esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
## 3 Mechanisms of Action of Phytobiotics Against Foodborne Pathogens
### Inhibition of Protein and DNA Synthesis  
As previously noted, phytobiotics inhibit bacterial cell wall synthesis; however, their activity extends further into molecular pathways such as protein biosynthesis and DNA replication, ultimately resulting in growth arrest and bacterial cell death. It was observed long ago that extracts from _Phytolacca dodecandra_ contain ribosome-inactivating proteins (RIPs) capable of inactivating the 60S ribosomal subunit, thereby effectively inhibiting translation [140]. Phenolic compounds and plant-derived alkaloids can interact with bacterial ribosomes, disrupting translation by altering the conformation of the ribosomal active site [115]. Beginning with translation, the flavonoid quercetin (found in _Calendula officinalis_) inhibits peptidyl transferase and peptide deformylase activity, resulting in the arrest of both initiation and maturation of polypeptide chains, as demonstrated in _S. aureus_[141]. In later translation stages, plant-derived compounds can act analogously to kirromycins by binding elongation factor Tu (EF-Tu) and halting polypeptide elongation [142]. Some plant extracts also affect the expression of key ribosomal proteins, including 30S subunit protein S1 and the GroEL chaperonin (60 kDa), thereby destabilizing the entire translational machinery [143]. With regard to DNA, phytobiotics are capable of inhibiting bacterial genome replication. Plant polyphenols effectively suppress the activity of DNA gyrase, an enzyme critical for DNA replication and transcription [135]. Isothiocyanates, such as sulforaphene and phenethyl isothiamate, have demonstrated strong inhibitory effects on DNA synthesis in _Bacillus subtilis_ and _E. coli_[144]. For example, andrographolide has been shown to inhibit DNA replication in _S. aureus_ and to prevent biofilm formation [145]. Additionally, recently isolated phenolic saccharides from _Polygonum cuspidatum_ were found to block the activity of DNA primase, an enzyme essential for replication initiation [146]. Coumarins such as scopoletin and daphnetin have also been shown to inhibit the GTPase activity of the FtsZ protein, a key factor in bacterial cell division and protein synthesis, thereby disrupting cytokinesis [147]. Furthermore, alcoholic extracts from _Achillea millefolium_ flower heads demonstrated the ability to inhibit protein synthesis in _E. coli_ cells [148].
# Phtyromediation of Toxic Metals: A Sustainable Green Solution for Clean Environment
## 5 Phytoremediation and Basic Types
### Rlizo/Plyto-Filtration  
Phyto-filtration is a method that reduces metal mobility in sediment by eliminating pollutants from the aqueous environment [120; 121]. Plants (Table 3) remove toxins from polluted water by absorbing them in their roots throughout this process [122; 77]. According to the plant parts employed, phyto-filtration can be classed as rhizofiltration (using plant roots), blastofiltration (using seedlings), or caulofiltration (using plant shoots) [123; 124]. Toxic metals such as Pb, Cd, Cu, Ni, Zn, and Cr can be easily removed from the environment using the rhizofiltration process [109]. Phyto-filtration works best in coastal areas with high root biomass aquatic plants [93; 125]. For rhizofiltration, terrestrial plants with fibrous root systems and rapid growth are preferred [109].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process** & **Plant Species** & **Metals** & **References** \\ \hline Phytoextraction & _Acanthus licífolius_ & Cu, Pb, Ni, Cr & [101; 126] \\  & _Alyssum bertoloni_ & Ni & [127] \\  & _Avicientia alba_ & Pb, Cd, Cr & [101; 128] \\  & _Brassica juncea_ & Pb, Cu, Zn & [114] \\  & _Eshlotlza splendens_ & Cu, Zn, Pb, Cd & [127; 128] \\  & _Helianthus anmus_ & Cd, Ni, Pb, Zn & [129; 130; 131; 132] \\  & _Noccaea aerulescens_ & Zn, Pb & [133] \\  & _Pisum sativum_ & Cd, Fe & [134] \\  & _Pteris vitalta_ & As, Cu, Cr & [135] \\  & _Ricinus communis_ & Co, Ni, Mn, Cu, Pb & [135] \\  & _Tagetes_ & Cd, Pb, Zn & [129] \\  & _Thlaspi caerulescens_ & Cd, Ni & [135; 136] \\  & _Verbena_ & Pb, Cd & [137; 138; 139] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lists of plants which can be used in different phytoremediation techniques.
## 5 Simulation Verification
### Impact of DC Side Capacity  
Figure 15 shows the simulation results of the VSC-HVDC system with a virtual impedance control strategy under a different DC side capacity. The voltage control parameters are \(k_{pcdc}\) = 15, \(k_{icdc}\) = 100, \(k_{i}\) = 10, \(R_{eq}\) = 0.5 \(\Omega\), and \(L_{eq}\) = 0.002 \(\Omega\). At 2 s, the steady-state power command value is set from 500 MW to \(-\)500 MW. As shown in Figure 15, the blue line indicates the simulation results when the DC side capacity is 300 uf, and the red line indicates 450 uf. The DC voltage in the blue line drops by approximately 5% and begins to oscillate, and the system loses stability. The DC voltage in the red line drops by approximately 5%, smoothly restoring the instruction value, and the system remains stable. The simulation results are consistent with the theoretical analysis in Section 4.5.
## 4 Closed-Form Solutions for FG Nano-Rods  
Closed-form elastic solutions for FG nano-rods with a clamped end at \(x=0\) and a free end at \(x=L\) and with both clamped ends are presented hereafter. The applied loads are a uniform load \(p\), a concentrated force \(\mathcal{F}\) at \(x=L\) (or an imposed axial displacement \(\delta\) at \(x=L\) for doubly-clamped rods). Kinematic and static boundary conditions are enforced to the FG nano-rod ends according to classical rod theory and, in addition, the constitutive boundary conditions Equation (26) are imposed according to the proposed NSG model. Hence, the axial displacement \(u\) can be recovered following Steps 1-4 in Section 3.  
In the sequel, the abbreviations \(CF\) and \(CC\) stand for clamped-free anddamped-clamped, respectively. Moreover, let us assume that the elastic area \(A_{E}\) is constant along the nano-rod axis \(x\).  
For completeness sake, the FG nano-rod constraints, the considered applied load and the related boundary conditions are reported in Table 1.  
To provide a non-dimensional analysis of FG nano-rods, the following non-dimensional variable \(\xi\) and the non-dimensional characteristic parameters \(\lambda\) and \(\mu\) are adopted in the examples  
\[\xi=\frac{x}{L},\ \ \ \ \ \ \lambda=\frac{L_{c}}{L},\ \ \ \ \ \ \mu=\frac{l}{L}. \tag{28}\]  
The non-dimensional axial displacement \(\bar{u}\) depends on the kind of load applied to the nano-rod according to Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**FG Nano-Rod Constraints—Applied Load**} & \multicolumn{2}{c}{**Boundary Conditions**} \\ \cline{2-3}  & _Classical_ & _Constitutive_ \\ \hline \(CF-\mathcal{F}\) & \(u(0)=0\) & \(\frac{F}{L_{c}}+\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=0}=0\) \\  & \(N(L)=\mathcal{F}\) & \(-\frac{F}{L_{c}}+\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=L}=0\) \\ \hline \(CF-p\) & \(u(0)=0\) & \(\frac{pL}{L_{c}}+\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=0}=-p\) \\  & \(N(L)=0\) & \(\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=L}=-p\) \\ \hline \(CC-p\) & \(u(0)=0\) & \(\frac{a_{1}}{L_{c}}+\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=0}=-p\) \\  & \(u\left(L\right)=0\) & \(\frac{1}{L_{c}}\left(px-a_{1}\right)+\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=L}=-p\) \\ \hline \(CC-\delta\) & \(u(0)=0\) & \(\frac{a_{1}}{L_{c}}+\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=0}=0\) \\  & \(u(L)=\delta\) & \(-\frac{a_{1}}{L_{c}}+\frac{l^{2}}{L_{c}^{2}}A_{E}\ \ \partial_{x}^{2}u\left(x\right)\big{|}_{x=L}=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fundamental schemes with corresponding boundary conditions.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Notes on Code and Algorithms  
Although the entire model creation process was carried out using Python programs and libraries, the results are represented by coefficients for a multivariate polynomial, stored in column-oriented._parquet_ files. This means that the noise figure evaluation can be performed using any programming language or tool capable of reading these files and performing basic mathematical operations. This ensures excellent portability for the model, offering maximum flexibility in creating an interface. A Python interface was chosen for the model due to the language's widespread use and its seamless integration with powerful libraries such as _Pandas_ and _NumPy_[29]. This interface is implemented as a Python class, providing attributes and methods to enhance ease of use and allowing integration into more comprehensive EDFA models. The models are named using the pattern \(\textbf{X}\_\textbf{Y}\_\textbf{Z}\), where X represents the device brand and specific model, Y denotes the gain range, and \(Z\) indicates the number of active channels in the training datasets of the model. This number is crucial for two reasons. First, for more accurate results, it is ideal to select a model trained with a spectral load similar to the one being estimated, to mitigate potential effects from different channel configurations. Second, the input parameter that the model accepts is the total power of all active channels. Therefore, when estimating the noise figure for a spectrum with a different number of channels, this difference needs to be considered. It is important to remember that the input power parameter refers to the total power of all active channels. Therefore, a conversion is necessary if the data being used correspond to a different number of channels:  
\[P_{IN_{ADJ}}=P_{IN}-10\log_{10}(N_{current})+10\log_{10}(N_{model}) \tag{19}\]  
where \(N_{current}\) and \(N_{model}\) represent the numbers of active channels in the configuration to be estimated and in the model training datasets, respectively.
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 2 Materials and Methods
### Methods  
#### 2.2.1 Experimental Design  
In order to analyze the effect of HY-2 series satellites to detect mesoscale phenomena, we selected three \(12^{\circ}\times 12^{\circ}\) boxes for our study areas: the north Atlantic at high latitudes, the northwest Pacific at middle latitudes and the north Indian Ocean at low latitudes. It is worth noting that we set the latitude coverage of high latitudes to 52-60\({}^{\circ}\), which was limited by the maximum latitude coverage of HY-2C and Jason-3 orbits. The selection scheme of the regions is a balance of the East-West and North-South hemispheres, involving coverage at different latitudes and ranging from areas of intense sea surface variability to areas of gentle sea surface variability.  
The workflow of the experiment is shown in Figure 1. For each region, we performed two groups of satellite simulation sampling on HYCOM data. Next, we used the OI algorithm to transform the satellite along-track data into SSH gridded products. Then, the eddy identification algorithm was applied to the models and two groups of synthetic gridded products to obtain eddy datasets. Finally, we evaluated the mapping capability of HY-2 series satellites on mesoscale eddies by comparing along-track coverage, OI gridded product precision and the mesoscale eddies identification of two groups of constellations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Satellite** & **Agency** & **Launch** & **Inclination** & \multicolumn{2}{c}{**Sun-**} & \multirow{2}{*}{**Waveband**} & \multirow{2}{*}{**Repetitive**} \\ \hline HY-2B & NSOAS & 2018.10 & 99.3\({}^{\circ}\) & Yes & Ku/C & 14 day \\ HY-2C & NSOAS & 2020.09 & 66\({}^{\circ}\) & No & Ku & 10 day \\ HY-2D & NSOAS & 2021.05 & 66\({}^{\circ}\) & No & Ku & 10 day \\ Jason-3 & NOAA\&NASA\& & & & & \\ CNES\&EUMETSAT & 2016.01 & 66\({}^{\circ}\) & No & Ku/C & 10 day \\ Sentinel-3A & NOAA\&NASA & 2016.02 & 98.6\({}^{\circ}\) & Yes & Ku/C & 27 day \\ Sentinel-3B & NOAA\&NASA & 2018.04 & 98.6\({}^{\circ}\) & Yes & Ku/C & 27 day \\ \hline \hline \end{tabular}
\end{table}
Table 1: Altimetry satellite information of HY-2B/C/D, Jason-3 and Sentinel-3A/B.
## 2 Physical Properties and Hybrid Graphene/Silicon Fabrication Processes  
Graphene, a single-atomic-layer system consisting solely of carbon atoms formed in a hexagonal lattice, holds several distinctive physical properties owing to its unique linear energy dispersion relation [17]. These properties make it an ideal enhancement towards the silicon modulators and detectors. Among these properties, the ultrahigh carrier mobility of graphene is most widely exploited since it enables the ultrafast silicon modulator and photodetector [12; 18; 19; 20]. Moreover, thanks to the gapless nature of graphene, it absorbs photons in wavelengths ranging from the visible to the infrared. This is highly favorable for photodetection operating in the telecommunications C-band (1530-1565 nm), in which silicon is transparent. Furthermore, graphene's extraordinarily large heat conductivity and relatively low light absorption can be exploited in high-performance nanoscale thermos-optic phase shifters within silicon photonics. These properties combine to exhibit superior performance in terms of the tuning efficiency and response time [21; 22; 23; 24; 25], when compared to traditional p-i-n or metallic microheater structures, which will be discussed in detail in the Section 3.  
The fabrication process of the silicon/graphene hybrid devices typically consists of three steps. The first step is the fabrication of passive silicon photonic circuit. In this step, the grating coupler, waveguides, and other passive elements is fabricated using a lithographic and etching processes. Subsequent planarization then allows the graphene to be placed flat on top of the silicon structures. Generally, graphene is first deposited with a chemical vapor deposition (CVD) process on a cooper coil. Then, it is then transferred onto the target area of the chip using a wet or dry transfer process [26]. This step is crucial, and the most challenging, as the quality of the transfer (and therefore of the graphene sample on the device) determines the performance of the graphene-based components on the chip. The third step is the patternization and metallization of the silicon/graphene hybrid chip, which is achieved by standard CMOS fabrication processes. In order to reach ideal Ohmic contact between the graphene and the metal pad, the type of the metal should be carefully chosen since large contact resistance can lead to large \(RC\) constant thus low operation speed of the device. Normally, titanium (Ti), palladium (Pd) and platinum (Pt) are ideal choices since they hold low contact resistance with graphene [27].
# Integrating Geometric Dimensioning and Tolerancing with Additive Manufacturing: A Perspective
## 4 Digital Thread and MBD Integration
By analyzing data from previous builds, machine learning models can anticipate deviations and adjust process parameters, leading to improved accuracy and reduced need for post-processing. This data-driven approach enhances the reliability and efficiency of AM processes [29]. MBD is used by firms such as Lockheed Martin, Boeing, and Baker Hughes to include GD&T into AM component designs, guaranteeing adherence to stringent aerospace tolerances throughout manufacturing and inspection. For AM parts, automakers such as Ford and BMW use MBD and GD&T integration to speed up prototyping and guarantee dimensional correctness during assembly.
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 2 Materials and Methods
### Aann  
AANNs are multi-layer perceptron networks, i.e., feedforward NNs, composed of layers of units that have nonlinear activation functions. As shown in Figure 6, the number of nodes in the input layer coincides with the number of nodes in the output layer. Unlike a standard topology, an AANN uses three hidden layers, including an internal bottleneck layer of smaller dimension than either input or output. Therefore, the network has a symmetrical structure that can be viewed as two successive functional mappings: the first half of the network maps the input vector to a lower dimensional subspace via the encoder module, while the second decodes it back via a decoder module. The dimension of the lower subspace is determined by the number of units in the bottleneck layer that plays the key role in the functionality of the AANN: data compression caused by the network bottleneck may force hidden units to represent significant features in the input data [8]. The number of neurons to be considered in the hidden layers is a critical choice, in particular in the bottleneck layer: a small number of units means a significant dimensionality reduction in the input data, but, if the number is too small, the input-output associative capabilities of the net are too weak.  
Further details regarding the AANN architecture used in this study are described in Section 2.5.
## 3 Experimental Results
### Failure Mode  
For the steel pipe specimens in Figure 10, little horizontal displacement and no obvious deformation were observed at the preliminary loading stage. Upon reaching the peak load, the horizontal displacement in the middle of the specimen increased nonlinearly, and the bearing capacity of the specimen decreased gradually with lateral bending occurring in the horizontal displacement plane until it lost its overall stability.  
All the CFST specimens without prestress presented elastoplastic instability failure, with more significant lateral bending occurring in the middle of the specimens at small diameters. Low horizontal displacement was produced at the elastic stage and increased slowly with the load raising before it developed rapidly after reaching the peak load. Similarly, instability in the steel pipes appeared when the specimen failed.  
The CFST specimens with prestress exhibited little difference from non-prestress members, with the failure mode still dominated by instability at the peak load. The initial stress caused the reinforced steel pipes to enter the plastic stage in advance, which occurred earlier under larger initial stress. For specimens with larger diameters and relatively smaller aspect ratios, the early plastic state of the steel pipe reduced its connection to the RPC, and local buckling accompanied by oblique shear failure occurred at the bottom of the specimen in the bearing capacity decline section, as shown in Figure 11. The strengthening  
Figure 10: The overall failure mode of the specimen by type. (**a**) Non-reinforced, IS0; (**b**) IS0; (**c**) IS4; (**d**) IS6; (**e**) IS8.  
measure around the grouting hole ensured that local buckling occurred there later than the overall instability, though tiny cracks in the steel adhesive around the reinforced arc plate appeared for most specimens, and a few reinforced arc plates peeled off, which resulted in a small area of crippling at the edge of the grouting hole.
## 4 Engineering Example
### Results and Analysis
#### 4.3.2 Comparative Analysis of the Performance of Different Prediction Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Index** & **Prediction Model** & \multicolumn{4}{c}{**Prediction Step Size**} \\  & **1** & **3** & **6** \\ \hline \multirow{3}{*}{MAE (\(\times 10^{-3}\))} & Adaptive MTGNN & 1.487 & 1.750 & 1.924 \\  & Non-adaptive MTGNN & 1.597 & 1.993 & 2.313 \\  & GRU & 1.718 & 2.188 & 2.625 \\  & LSTM & 1.820 & 2.394 & 2.836 \\ \hline \multirow{3}{*}{RMSE (\(\times 10^{-3}\))} & Adaptive MTGNN & 1.856 & 1.974 & 2.106 \\  & Non-adaptive MTGNN & 1.941 & 2.186 & 2.521 \\  & GRU & 2.081 & 2.298 & 2.760 \\  & LSTM & 2.172 & 2.481 & 2.923 \\ \hline \multirow{3}{*}{MAPE (\%)} & Adaptive MTGNN & 8.261 & 8.906 & 9.533 \\  & Non-adaptive MTGNN & 8.697 & 9.617 & 10.852 \\ \cline{1-1}  & GRU & 8.992 & 10.032 & 11.649 \\ \cline{1-1}  & LSTM & 9.508 & 10.849 & 12.257 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of predictions of different models at different prediction steps. center of the radar chart. As the prediction step size increases, the error increment is the smallest, and the error improvement of each index is also gradually improved, indicating that the \"chunked progressive\" training strategy adopted in this paper has an outstanding advantage in multi-step prediction. In Tables 3 and 4, the prediction results of the adaptive MTGNN are consistently better than those of the non-adaptive MTGNN, which reveals the significant advantage of the adaptive adjacency matrix in learning the spatial features of the corrosion rate. When the adjacency matrix in MTGNN is a static unit matrix, the node information can only be propagated within each node itself, and there is no effective information aggregation with other nodes, so it is difficult for the model to learn the changing patterns of corrosion information in space. Adaptive MTGNN overcomes the shortcomings of artificially defining the graph structure and can dynamically adjust the connection weights between nodes according to the inputs, thus improving the prediction accuracy of the region as a whole. Figure 11: \(Cont.\)
## 3 Field Test Analysis  
### ERT Data Interpretation Workflow  
Field testing was conducted using the \"WCMD-9 High-Density Resistivity System\", yielding extensive apparent resistivity datasets. During data acquisition, anomalies may arise from the following: (1) poor electrode contact with brick/gravel substrates in grassy areas, (2) power supply interference, or (3) high-resistivity surface obstructions (e.g., concrete/asphalt pavements). Such anomalous high-resistivity data were systematically eliminated to minimize interpretive bias. Post-electrode anomaly removal, datasets exhibiting voltage > 5000 mV or current > 1000 mA--attributable to overcurrent or electromagnetic interference--were further excluded. To mitigate random errors and verify data reliability,  
Figure 3: The technical workflow of this research.  
each survey line was measured twice, producing dual apparent resistivity (\(\rho\)s) values per spatial measurement point. Data consistency was quantified using the sample standard deviation formula:  
\[S=\sqrt{\frac{1}{N-1}{\sum_{i=1}^{N}{(X_{i}-\overline{X})}^{2}}} \tag{3}\]  
The standard deviation (S) represents the dispersion of the two measurements (\(X_{1}\)) and (\(X_{2}\)), while \(\overline{X}\) denotes their mean value. The percentage standard deviation (M), calculated as the ratio of S to \(\overline{X}\), quantifies the relative deviation of the dual measurements from the mean apparent resistivity. Data points with M > 5% were discarded to ensure signal stability. The retained data were converted into inversion-compatible formats, requiring parameter specifications including the measurement method, electrode count, and electrode spacing during format conversion.