esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Comparison and Validation  
Given the numerous factors involved in evaluating geological disasters, statistical multicollinearity is often unavoidable, potentially hindering the accurate analysis of the relationships between susceptibility models and geological disasters. Therefore, prior to model establishment, correlation and multicollinearity analyses were conducted on the 14 selected factors to ensure their appropriateness. The multicollinearity analysis was conducted using Stata 18, and the results indicated no significant multicollinearity issues among the 14 factors. Specifically, the variance inflation factors for all 14 influencing factors were well below the threshold of 10, and the tolerance values were above 0.1. In the landslide susceptibility assessment, the contribution of each factor to the model can be analyzed using the mean decrease in Gini and SHAP values. The mean decrease in Gini determines the contribution of each feature to the model by evaluating the improvement in purity brought by each feature to the model, thereby measuring the importance of the features. This indicator is commonly used in RF models [68]. The SHAP value model was used to assess the performance of the XGB and GBM models by ranking the importance of the factors (Figure 7a). EAP shows the highest contribution, followed by the TRI, the slope, the SPI, and the distance to roads. The SHAP visualizations (Figure 7b,c), shown by the red points, are concentrated on the right side, indicating a strong positive influence of the EAP on landslide occurrence. The TRI, SPI, and slope also have positive impacts, while the distance to roads, the NDVI, and the distance to fault lines mostly show negative effects. This suggests that areas with higher EAP values, more rugged terrain, higher SPI values, steeper slopes, proximity to roads, less vegetation, and proximity to fault lines are more susceptible to landslides. The results indicate that EAP has a significant impact on the prediction outcomes, which are clearly related to the widespread spatiotemporal distribution of rainfall-induced landslides in the study area, where high-frequency rainfall events are common [69; 70]. Under the monsoon climate conditions in Zhejiang Province, rainfall is concentrated in the summer, with precipitation from May to September accounting for 69% of the annual total. Particularly during the typhoon season and the plum rain season, such heavy rainfall/prolonged precipitation greatly contributes to the occurrence of landslides. This further emphasizes the importance of considering EAP in landslide susceptibility assessments. Machine learning involves both training and validation phases. For this study, the research samples were randomly divided at a 70:30 ratio, with 70% of the data used for training and 30% for validation. The testing dataset includes 640 landslide points and 599 non-landslide points. Model performance was evaluated using the accuracy, recall, F1 score, log loss, and AUC values. Model evaluation results and optimized hyperparameters are presented in Table 4. The receiver operating characteristic curve is shown in Figure 8.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 1 Introduction
### State-of-the-Art Issues
#### 1.1.1 Theoretical and Numerical Research of Locking Phenomena  
The numerical locking phenomena concern thin-walled structures in which the true solution is characterized by bending strains dominance over shear and/or membrane strains. The phenomenon does not appear in the case of the membrane strains dominance. If, due to poor discretization of the problem, the shear and/or membrane strains are not equal to zero, as it results from the thin-walled theories for the thickness \(t\) tending to \(0\), then the shear, membrane, or shear-membrane strain energy numerically dominates over the bending energy, leading to numerical over-stiffening of the structure, which in turn results in too low (zero or almost zero) values of displacements (compare the work [1]). This phenomenon is called the shear, membrane, or shear-membrane locking and is typical for the displacement finite element method.  
Theoretical and numerical studies of the locking phenomena concern one- and two-dimensional problems, including beams, arcs, plates, and shells. Different kinds of locking are investigated: volume (Poisson's) locking present in nearly incompressible materials (\(\
u\to 0.5\)), deformational locking present in bending-dominated thin-walled structures within the displacement formulation of the finite element method, and finally the trapezoidal locking present in hybrid-stress finite elements. The deformational locking, which is the subject of this work, may be shear (plates), membrane, or shear-membrane (shells). The significant exemplary theoretical and numerical research results concerning deformational locking are presented in [1; 2; 3; 4; 5; 6], respectively. These works refer to the first-order, higher-order, and hierarchical models of plates and shells.
## 4 Results  
To evaluate the trained models, a wrapper function was created to efficiently get the metric scores, relevant confusion matrices and the receiver operating characteristic (ROC) with the area under curve (AUC) and the precision-recall diagrams. The evaluation of each classification model is performed by applying the k-fold (k = 5) cross validation technique on a stratified hold-out sub-dataset that was kept initially specifically for the purpose of model evaluation. A split of the initial dataset was performed yielding a training sub-dataset (70% of the dataset) and a test sub-dataset (30% of the dataset). The performance of models with different configurations is then evaluated on the hold-out set, for the purpose of selecting the best performing model. This approach is useful to measure the prediction performance of the final production model or compare predictions with reference to held-out samples [54]. The performance results of all the models trained are presented in two separate tables. Table 9 records the results that are related to the baseline models, the application of the SMOTE, PCA and HPO methods using the two feature subsets selected. Table 10 records the results of the models that were trained using pipelines. A pipeline in the context of ML can be described as a utility method that allows the design of a procedure from the data preprocessing to the training of the classifier offering some advantages over the manual execution of these steps. The purpose of the pipeline is to assemble the above methods that can be cross-validated together while setting different parameters in the context of using the _Scikit-learn_ library [55]. The pipeline method eventually implements the solution for avoiding data leakage. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Manually Selected Feature Set**} & \multicolumn{3}{c}{**Automatically Selected Feature Set**} \\ \cline{2-7}
**Algorithm** & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**SD**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**SD**} \\ \cline{2-7}
**Training** & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Baseline Models**} & \\ \hline
**Logistic Regression** & **100** & **100** & **0** & 93.33 & 93.33 & 13.33 \\ \hline Decision Tree & 100 & 100 & 0 & 96.67 & 96.67 & 6.67 \\ \hline Random Forest & 100 & 100 & 0 & 96.67 & 96.67 & 6.67 \\ \hline Support Vector Classifier & 93.33 & 93.33 & 4.71 & 93.33 & 93.33 & 8.16 \\ \hline Gaussian Naive Bayes & 100 & 100 & 0 & 100 & 100 & 0 \\ \hline Multi-layer Perceptron & 90 & 90 & 8.16 & 90 & 93.33 & 8.16 \\ \hline k-Nearest neighbors & 76.67 & 76.67 & 9.43 & 96.67 & 93.33 & 8.16 \\ \hline Custom Ensemble & 100 & 100 & 0 & 96.67 & 96.67 & 6.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation results, by ML algorithm, for the training and testing processes, for both feature selection strategies, from the stage of baseline models up to applying hyperparameter optimization.
###### Abstract  
The distribution of growth-inhibiting bacteria (GIB) against the toxic dinoflagellate _Alexandrium catenella_ (Group I) was investigated targeting seagrass leaves and surface waters at the seagrass bed of Akkeshi-ko Estuary and surface waters of nearshore and offshore points of Akkeshi Bay, Japan. Weekly samplings were conducted from April to June in 2011. GIBs were detected from surface of leaves of the seagrass _Zosten marina_ in Akkeshi-ko Estuary (7.5 \(\times\) 10\({}^{5}\)-4.7 \(\times\) 10\({}^{6}\) colony-forming units: CFU g\({}^{-1}\) wet leaf) and seawater at the stations in Akkeshi Bay (6.7 \(\times\) 10\({}^{9}\)-1.1 \(\times\) 10\({}^{3}\) CFU mL\({}^{-1}\)). Sequence analyses revealed that the same bacterial strains with the same 165 rRNA sequences were isolated from the surface biofilm of _Z. marina_ and the seawater in the Akkeshi Bay. We therefore strongly suggested that seagrass beds are the source of algicidal and growth-inhibiting bacteria in coastal ecosystems. Cells of _A. catenella_ were not detected from seawaters in Akkeshi-ko Estuary and the coastal point of Akkeshi Bay, but frequently detected at the offshore point of Akkeshi Bay. It is suggested that _A. catenella_ populations were suppressed by abundant GIBs derived from the seagrass bed, leading to the less toxin contamination of bivalves in Akkeshi-ko Estuary.  
Alexandrium catenella; growth-inhibiting bacteria; _Zostera marina_; Akkeshi-ko Estuary; distribution; phylogenetic analysis +
Footnote †: journal: _sciences_
# NO Separation Characteristics in Integrated Electromigration Membrane Reactor
###### Abstract  
An integrated electromigration membrane absorption method has been proposed for the separation of NO from simulated mixed gas. The experiments were conducted to investigate the effect of discharge voltage, gas flow rate, inlet concentrations, and absorbents on the NO separation efficiency and total mass transfer coefficient in the integrated electromigration membrane reactor. The experimental results demonstrated that the NO separation efficiency and total mass transfer coefficient increased with the increase in the applied discharge voltage of the integrated electromigration membrane reactor. Regardless of discharge or not, the separation efficiency of NO continuously decreased with the increase in the gas flow rate and inlet concentration of NO in the experimental process. The total mass transfer coefficient of NO increased first and then decreased with an increase in the gas flow rate, while it decreased with an increase in NO inlet concentration. Compared with the membrane absorption without discharge voltage under the condition tested, at a discharge voltage of 18 kV, the NO separation efficiency and the total mass transfer coefficient increased by 48.7% and 9.7 times, respectively.  
electromigration; membrane separation; separation efficiency; total mass transfer coefficient; denitrification
# Fungal-Lactobacteria Consortia and Enzymatic Catalysis for Polylactic Acid Production
## 3 Results and Discussion
### LA Production from Industrial Residues Catalyzed by a Fungus-Bacteria Consortium  
As we have seen above, the consortium _T. amestolkiae-L. plantarum_ M9 MG6-B2 with dodecanol was the best option for an effective production of LA from pure commercial starch. Moreover, the association of both strains was confirmed by SEM (Figure 5). Thus,  
Figure 4: Quantification of biofilm density in MRS medium with starch, induced or not with dodecanol, incubated in a 24-well plate at 28 \({}^{\circ}\)C and 200 rpm. _T. amestolkiae_ (T), _L. plantarum_ M9 MG6-B2 (M), 100 mM dodecanol (D), without dodecanol (-).  
Figure 5: Scanning electron micrographs of _T. amestolkiae–L. plantarum_ M9 MG6-B2 consortia in the absence (**A**) or presence (**B**) of dodecanol.  
this combination was further tested with two real agro-industrial residues (starch from a potatoes chip factory and bagasse from a beer factory) in separate cultures without any other carbon source. The production of LA from these two sources is presented in Figure 6.  
In the case of the potato residue, there is a direct relationship between starch consumption and LA production, which reaches a maximum at around 24 h of incubation (Figure 6A). Then, starch levels do not continue going down and LA is consumed. Surprisingly, LA production from bagasse at the same incubation time was higher and seems to continue to increase, albeit more slowly. Bagasse is supposed to be more difficult to decompose than starch, but _T. amestolkiae_ has been described as an excellent producer of cellulases and hemicellulases, which could explain the good conversion of bagasse into fermentable sugars [48]. In addition, the complex composition of this substrate could provide components that favor the growth of the consortium and explain the higher yields.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Channel Noise Models under Test  
#### 4.3.1 Additive White Gaussian Noise (AWGN) Generation  
AWGN is a representative synthetic example of the PLC background noise as defined in references like [2; 3]. The AWGN generation has been performed in fixed point arithmetic, using the central theorem limit. For this purpose, the Gaussian variable is generated by adding 12 values of uniform distribution.  
The uniform noise generator and the AWGN generator passed the Kolmogorov-Smirnoff test. The Kolmogorov-Smirnoff test with 1 million random values generated has a certainty higher than 99.9% to match the required distributions.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 4 CLOR Protocol
### Main Idea  
#### 4.1.1 Forwarder Selection Algorithm  
In CLOR, we exploit a hybrid candidate set selection strategy by utilizing metrics such as topological information, link quality, and congestion state.  
First, we use hop count information to bypass connectivity holes and shadow zones [5]. The topological information can be obtained through broadcasting of network initialization and be updated in each transmission. Then, we consider the link quality of the forward link to the nodes with low hop count.  
Link quality estimation reflects the variation of the time-varying channel. The typical mechanism of a link quality estimator (LQE) can be classified into the hardware-based LQE and software-based LQE [43]. Each node cannot accurately estimate the link quality only by using the Signal-to-Noise Ratio (SNR) due to diverse channel variation. Therefore, we introduce the packet received ratio (PRR) that reflects the link quality at the high layer. SNR can be directly read at the physical layer. PRR can be obtained by using periodic beacons or directly via data traffic rates at the high layer [44].  
Second, we exploit the topological information and link quality of multi-hop nodes to avoid routing falls into local optimal [8; 45]. On this basis, we calculate the advancement information to the sink.  
Third, we introduce the packet received ratio (PRR) that reflects well the link quality at the high layer. Each node measures the local congestion degree (CD) with the arrival rate and the forward rate of the data packets [46]. We consider two types of arrival packets in the network layer; i.e., the data packets from the high layer and the data packets from the other nodes.  
These strategies are devoted to resolving different problems that are slightly correlation with each other. Thus, to develop a candidate selection algorithm based on these strategies is a complex problem. Fuzzy logic is a typical method to optimize this problem [21; 44]. In this paper, we introduce the fuzzy logic method to functioning these metrics to make forwarding decisions.
## 5 Discussion
### Generalizability  
The generalizability of MultiScaleAnalyzer presents both strengths and limitations. On one hand, MultiScaleAnalyzer can be applied across diverse datasets and educational contexts, such as traditional classroom activities, online collaborative platforms, and virtual simulations. For example, in VR-based learning environments, where students interact with 3D objects or immersive simulations, MultiScaleAnalyzer could jointly analyze gaze data and motion tracking to help educators understand how students navigate virtual spaces or respond to stimuli. Similarly, in traditional classroom settings, where students engage in face-to-face activities or interact with physical learning materials, MultiScaleAnalyzer could analyze gaze patterns and interactions to help educators understand how students focus on specific teaching materials, collaborate with peers, or respond to instructor-led activities. This flexibility is enabled by key design features, including customizable areas of interest (AOI), adjustable information scales, and compatibility with various data types (e.g., eye-tracking data, interaction data, log data).  
However, the aggregated data view \"Elements Vis\", which reconstructs problem-solving scenarios, brings challenges for the generalizability of MultiScaleAnalyzer. For different learning scenarios or datasets, while layout reformatting enhances visualization, it may not be easily transferable, especially for layouts that are highly specific or complex. In summary, although MultiScaleAnalyzer demonstrates strong generalizability through its adaptable framework and multiscale approach, its scenario reconstructions constrain its applicability in certain contexts. These trade-offs highlight the need to design context-flexible visualizations in future iterations.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 2 Control Design
### ESO-Based PIDSMC
: _The estimated dynamics error is assumed to be:_  
\[\widetilde{x}_{2}=0, \tag{32}\]  
_based on Assumption 3, Equation (31) can be rewrite as:_  
\[\dot{\hat{s}}=\hat{x}_{3}+\left(\frac{l_{2}}{\epsilon_{o}}+\frac{c_{1}\cdot\!l_ {1}}{\epsilon_{o}}\right)\widetilde{x}_{1}+u-\tilde{x}_{d}+c_{1}\big{(}\hat{x }_{2}-\dot{x}_{d}\big{)}+c_{2}\cdot\!\hat{e}, \tag{33}\]  
_to calculate the control input (u), Equation (33) is substituted into Equation (27):_  
\[\hat{x}_{3}+\left(\frac{l_{2}}{\epsilon_{o}}+\frac{c_{1}\cdot\!l_ {1}}{\epsilon_{o}}\right)\widetilde{x}_{1}+u-\tilde{x}_{d}+c_{1}\big{(}\hat{x }_{2}-\dot{x}_{d}\big{)}+c_{2}\cdot\!\hat{e}=-K^{\prime}\cdot\!\mathrm{sat}( \tilde{s}), \tag{34}\]  
_the control input (u) is calculated as:_  
\[u=-K^{\prime}\cdot\!\mathrm{sat}(\hat{s})-\left(\frac{l_{2}}{\epsilon_{o}}+ \frac{c_{1}\cdot\!l_{1}}{\epsilon_{o}}\right)\widetilde{x}_{1}+\tilde{x}_{d}- c_{1}\big{(}\hat{x}_{2}-\dot{x}_{d}\big{)}-c_{2}\cdot\!\hat{e}-\hat{x}_{3}, \tag{35}\]  
_the control input mentioned above is the sum of the input from PIDSMC (\(\overline{u}\)) and the estimated perturbation from ESO (\(\hat{x}_{3}\)):_  
\[u=\overline{u}-\hat{x}_{3}, \tag{36}\]  
_where_  
\[\overline{u}=-K^{\prime}sat(\hat{s})-\left(\frac{l_{2}}{\epsilon_{o}}+\frac{ c_{1}\cdot\!l_{1}}{\epsilon_{o}}\right)\widetilde{x}_{1}+\tilde{x}_{d}-c_{1} \big{(}\hat{x}_{2}-\dot{x}_{d}\big{)}-c_{2}\cdot\!\hat{e}. \tag{37}\]  
The actual control input is \(u\), which is the sum of the PIDSMC input \(\overline{u}\) and the estimated perturbation (\(\hat{x}_{3}\)). Integrating the estimated perturbation with the control input is helpful in achieving the desired control response. The estimated perturbation consists of system dynamics, uncertainty in dynamics, nonlinearity, and external disturbance. The accuracy of the perturbation estimation depends on the observer gains. The higher the magnitude of gains, the better the estimation accuracy. In the next subsection, a detailed mathematical analysis of the designed control logic is performed. The analysis includes system stability (Section 2.2.1), the effect of perturbation on the sliding variable (Section 2.2.2), and the relationship between the error and perturbation (Section 2.2.3).
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods
### Algorithms in Comparison
#### 2.4.1 Color Histogram  
Given a color space of an image, the color histogram is calculated by counting the times of each color occurs in the array of image data [24]. In this work, the color histograms of all channels (red, green, and blue) were calculated respectively and concatenated as the feature vector.
# Balancing Distributed Key-Value Stores with Efficient In-Network Redirecting
## 4 KVSwitch Design
### Functions in ASIC pipeline
#### 4.2.2 Round-Robin Load Balancer  
KVSwitch uses one match-action table and a register array to implement the load balance process, as shown in Figure 5a.  
All packets identified as KVS queries first look up the HotkeyTable to check whether the query is requesting a hot item. HotkeyTable matches the \(KEY\) field in the packet header and returns two parameters: the number of servers containing this item and an index of a cell in the _Round-Robin_ register array. Both parameters are stored in self-defined metadata, which is in a one-to-one relationship with the packets. The _Round-Robin_ register array contains \(K\) cells, each of which is used for a hot key in HotkeyTable.  
If this packet is a _Get_ query, KVSwitch will assign a new destination server to balance the rack. KVSwitch uses the index obtained from HotkeyTable to obtain the corresponding value in the _Round-Robin_ register array and also stores it in the metadata, which denotes the new destination server among all shadow servers of this key. The value in this cell is then incremented by 1 so that the next query for this key is redirected to the next server in a _Round-Robin_ manner. When this value exceeds the total number of servers containing this item, it is reset to 1.  
Figure 5: ASIC designs of two important modules. (**a**) Load balance module; (**b**) Consistency module.  
Otherwise, if this is a _Set_ query, no further operation with the _Round-Robin_ registers is needed, so it goes directly to the following tables (Section 4.2.3).  
Some examples are shown in Figure 5a. A _Get_ query for hot key A hits the HotkeyTable, and the two parameters are 20 and 70. This means that, in the rack, there are 20 servers containing the required item and that we should look up the 70 th cell of the _Round-Robin_ register array to find the new destination server. Since the value in the 70 th cell is 20, this query will be redirected to the 20 th server containing this item. Because 20 equates to the total number of servers that contain item A, the new value in this cell will be set to 1 to redirect the next _Get_ query for A to the first server. A _Set_ query is also tested with HotkeyTable to check whether it will modify a hot item (see _Set_ query for F in Figure 5a). The action parameters are written into the metadata in the same way as for _Get_ queries, but no further operation is executed in this process.