esa-sceva/satcom-chunk-collection huggingface dataset:
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 3 Experiments and Results
### Results and Analysis
#### 3.2.3 Comparison of Global Structural Feature Retention
Linear density was used as an indicator for evaluating the ability of each method to retain global structural features [43]. This indicator was defined as Equation (12). \[Linear\ Density\left(L_{i}\right)=\frac{Length(L_{i})}{Area(Buffer(L_{i}))} \tag{12}\]  
where \(Length(L_{i})\) represents the length of the simplified contour, and \(Area(Buffer(L_{i}))\) represents the value of the buffer area of the simplified contour. According to [PERSON] [44], the standard position deviation reflects the global deviation of a simplified line from the  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Initial Contour ID** & **Number of Groups** & \(S_{d-ID}(i,DP,CCSM)\) & \(S_{d-ID}(i,DP,WM)\) & \(S_{d-ID}(i,WM,CCSM)\) & **MAAV** \\ \hline
1 & 4 & 0.0004 & 0.0020 & 0.0020 & 0.0020 \\
2 & 2 & 0.0010 & 0.0006 & 0.0005 & 0.0010 \\
3 & 2 & 0.0007 & 0.0006 & 0.0013 & 0.0013 \\
4 & 3 & 0.0003 & 0.0010 & 0.0  
initial line. Therefore, the standard deviation of the position was designated as the width parameter in the buffer operation. The linear densities were calculated separately for the simplified contours at the same scale in each group of the 20 initial contours. The average values of each initial contour linear density corresponding to the three methods are shown in Figure 11. According to [PERSON] [43], a higher linear density indicates a better overall trend retention rate. As shown in Figure 11, the DP algorithm, WM algorithm, and CCSM show differences for each contour group. The linear density value obtained by CCSM was higher than that obtained by the DP algorithm, indicating that the former has a better global structure retention ability than the latter. Several contour groups of the linear densities of CCSM were significantly better than those of the WM algorithm (such as groups 6#, 8#, 13#, and 15#, etc.). Overall, CCSM is superior to the other two methods in terms of global structural feature retention.
## 6 Challenges and Limitations
### Model Generalizability and Bias  
AI models trained on specific datasets may not perform well when applied to new populations or settings. This lack of generalizability can result from overfitting to the training data or from biases present in the data. Algorithmic bias is a significant concern, particularly in pediatric populations where variability in growth and development is high [60; 61]. If models are trained predominantly on data from certain demographic groups, they may not perform adequately for others, leading to disparities in care. Mitigation strategies include the following:  
Diverse Training Data: Ensuring that datasets represent a wide range of patient demographics, clinical presentations, and imaging characteristics;  
Cross-Validation: Using techniques like k-fold cross-validation to assess model performance on different subsets of data;  
External Validation: Testing models on independent datasets from other institutions or populations to evaluate generalizability;Bias Detection and Correction: Implementing methods to identify biases in model outputs and adjusting algorithms accordingly.
## 3 Rice Phytoalexin Biosynthesis  
### Biosynthesis of Rice Diterpenol Phytoaleoxins  
All isoprenoids are derived from the common five-carbon precursor isopentenyl diphosphate (IPP) and its allylic isomer dimethylallyl diphosphate (DMAPP). In plants, IPP and DMAPP are biosynthesized through two independent pathways: the meval \begin{table}
\begin{tabular}{l l l l} \hline
**Species** & **Family** & **Part of the Plant** & **Reference** \\ \hline _Dicerothamus rhinocerotis_ Less. & Asteraceae & Leaves & [89] \\ _Prunus avium_ L. & Rosaceae & Sweet cherry & [90] \\ \hline _Viscum album_ L. & Santalaceae & Tinctures & [91] \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._onate (MVA) pathway, which occurs in the cytosol, and the methylerythnitol-4-phosphate (MEP) pathway, localized in the chloroplast (Figure 5). Certain isoprenoids, such as monoterpenes, diterpenes, carotenoids, tocopheroids, and the side chains of chlorophylls, are formed via the MEP pathway, while others, such as phytosterols, sesquiterpenes, triterpenes, and the side chain of ubiquinone are biosynthesized via the MVA pathway [101]. Several lines of evidence suggest that the MEP pathway is involved in the biosynthesis of diterpenoids [102; 103], although some MEP-MVA crosstalk could not be ruled out [104; 105]. Microarray analysis showed a clear correlation between the expression of MEP pathway genes and the accumulation of diterpenoid phytoalexins in elicitor-treated suspension cells [102]. Isopentenyl diphosphate \(\Delta\)-isomerase (IPPI) catalyzes the reversible conversion of IPP to DMAPP. To convert IPP to DMAPP in the cytoplasm, IPPI is required, and in the absence of this enzyme, the MEP pathway is blocked. In contrast, IPPI is not strictly essential in plastids where a mixture of DMAPP and IPP is produced from 4-hydroxy-3-methylbut-2-enyl diphosphate (HBMPP) by the last enzyme in the MEP pathway HMBPP reductase (HDR) [106]. Two IPPI isoforms, OsIPPI1 and OsIPPI2, have been identified in rice. The subcellular localization of OsIPPI1 and OsIPPI2 was recently investigated by [PERSON] et al. [101] by constitutively expressing these enzymes fused to synthetic green fluorescent protein (sGFP). Although both isoforms were detected in the endoplasmic reticulum (ER), mitochondria and peroxisomes, only OsIPPI2 was identified in plastids. However, _OsIPPI2_ gene expression did not correlate with chlorophyll or carotenoid accumulation in plastids, suggesting that it may be a redundant component of the MEP pathway. Colocalization of both OsIPPI1 and OsIPPI2 in the ER suggests that DMAPP may be synthesized _de novo_ in this compartment in rice [101]. Figure 5: Biosynthesis of IPP and DMAPP through MVA and MEP pathway in plants. Although IPP and DMAPP act as precursors for all terpen, sesqui-, and triterpenes biosynthesis typically is fed by the cytosolic MEV pathway, while mono-, di-, and triterpenes biosynthesis typically is fed by the plastidial MEP pathway. AACT: acetoacetyl-CoA thiolase (EC 2.3.1.9); CMK: 4-(cytidine 5′-diphospho)-2-C-methyl-D-erythhtol kinase (EC 2.7.1.148); DMAPP: dimethylallyl diphosphate; DPTS: diterpene synthase (EC.
# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 4 Evaluation and Analysis
### Experimental Analysis
#### 4.2.2 Comparative Experiment  
Figure 6 shows the results of comparative experiments conducted on three datasets using Recall@10 and NDCG@10 as evaluation indicators. DCFECF performed the best on all three datasets, and among the four benchmark comparison models, the DCCF model performed the best. The NGCF model uses a multi-layer graph convolutional network to aggregate higher-order neighborhood information in the local structure, which consistently performs better than the GC-MC model using a single-layer graph convolutional network. The LR-GCCF model is simplified on the basis of the NGCF model. The activation function and parameter matrix are removed, and the model performance is improved. Both DCFECF model and DCCF model use the single-layer graph convolution network to calculate, suppress the over-smoothing problem, effectively filter neighborhood information, and use clustering to construct global interaction graph, which overcomes the problem that interaction information is limited to the local neighborhood, and has better performance than NGCF model, GC-MC model, and LR-GCCF model. Compared to the DCCF model,  
Figure 5: Model performance under different values of \(\lambda\) in the time factor expression.  
the DCFECF model achieved a maximum improvement of 2.3% and 4.1% on three datasets based on two evaluation indicators, indicating that improvements to the DCCF model can further improve the algorithm recommendation performance.
# Oxidation in Low Moisture Foods as a Function of Surface Lipids and Fat Content
## 4 Discussion
### Effect of Fat Concentrations on Lipid Oxidation  
When lipid oxidation was examined as a function of fat concentration, both hydroperoxide (Figure 5) and hexanal (Figure 6) formations increased with increasing fat content. Confocal imaging (Figure 7) showed that the fat increasingly masked the images of both the starch (black oval images) and protein (orange) as fat content increased [10]. This suggests that the high-fat concentrations were producing a thick continuous fat layer around the starch and protein, whereas the fat was more dispersed within the fat and starch at low-fat levels. The increased oxidation rates observed in the high-fat crackers could be due to the lipid acting more like a bulk lipid, where free radicals can more easily diffuse and oxidize unsaturated fatty acids. However, since crackers with increased lipids were made by replacing water with fat, this resulted in different water activity rates, with water activity decreasing from 0.65 to 0.10 from 0.1% to 20% fat. [PERSON] et al. (2020) found that decreasing water activity increased oxidation rates in this cracker model [12]. This could also help explain the shorter hydroperoxide and hexanal lag phases in the high-fat crackers.  
Figure 7: Confocal microscopy images of the crackers with (**a**) 0.1%, (**b**) 1.0%, (**c**) 8.1%, and (**d**) 20.0% fat content. Proteins are stained with Rhodamine B (seen in red), and lipid phases are stained with BODIPY (seen in green).
# Fault Detection Algorithm Based on Dynamic Global-Local Preserving Projection
## 3 Fault Detection Based on DGLPP
### DGLPP Algorithm
\begin{array}{ll}J_{GLPP}(a)&=\min\limits_{a}\frac{1}{2}\{\eta J_{Lobal}(a)+(1- \eta)J_{Clocal}(a)\}\\ &=\min\limits_{a}\frac{1}{2}\{\eta\sum_{ij}\left(y_{i}-y_{j}\right)^{2}W_{ij }-(1-\eta){\sum_{ij}\left(y_{i}-y_{j}\right)^{2}\hat{W}_{ij}\}\\ &=\min\limits_{a}\frac{1}{2}{\sum_{ij}\left(y_{i}-y_{j}\right)^{2}R_{ij}} \end{array}  \tag{11}\]  
where \(\eta\in[0,1]\) is determined using the spectral radius \(\rho(L)\) and \(\rho(\hat{L})\) of the matrices \(L\) and \(\hat{L}\) corresponding to the global and local manifold structure, as follows:  
\[ \begin{array}{ll}\eta=\frac{\rho(\hat{L})}{\rho(L)+\rho(\hat{L})}.\end{array}  \tag{12}\]  
Different tradeoff parameters \(\eta\) correspond to different degrees of preservation and extraction of local and global manifold structure information. After the weight coefficient \(\eta\) is determined, the \(R_{ij}\) in Equation (11) is also obtained. Equation (13) can be further simplified using algebraic transformation as follows:  
\[ \begin{array}{ll}J_{GLPP}(a)&=\min\limits_{a}\frac{1}{2}{\sum_{ij}}\left(y_ {i}-y_{j}\right)^{2}R_{ij}\\ &=\min\limits_{a}\{\sum\limits_{i}y_{i}H_{ii}y_{i}^{\top}-\sum_{ij}y_{i}R_{ij }y_{i}^{T}\}\\ &=\min\limits_{a}\{\sum\limits_{i}a^{T}x_{i}H_{ii}x_{i}^{\top}a-\sum_{ij}a^{T} x_{i}R_{ij}x_{i}^{\top}a\}\\ &=\min\limits_{a}a^{T}X(H-R)X^{T}a\\ &=\min\limits_{a}a^{T}XMX^{T}a\end{array}  \tag{13}\]  
where \(M=H-R\) is the Laplace matrix, \(H\) is the diagonal matrix, and \(H_{ii}=\sum_{i}R_{ii}\). The objective function of GLPP is constrained as follows:  
\[a^{T}(\eta XHX^{T}+(1-\eta)I)a^{T}=a^{T}Na=1 \tag{14}\]  
GLPP solves the following transformation problems to find the optimal transformation vector:  
\[ \begin{array}{l}\underset{\vec{a}}{\min}a^{T}XMX^{T}a\\ s.t.a^{T}Na=1\end{array}  \tag{15}\]  
where \(N=\eta XHX^{T}+(1-\eta)I\),\(H=\eta D-(1-\eta)\hat{\bar{D}}\) and \(I\) is the identity matrix. Therefore, the optimal transformation vector \(A\) can be obtained by solving the problem of generalized eigenvectors \(a\):  
\[XMX^{T}a=\lambda Na. \tag{16}\]  
The eigenvalues obtained from the solution are sorted from smallest to largest \(\lambda_{1}<\lambda_{2}\cdots\lambda_{l}\). Their corresponding eigenvectors are the obtained projection matrix \(A\) that can maintain the global and local structures of the data, which can be constructed as:  
\[x_{i}\to y_{i}=A^{T}x_{i}=[a_{1},a_{2}\cdots a_{l}]. \tag{17}\]
###### Abstract  
In general, the optical and electrical characteristics of Cu(In,Ga)Se\({}_{2}\) (CIGS) solar cells have been studied under the condition that sunlight is normally incident from the air to the CIGS solar cell having no thick front encapsulation layers. To obtain the calculation results in a realistic module application, we calculate the optical and current-voltage (J-V) characteristics of surface-textured CIGS solar cells by simultaneously considering the thick front encapsulation layers and oblique sunlight incidence. Using the proposed angle-dependent equispaced thickness averaging method (ADETAM), we incoherently model two successive front encapsulation layers of a cover glass layer and an ethylene vinyl acetate (EVA) layer, whose respective thicknesses are greater than the coherence length of sunlight (-0.6 \(\upmu\)m). The angular dependences of reflectance spectrum and J-V curves are calculated and compared in a surface-textured CIGS solar cell with and without the inclusion of the two front encapsulation layers. We show that the optical absorption improvement of the surface-textured CIGS solar cell over the planar CIGS solar cell can be over-predicted when the thick front encapsulation layers are not considered in the optical modeling.  
photovoltaics; optoelectronic modeling; optical incoherence; spectral effect +
Footnote †: journal: Applied Sciences
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 3 Selection of Microbes for the Phytoremediation of Metal Contaminated Saline Soils
### Frequently Investigated PGP Properties
#### 3.1.4 Multiple PGP Effects in Interaction with Metal Stress  
[PERSON] et al. [45] screened PGP properties (nitrogen fixation, siderophores production, IAA production, and biofilm formation), and salt and metal resistance of bacterial strains isolated from the roots of the halophyte _Arthrocennium macrostachyum_ and belonging to _Halomonas_, _Kushneria_, _Microoccus_, _Bacillus_, _Vibrio_, _Pseudoalteromonas_, and _Staphylococcus_ genera. All of the strains showed at least one PGP property, but the presence of Cd had, in general, a negative impact, especially on the ability to fix atmospheric nitrogen. On the contrary, the production of IAA and the solubilization of phosphates were enhanced in presence of As and Cd, respectively. From this study, endophytic bacteria were the most resistant to metals. Another survey on _Spartina maritima_ plants reported interesting PGP characteristics of rhizosphere-halotolerant bacteria isolated from the Tinto River estuary in Spain that is contaminated by Cu, As, Cd, Cu, Ni, Pb, and Zn: biofilm formation, siderophores production, IAA production, ACCD activity, phosphate solubilization, and nitrogen fixation [125]. PGP features were screened with and without Cu. The presence of high levels of metals decreased the number of strains exhibiting PGP features, except for the ability of forming biofilms. They selected the four most performing ones and inoculated _Medicago sativa_ seeds to evaluate their effects on seed germination and root elongation. The results showed that all bacterial strains could significantly enhance the root biomass in absence of Cu, but only three of the four bacterial strains have the same effect in the presence of Cu. In another study [126], the same authors used a consortium of these four bacterial strains to inoculate _S. maritima_ plants in contaminated soil from the Tinto River estuary. Inoculation had a significantly positive effect on the root growth and metal uptake, which increased by 19% for As, 65% for Cu, 40% for Pb, and 29% for Zn. The impact of PGP properties that are involved in augmented plant growth could also explain a better absorption and accumulation of metals because of greater storage of ions in relation to a greater plant biomass [30]. In the region of the Tinto and Odiel Rivers in Southern Spain, one of the most polluted areas of the world, bacteria from _Spartina densiflora_'s rhizosphere were tested for their contribution to growth in early stepsof plant development [127]. The germination rate was successfully improved when seeds were associated with a bacterial consortium composed of _Aeromonas aquarium_ SDT13, _Pseudomonas composti_ SDT3, and _Bacillus_ sp. SDT14. These bacteria showed several PGP features, namely phosphate solubilization, siderophores and IAA production, and nitrogen fixation. The consortium could increase seed germination and limited pathogenic fungal infection [127].
# Compact High-Diectivity Contra-Directional Coupler
###### Abstract  
This paper presents a novel design of a compact contra-directional coupler with high directivity for high-power monitoring in high frequency. Microstrip parallel coupled lines are widely used for directional couplers; however, they show poor directivity inherently. Their directivity has been improved by many works. However, the suggested approaches often result in other limitations, such as a weak structure for high-power monitoring, or a larger size to be integrated with other circuits. The design approach proposed in this study starts from a ring-type four-port network to avoid weak components that are vulnerable to high power, and uses a \(60^{\circ}\) electrical length of coupled line for a compact size. The design equations for the initial dimensions are derived from the ring-type four-port network model. The weak coupling of the 20 dB coupler was designed and measured. The measurement shows 20 dB directivity from 12.8 GHz to 14.8 GHz, covering the Ku-band satellite uplink communication and peak directivity of about 45 dB. The coupler's active area is 4 mm by 5.5 mm; this is a compact size compared with other works.  
directional coupler; power detector; high directivity; VSWR detection +
Footnote †: journal: _electronics_  
0
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# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Current Challenges and Research Agenda
Additionally, the existing body of work overlooks human-in-the-loop feedback and domain knowledge within the Prescriptive Maintenance domain. Consequently, human-machine co-decision environments, training simulators, and XAI-PsM could bridge digital and human reasoning. Validating PsM models in operator-in-the-loop settings, such that recommendations are accompanied by human-readable information, will enable progressive workforce integration without compromising system autonomy. **Causal Prescriptive Maintenance**: The existing PsM research primarily focuses on correlation-based ML modelling. The work must move beyond associative ML models, which often contain spurious correlations, towards integrating domain knowledge, often in the form of a causal graph. This way, building a PsM system that is more capable of robust interventions and counterfactual reasoning will add more value to the production chain in terms of precision and resource efficiency. Moving from simple predictions to causally optimised decisions, overcoming the limitations of associative models paired with post hoc explanations, will certainly bridge this gap. Validating causal models through real-world scenarios where maintenance actions are triggered by identified root causes, rather than correlation-based symptoms, would certainly demonstrate superior decision reliability. This has been recently shown by [56], where predicting root cause analysis using knowledge graphs offered a more nuanced understanding of failures, rather than simply relying on the data visualisation outcomes of machine learning models. **Sustainable- and Energy-Based Prescriptive Maintenance**: Although numerous studies outside the PsM domain address the sustainable aspect of industrial maintenance, most PsM models focus solely on cost optimisation or uptime. The existing work in PsM pays little attention to sustainability metrics, energy usage, and lifecycle costs, as evidenced by the absence of green and circular economy objectives. Some of the possible solutions could include assessing the sustainability aspect, including both environmental-sustainable and social aspects that constitute the I5.0 pillars [56; 64; 120; 121]. Validating psM models using sustainability-aware KPIs, such as CO2 emissions, energy consumption, and material waste, will provide tangible evidence that supports arguments about their contribution beyond operational efficiency, aligning prescriptive actions with broader environmental and social goals.
## 4 Simulations and Experimental Test
### Performance against Disturbances and Parameters Perturbation
From Figure 5, it can be seen that the attitudes can track the reference signal well. When the sinusoidal disturbance torques are added, the controller can quickly suppress the influence of external disturbances, and the attitude almost does not change. Besides, due to the use of the disturbance observer, there is no chattering caused by the sliding mode controller. Figure 4: Position variations under the disturbances \((X,Y,Z)\). In order to verify the robustness of the proposed control system, several significant model parameters are changed as shown in Table 2. The simulation process is reproduced again. Figure 6 gives the position curves under the condition of step signal and external sinusoidal force disturbances. Obviously, the proposed position controller can also ensure the tracking and anti-interference performance of the system when parameters change. Moreover, the tracking curves of the attitude control subsystem under parameter variations are shown in Figure 7. Similarly, the control system ensures the tracking ability and anti-interference ability of the attitude control loop.The results show that the proposed control scheme has good robustness. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Variation** & **Symbol** & **Variation** \\ \hline \(m\) & 120\% & \(I_{xx}\) & 120\% \\ \(k_{f}\) & 80\% & \(I_{yy}\) & 120\% \\ \(k_{d}\) & 80\% & \(I_{zz}\) & 120\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variation of model parameters. Figure 5: Attitude variations under the disturbances \((\phi,\theta,\psi)\). Figure 6: Position variations under the disturbances when parameters change \((X,Y,Z)\).