesis in higher plants) into ammonia and \(\alpha\)-ethylene chetobutyrate instead of ethylene [236]. Additionally, as the enzyme ACC deaminase breaks the ethylene substrate ACC, which is transformed into alpha-ketobutyrate and ammonium, it supplies the microorganisms with N [237]. PGPB such as _Rlodococcus_, _Variovorax_, _Alcaligenes_, _Bacillus_, and _Ochroductrum_ have been identified to synthesize ACC deaminase [238], and have been shown to improve plant nodulation under salinity [239]. Recent findings on the synergetic functioning of ACC deaminase and other bacterial mechanisms of salt stress tolerance, such as trehalose accumulation, are also established [240].  
ACC deaminase based PGPB can also reduce the harmful impacts of environmental stress and enhance crop resistance through multiple processes including improvement of mineral solubilization, phytohormone synthesis, leaf area raising, nutrient absorption, enhanced antioxidant enzyme activity, and soluble protein and chlorophyll concentration [241]. Direct demonstration for such processes derives from the observation that _Pseudomonas_ sp. _acdS_ mutants UW4 (ACC deaminase minus) lost the capacity to stimulate canola root extension during genotobiotic growth [242], and that canola plants inoculated with the mutant are not able to develop under salinity [243].  
Salt tolerant PGPBs producing ACC deaminase have impacts on many biochemical characteristics of plant cells, including production of biocompatible solutes, membrane stability, and the synthesis of photosynthetic pigments during salinity stress [244]. For instance, [PERSON] et al. [159] demonstrated that the strain _Streptomyces_ sp. GMKU 336 developing ACC deaminase might improve rice development and salt tolerance by reducing ethylene concentration, scavenging ROS, and preserving ion homeostasis. Biomass and root length of maize plants grown under saline conditions increase in plants inoculated with _P. fluorescens_ possessing ACC deaminase [242]. During abiotic stress, rhizobacteria endowed with ACC deaminase activity may ameliorate plant development by controlling ethylene production [245]. Enhanced ethylene synthesis due to exogenous use of 1-aminocyclopropane-1-carboxylic acid (ACC)or salinity may reduce the root development [246], and subsequently increase plant performance. Similarly, supplementation of legume crops with ACC deaminase-synthesizing rhizobia isolated from saline soils boosted nodule construction [247]. The halophilic bacteria _Microbacterium_ sp., isolated from rhizosphere of rice, is another strain that revealed ACC deaminase function [248].
# Improved Safety Analysis Integration in a Systems Engineering Approach
## 6 Case Study
### Step 3: DFT Generation from System Model  
In the third step, and since we have redundancy in the system, a BDD is created using the redundancy profile in order to describe the relation between redundant components and to facilitate the generation of the DFT. The new (updated) system contains two groups of redundant components with switch. The BDD describes, both, the redundancy of the geared motor and the redundancy of the MCU. Figure 7 describes the redundancy between the two redundant groups. \"MCU1\", \"MCU2\", \"Geared motor 1\", and \"Geared motor 2\" are redundant components. \"MCU1\" and \"MCU2\" are redundant between each other and the redundancy type is standby. \"MCU1\" is primary and \"MCU2\" is secondary. The \"Switch MCU\" is a switch component that allows the switching from \"MCU1\" to \"MCU2\" when failure of \"MCU1\" occurs. In the same way as MCU, \"Geared motor 1\" and \"Geared motor 2\" are redundant between each other and the redundancy type is standby. \"Geared motor 1\" is primary and \"Geared motor 2\" is secondary. The \"Switch Motor\" is a switch component that allows the switching from \"Geared motor 1\" to \"Geared motor 2\" when failure of \"Geared motor 1\" occurs.  
Then, the algorithm of the dynamic fault tree generation uses the structure of the system (IBD) represented in Figure 6 and the redundancy information (BDD) represented in Figure 7. Firstly, the error of the Output port is taken as an undesired event. Secondly, the depth first search through  
Figure 7: Block Definition Diagram (BDD) of Electro-Mechanical Actuator (EMA) system with redundancy information.  
the structure of the system is applied in order to determine the external inputs of the system. In the EMA example, two external inputs are reached which are the \"CtrlData\" and the \"Electric Power\". For each external input, a leaf failure event is generated. Also, the non redundant component errors are represented by a leaf failure event in the DFT. In this example, just the \"Mechanical Transmission\" component is a non redundant component. The two redundancy groups are standby redundancy with switch component, then a subtree of a standby redundancy with switch component is generated as shown in Figure 4. Knowing that the general distribution of the components is in series, all generated subtree are directly related to the main OR gate. The output of the algorithm is a DFT as shown in Figure 8 and it provides all possible failure combinations.
###### Abstract  
With the development of fiber optic sensing technology, optical fiber sensors have been widely used in online monitoring of power transformers. To investigate the influence of aging fiber sheaths on transformer oil, two kinds of special optical fibers with thermoplastic polyester elastomer (TPEE) and poly tetra fluoroethylene (PTFE) as sheaths underwent thermally accelerated aging in transformer oil at 130 \({}^{\circ}\)C. The volume resistivity, dielectric dissipation factor (DDF), and breakdown voltage of the oil were measured to indicate insulation strength. The water content and acid value of the oil were measured and fitted to predict the aging tends. The thermal aging characteristics of the oil were quantitatively compared and results showed two kinds of optical fibers could exacerbate all the physical and chemical parameters of oil, and the TPEe sheath had a more significant impact on the oil. The reasons contributing to such phenomenon were analyzed using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Both TPEE and PTFE were depolymerized in high-temperature transformer oils, producing water and small molecule hydrocarbon groups. The accumulation of small hydrocarbon groups promoted positive feedback of pyrolysis in the oil. The free hydrogen produced by oil pyrolysis increased the acidity of the oil, which in turn increased the solubility of the water produced by sheath depolymerization. The chain depolymerization of TPEE was more severe than that of PTFE, further exacerbating the deterioration of TPEE-containing oil.  
Keywords:transformer; optical fiber; copolymer; thermal aging properties +
Footnote †: journal: Applied Physics Letters
# Compact UHF Circularly Polarized Multi-Band Quadrifilar Antenna for CubeSat
###### Abstract  
This article presents a multi-band right-hand circularly polarized antenna designed for the Cube Satellite (CubeSat). Based on a quadrifilar structure, the antenna provides circular polarization radiation suitable for satellite communication. Moreover, the antenna is designed and fabricated using two 1.6 mm thickness FR4-Epoxy boards connected by metal pins. In order to improve the robustness, a ceramic spacer is placed in the centerboard, and four screws are added at the corners to fix the antenna to the CubeSat structure. These additional parts reduce antenna damage caused by vibrations in the launch vehicle lift-off stage. The proposal has a dimension of 77 \(\times\) 77 \(\times\) 10 mm\({}^{3}\) and covers the LoRa frequency bands at 868 MHz, 915 MHz, and 923 MHz. According to the measurements in the anechoic chamber, antenna gains with the values of 2.3 dBic and 1.1 dBic are obtained for the 870 MHz and 920 MHz, respectively. Finally, the antenna is integrated into a 3U CubeSat that was launched by a Soyuz launch vehicle in September 2020. The terrestrial-to-space communication link was measured, and the antenna performance was confirmed in a real-life scenario.  
circular polarization; CubeSat; multi-band antenna; antenna on structure +
Footnote †: journal: Sensors  
0023233561
## 4 Results and Discussion
### Decomposition with EMD, EEMD, SEMD, and Akima-EMD  
The actual closing prices of the KSE-100 index are decomposed into numerous IMFs and monotone residues by implementing EMD, EEMD, SEMD, and the proposed new method. It can be seen from Figure 5 that the IMFs extracted by implementing the procedure of EMD, EEMD, and the proposed new method are the same, whereas the number of IMFs produced by the SEMD method is less than the benchmark EMD, EEMD, and the new method. The first few IMFs represent high-frequency components in the actual data and have a very low Pearson coefficient of correlation with the original stock prices. Therefore these high-frequency components are not considered to be modeled with the help of the LSTM network, as these sub-parts represent only noise in the stock price data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{1}{c}{**Average**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} \\  & **Trading Days** & & & \\ \hline
2015 & 249 & 33,649 & 28,927 & 36,229 \\ \hline
2016 & 248 & 37,617 & 30,565 & 47,807 \\ \hline
2017 & 249 & 45,621 & 37,919 & 52,876 \\ \hline
2018 & 246 & 42,153 & 36,663 & 46,638 \\ \hline
2019 & 247 & 36,064 & 28,765 & 41,769 \\ \hline
2020 & 251 & 38,311 & 27,229 & 43,767 \\ \hline
2021 & 247 & 45,959 & 42,780 & 48,726 \\ \hline
2022 & 158 & 43,577 & 39,832 & 46,602 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of yearly KSE-100 index closing price.  
As a first step, the KSE-100 index closing prices are decomposed with the help of EMD, EEMD, SEMD, and the proposed new method. After decomposing the data, the Pearson correlation coefficient is used to separate the correlated IMF and residue from the actual stock data. To normalize the correlated IMFs in the range of '0' and '1', the well-known MinMaxScaler is used. This method of normalizing the data subtracts the minimum value from the actual data and then divides it by its range. The key characteristic of MinMaxScaler is that it preserves the shape of the original time series data. It does not meaningfully change the information embedded in the original data. However, in case of any outliers in the data, this method is not recommended, and in such scenarios, the RobustScaler method for normalization is preferred. Following is the mathematical structure of MinMaxScaler.  
\[\dot{y}(t)=\frac{y(t)-min(y(t))}{y(t)-y(t)} \tag{15}\]  
Figure 5: Decomposition results of KSE-100 index with (**a**) EMD, (**b**) EEMD, and (**c**) SEMD (**d**) Akima-EMD.  
where \(y(t)\) denotes the corresponding normalized IMF and monotone residue that we used to build our proposed hybrid LSTM network.
###### Abstract  
Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.  
sustainable development goals; smallholder; maize; Sentinel-1; principal component analysis; SVM; Xgboost Article
## 4 Design
### Topic Drift Monitoring  
This module penalizes the reputation of a topic for delays in data arrival. The _TDM_ keeps track of how well data publishing stays on schedule. Initially, publishers specify their intended publication intervals (\(\delta\)) for each topic. The _TDM_ then monitors when each registered topic's data are published to ensure that it aligns with the intended interval (\(\delta\)). Let us assume that \(t_{u}^{\lambda}\) is the time at which the \(u^{th}\) data point has arrived, \(t_{0}^{\lambda}\) denotes the reference time from when the drift is measured, which is the beginning of the drift time window \(\lambda>\delta\). The \(u^{th}\) data for \(\tau\) must arrive at \(t_{0}^{\lambda}+u\delta\) time, that is,  
\[t_{u}^{\lambda}=t_{0}^{\lambda}+u\delta \tag{4}\]  
where \(\lambda=\lambda_{\tau}\), from \(C\) in Equation (3). Based on this, the drifting score \((D_{\tau})\) is created as shown in Equation (12).  
\[D_{\tau}=\begin{cases}\begin{array}{c}\left|t_{u}^{\lambda}-\left(t_{0}^{ \lambda}+u\delta\right)\right|>\mu\\ \left|t_{u}^{\lambda}-\left(t_{0}^{\lambda}+u\delta\right)\right|=0\\ \text{Otherwise }\end{array}&1-\frac{\left|t_{u}^{\lambda}-\left(t_{0}^{ \lambda}+u\delta\right)\right|}{\mu}\end{cases} \tag{5}\]  
where tolerance \(\mu\geq\delta\). If in Equation (12), both sides are equal, that is, the data are arriving at the correct time, the \(D_{\tau}=1\), that is, when everything is perfect since \(t_{0}^{\lambda}\). If the delay in the data is more than \(\mu\), then the \(D_{\tau}=0\), which means that the data are no longer there and reliability with respect to time is zero. If \(0<\left|t_{u}^{\lambda}-\left(t_{0}^{\lambda}+u\delta\right)\right|<\mu\), then, the last part returns a score between 0 and 1. This drifting score, D\({}_{\tau}\), is used by the TRS further to calculate the overall topic reputation score. D is calculated at every interval of \(\delta\).  
The drift in the topic can cause some unwanted problems, as it could occur that in a particular window, there is not enough data to calculate the quality. In such cases, the \(D\) would already become 0, and the quality calculations would be impacted. The value of \(\mu\) should be set in a way such that the system quickly recognizes that excessive drift is occurring, resulting in difficulties with quality calculations as discussed below.
## 3 Numerical Calculation Application and Experimental Setup
### Rotordynamics Model Setup
#### 3.4.3 Mesh and Material Attribution  
As shown in Figure 7, the main shaft meshed into high-quality structural hexahedral grids. The minimum grid size was set 0.002 m and each radial section was assured to have at least 20 layers of elements in order to assure solving accuracy. Local grids at ends of shaft were zoomed up and positions of the bearing were marked with 'K0'.  
The main shaft was made of structural steel, whose physical properties were listed in Table 4. According to the data provided by the manufacturer, the rigidity of the rolling bearings was set \(5\times 10^{9}\) N/m, and the damping can be ignored.
## 3 Methods
### Loss Functions  
The loss function of the WGAN-GP model proposed in this paper, which considers both pixel accuracy and the quality of the generated image, is expressed as follows:  
\[Loss_{WGAN}=E_{x_{ref}}\left[C\left(x_{ref}\right)\right]-E_{G\left(x_{ref}x_{ target}\right)}\left[C\left(G\left(x_{ref},x_{target}\right)\right)\right]\]  
where \(C(x_{ref})\) is the value that the Critic calculates for the reference image (\(x_{ref}\)). \(C(G(x_{ref},x_{target}))\) is the value the C calculates for the image generated by the G using the reference image (\(x_{ref}\)) and the target image (\(x_{target}\)). This value assesses how closely the generated image resembles the real image.  
The loss function operates by having the C take both the reference image (\(x_{ref}\)) and the generated image \(G(x_{ref},x_{target})\) as inputs, determining which of the two is the real image (\(x_{ref}\)) and which is the generated image \(G(x_{ref},x_{target})\).  
The G takes a reference image (\(x_{ref}\)) and a target image (\(x_{target}\)) as inputs and generates an image that reflects the style of the reference image (\(x_{ref}\)) while preserving the structure or content of the target image (\(x_{target}\)).  
The C aims to maximize the value of \(C(x_{ref})\) and minimize the value of \(C(G(x_{ref},x_{target}))\), meaning it seeks to evaluate the reference image (\(x_{ref}\)) as real and the generated image as not real. Conversely, the G tries to maximize the value of \(C(G(x_{ref},x_{target}))\), aiming for the generated image (\(G(x_{ref},\ x_{target})\)) to be perceived as a real image.  
The model simultaneously learns the color distribution and texture of the target image. In this process, the C evaluates both aspects of color consistency and structure. This learning is crucial in aviation simulators because maintaining the color consistency and structural integrity of terrain images provides pilots with an accurate and immersive training environment. If either the color or texture is not accurately represented, the effectiveness of the aviation simulation could be compromised.  
By repeating this process, the G learns to produce images that increasingly reflect the style of the reference image (\(x_{ref}\)) while also preserving the content of the target image (\(x_{target}\)).  
However, it is important to note that while the WGAN-GP model provides superior results in terms of color consistency and texture preservation, it comes with a higher computational cost compared to simpler models such as histogram matching and basic color transfer. Training the WGAN-GP model requires more computational resources, including multiple high-performance GPUs and an extended training time. This makes it suitable primarily for applications where the accuracy and quality of color correction are critical, such as high-fidelity aviation simulators, rather than for use cases where computational efficiency is the priority. By understanding these trade-offs, practitioners can make informed decisions on whether the benefits of WGAN-GP justify its use based on the specific requirements and constraints of their application.