esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Comparison Results  
This section presents a comparative analysis of our DEPANet against 16 other methods on the SD-saliency-900 dataset, i.e., 2 LSG [32], BASNet [33], BC [34], BMPM [35], CPD [36], DACNet [37], DSS [38], EDRNet [39], F3 Net [40], ITSD [41], MIL [42], MINet [43], NLDF [44], PFANet [45], PiCANet [46], PoolNet [47], R3 Net [48], RCRR [49], SAMNet [50], and SMD [51]. For a fair evaluation, we run the original code of the authors or reference their published experimental data. Table 1 compares the methods in the SD-saliency-900 dataset, and Figure 5 visualizes their F-measure and Precision-Recall curves.  
From Table 1, our DEPANet demonstrates significant advantages across multiple metrics, outperforming other comparative methods in overall performance. Firstly, our DEPANet ranks first in terms of structural similarity (SM, 0.9373) and weighted F-measure (\(w-F_{\beta}\), 0.9347), indicating its exceptional ability to capture image structures and details. Secondly, our method also achieves the best results in metrics related to edge detection (\(E_{e}\), 0.9793; \(E_{\beta}\), 0.9765; \(E_{max}\), 0.9793), further proving its superior capability in edge feature extraction and enhancement. Furthermore, our method reaches the highest level in F-measure (\(F_{e}\), 0.9223; \(F_{\beta}\), 0.9193) and ties with EDRNet in \(F_{max}\) (0.9370), demonstrating its strong competitiveness in comprehensive evaluation metrics. In terms of deployment feasibility, DEPANet has 47.6 million parameters, significantly fewer than models like DACNet (98.2M), which makes it more memory-efficient and suitable for embedded GPU or edge-device deployment.  
In addition to segmentation accuracy, inference time is a critical factor for practical deployment in steel production lines, where real-time or near real-time processing is often required. As shown in Table 1, DEPANet achieves a 22.3 FPS on an NVIDIA A100 GPU with a batch size of 16 for 256 \(\times\) 256 images.  
Figure 5 shows that DEPANet outperforms all other models, demonstrating a high F-measure at different thresholds and superior precision at various recall rates. The DEPANet precision recall curve is significantly closer to the ideal (1,1) point compared to other models, while its F-measure curve consistently dominates those of alternative approaches. Furthermore, when evaluated against state-of-the-art models, including PiCANet, DACNet, R3 Net, and MINet, DEPANet maintains a significant performance advantage. These comprehensive experimental results validate both the effectiveness and reliability of the proposed DEPANet architecture.
###### Abstract  
In recent years, layered double hydroxide (LDH) has attracted extensive attention of researchers in the field of corrosion protection due to its unique structure and anion exchange characteristics. However, its chloride adsorption capacity remains to be further optimized to increase its corrosion protection ability. In this work, the influence of reactant concentration on the prepared MgAl-LDH intercalated with nitrates was investigated, and the morphology, composition, and structure were characterized by scanning electronic microscopy/energy dispersive spectrometer (SEM/EDS), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD), respectively. The results indicate that the reactant concentration did not have an obvious influence on the particle size and morphology of LDH, while the composition and crystalline structure were changed to some extent. The corresponding reasons were interpreted deeply. The chloride absorption behavior was investigated, and the powder after immersion in NaCl solutions with different concentrations was characterized by XRD to explain the difference in chloride adsorption amounts. The MgAl-LDH powder synthesized at higher concentrations presented excellent chloride adsorption ability with a Qm value of 155.88 mg/g, which was much better than that reported in the literature. This work demonstrated the superior chloride adsorption of the synthesized MgAl-LDH, and it is of great significance for providing a solid foundation and guidance for the industrial application of LDH in the field of corrosion protection in the future.  
layered double hydroxide; anion exchange; chloride adsorption; mechanism +
Footnote †: journal: _Sciences_  
_Applied_  
_sciences_
## 3 Results and Implementation of the Optical System to Improve Pointing Accuracy
The adopted scanning quantities ensured a first targeting time of 0.25 s alongside the probability of hitting the target once every 0.0013 s.  
Figure 20 presents the optical system setup with four actuators linked to the optical mirror:  
Considering a mirror radius (\(r_{m}\)) equal to 0.015 m, thickness (\(s\)) equal to 0.01 m and mass (\(m\)) of about 0.016 kg, the actuators' characteristics can be determined as follows. Travel range:  
\[c=a\cdot r_{m}\;[\text{m}] \tag{15}\] \begin{table}
\begin{tabular}{c c c} \hline
**Scenario** & **Conical Scanning** & **Bernoulli’s Lemniscate Scanning** \\ \hline
01 & N\({}_{\text{max}}\) = 628 (r\({}_{\text{s}}\) = 0.009, \(\omega\) = 5000) & N\({}_{\text{max}}\) = 31245 (a = 0.07, \(\omega\) = 5000) \\
02 & N\({}_{\text{max}}\) = 642 (r\({}_{\text{s}}\) = 0.009, \(\omega\) = 5000) & N\({}_{\text{max}}\) = 28952 (a = 0.07, \(\omega\) = 5000) \\
03 & N\({}_{\text{max}}\) = 583 (r\({}_{\text{s}}\) = 0.009, \(\omega\) = 5000) & N\({}_{\text{max}}\) = 29245 (a = 0.07, \(\omega\) = 5000) \\
04 & N\({}_{\text{max}}\) = 642 (r\({}_{\text{s}}\) = 0.009, \(\omega\) = 5000) & N\({}_{\text{max}}\) = 30473 (a = 0.07, \(\omega\) = 5000) \\ \hline \end{tabular}
\end{table} 
Table 6: Results for every ship motion scenario. Figure 20: Optical system section. Pull force:  
\[F=\frac{I\cdot a\cdot\omega^{2}}{r_{m}}\;[N] \tag{16}\]  
The Equations (15) and (16) led to the selection of the actuator with a travel range of 108 \(\upmu\)rad and a pull force of 125.7 N.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 5 Trajectory Feasibility Assessment  
In order to provide an efficient trajectory generation method, thrust and body velocity limitations were temporarily ignored in Section 3 of this article. For practical applications, it is necessary to limit the control input trajectory based on the performance parameters of the drone. As mentioned in Section 4, compared to the ideal model, the presence of two types of resistance has a complex and comprehensive impact on the optimal input trajectory. This section will provide a low-cost and fast judgment method for the feasibility  
Figure 4: Optimal control input trajectory corresponding to different rotor resistance parameters.  
Figure 5: Optimal control input trajectories corresponding to different body resistance parameters.  
of this trajectory based on the control input constraints brought by the real quadcopter physical system.  
Given a certain time interval \(\mathcal{T}=[t_{1},t_{2}]\subseteq[0,T]\) and the optimal state trajectory \(s^{*}(t)\) in the form of (31) and its corresponding jerk input \(j^{*}(t)\), the goal is to determine whether the system inputs \(f_{t}\) and \(\mathbf{\omega}\) appearing in the quadcopter dynamics models (2) and (5) in Section 2.1 meet the feasibility requirements of (7) and (8) in Section 2.2.
## 3 Optimization of Controllable Rectification Based on Advanced Control Strategies
### MPSMC Control Strategy
#### 3.3.2 Design of Sliding Mode Controller
This requires considering system characteristics and control objectives. In this paper, a suitable sliding surface is constructed. The Lyapunov method is used for control effect analysis to determine parameter ranges. Also, the impact of disturbances and uncertainties is considered. Afterward, parameters are optimized. A necessary precondition for constructing sliding mode control is the existence of the sliding mode. We must rigorously verify the existence conditions of its sliding mode in order to guarantee that the trajectory points near the sliding surface can successfully reach the sliding surface in a finite amount of time. Select the Lyapunov function as:  
\[V=\frac{1}{2}s^{2} \tag{25}\]  
By performing deformation treatment on the above equation, it can be concluded that:  
\[\dot{V}=s\dot{s}=-k_{1}s^{2}-k_{2}s|s\,|\,^{\alpha}sat(s) \tag{26}\]  
If the arrival point is in a positive position within the field of the created switch surface, \(s>0\). There are:  
\[sat(s)=\left\{ \begin{array}{ll}1&s>\Delta\\ \frac{s}{\Delta}&|s|\leq\Delta\end{array} \right. \tag{27}\]  
\[\dot{V}=-k_{1}s^{2}-k_{2}s|s\,|\,^{\alpha}sat(s)<-k_{2}s|s\,|\,^{\alpha}sat(s )<0 \tag{28}\]  
If the arrival point is in a negative position within the field of the created switch surface, i.e., \(s<0\). There are:  
\[sat(s)=\left\{ \begin{array}{ll}\frac{s}{\Delta}&|s|\geq\Delta\\ -1&s<-\Delta\end{array} \right. \tag{29}\]  
\[\dot{V}=-k_{1}s^{2}-k_{2}s|s\,|\,^{\alpha}sat(s)<k_{2}s|s\,|\,^{\alpha}<0 \tag{30}\]  
From the above analysis, it can be concluded that the existing sliding mode condition is satisfied. The final control block diagram of the Model Predictive Sliding Mode Control (MPSMC) is shown in Figure 13, where the voltage outer loop employs a sliding mode controller, while the current inner loop utilizes a model predictive controller.
## 4 Results
### Network Results  
Of interest for a user of the PPP-RTK products provided by a regional network in real-time applications would be the time it takes for precise satellite clocks and biases to be available, since the rise of the satellite. In Figure 4a, two metrics are displayed: the time to first fix (TTFF) for an ambiguity-fixed solution for a satellite and the time until the standard deviation of the satellite clock falls below a certain threshold, here 15 cm. Computing a first fixed solution for a satellite can be done in under 10 min in almost all cases, whereas a satellite clock better than 15 cm is expected to be available in around 20 min. Providing satellite clocks and biases to the user that have not been estimated yet with high accuracy would not be a problem if the full VCM information of the network parameters were transmitted to the user as well. However, since satellite clocks and biases are usually treated as deterministic corrections on the user side, the VCM is discarded. Discarding the VCM of the network products leads to worse results for the user [33]. This effect, however, is less pronounced the smaller the uncertainty of the network parameters is. In this work, the threshold for a satellite to be considered for a user is 15 cm for the standard deviation of the satellite clock estimate. This has shown to be a good compromise regarding the time frame in which this satellite becomes available to the user and the effect of neglecting the VCM of the network products on the user solution.  
Figure 4: For brevity, only the results of constant phase bias estimation are shown. Other results do not differ significantly. (**a**) TTFF for network receivers and time until the satellite clock standard deviation falls below 15 cm. Note that satellites are only considered for the user when the clock estimate standard deviation is below that threshold. (**b**) Between L1 and L5 satellite phase biases for GPS satellites BLOCK IIF and IIIA differed from their starting epoch. Greater variation is apparent for GPS BLOCK IIF satellites, plausibly explaining the need to model the L5 satellite phase bias as a random walk parameter only for GPS BLOCK IIF. Each line represents a satellite arc. Only satellite arcs that have been observed for more than 50 epochs are shown.  
As noted in the introduction, GPS L5 phase biases are estimated as random walk parameters with the random walk process noise of 1 \(\frac{\text{cycle}}{\text{h}}\), since significant variations of the L5 phase bias relative to the L1 and L2 phase biases have been demonstrated for GPS IIF satellites [11]. In Figure 4b, the differences between L1 and L5 phase biases are visualized for GPS BLOCK IIIA and GPS BLOCK IIF satellites. It can be seen that the differenced phase biases vary a lot more for BLOCK IIF satellites, whereas the differenced phase biases for BLOCK IIIA satellites appear to be almost constant. Note that time-varying GPS L5 phase biases have been assumed for BLOCK IIIA and BLOCK IIF to enable a fair comparison.
## 3 Results
### Validation of Results
#### 3.3.4 Mix Index Analysis  
We used both numerical results and pictures to compare the results that we obtained. In the graphs below, we use values from the image analysis to demonstrate the mixing process. We also compared these values with data from our computer simulation to ensure their matching. Figure 15 displays one random test datum from each device. Three samples from each of the five devices were used. The graph illustrates the repeatability of the experiment.  
In this way, we can confirm that our simulation accurately represents the mixing process.  
Figure 14: Comparative graph of ANSYS simulation and photo of the mixing process.  
In summary, our study showcases the potential of SLA technology for the fabrication of microchannels and highlights the efficacy of capillary-driven mixing, validated through simulations. The diverse applications of our findings in various industries underscore the practicality of manufacturing mixing systems using SLA additive manufacturing. Moreover, the ability to achieve rapid mixing in short channels represents a compelling advancement in microfluidic technology, promising enhanced performance and efficiency for a wide range of applications.
# Framework for the Ensemble of Feature Selection Methods
###### Abstract  
Feature selection (FS) has attracted the attention of many researchers in the last few years due to the increasing sizes of datasets, which contain hundreds or thousands of columns (features). Typically, not all columns represent relevant values. Consequently, the noise or irrelevant columns could confuse the algorithms, leading to a weak performance of machine learning models. Different FS algorithms have been proposed to analyze highly dimensional datasets and determine their subsets of relevant features to overcome this problem. However, very often, FS algorithms are biased by the data. Thus, methods for ensemble feature selection (EFS) algorithms have become an alternative to integrate the advantages of single FS algorithms and compensate for their disadvantages. The objective of this research is to propose a conceptual and implementation framework to understand the main concepts and relationships in the process of aggregating FS algorithms and to demonstrate how to address FS on datasets with high dimensionality. The proposed conceptual framework is validated by deriving an implementation framework, which incorporates a set of Phyton packages with functionalities to support the assembly of feature selection algorithms. The performance of the implementation framework was demonstrated in several experiments discovering relevant features in the Sonar, SPECTF, and WDBC datasets. The experiments contrasted the accuracy of two machine learning classifiers (decision tree and logistic regression), trained with subsets of features generated either by single FS algorithms or the set of features selected by the ensemble feature selection framework. We observed that for the three datasets used (Sonar, SPECTF, and WD), the highest precision percentages (86.95%, 74.73%, and 93.85%, respectively) were obtained when the classifiers were trained with the subset of features generated by our framework. Additionally, the stability of the feature sets generated using our ensemble method was evaluated. The results showed that the method achieved perfect stability for the three datasets used in the evaluation.  
feature selection; variable elimination; relevant features; consensus; ensemble learning; ensemble feature selection +
Footnote †: journal: Applied Sciences  
1 Faculty of Electronic Engineering and Telecommunications, Campus Tulcan, University of Cauca, Popayan 19001, Colombia; [EMAIL_ADDRESS]  
2 Group Data Science, Division Supply Chain Services SCS, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 90411 Nuremberg, Germany; [EMAIL_ADDRESS]
# AIoT Monitoring Technology for Optimal Fill Dam Installation and Operation
## 3 AIoT Dam Monitoring Technology  
### Overview  
To monitor social infrastructure, it is necessary to go beyond simple measurement and information communication. Instead, it is necessary to autonomously adjust the sensor measurement cycle by recognizing the communication between the sensor and device when hazardous factors such as heavy rain, typhoons, and earthquakes are detected.  
In this study, we improved the existing automated measurement system to realize an AIoT-based monitoring system by enhancing the system's ability to detect sensor measurement malfunctions, which is a problem inherent to conventional automation technology. In addition, the monitoring system has been enhanced to maintain communication efficiency and enable system maintenance based on low power consumption, even in outdoor environments. Furthermore, the system has been improved to surpass a simple monitoring technology, allowing for immediate evaluation of the safety condition of facilities by utilizing sensor measurements and simultaneously predicting the time to reach the limit state, thus maximizing the AIoT functionality of the monitoring system.  
The improved AIoT system exhibited the following functions: autonomous operation, malfunction prevention, optimal communication environment construction, machine learning prediction, and data authentication and security. Based on these characteristics, we attempted to secure faster and safer dam safety management technology by analyzing the fill dam measurement data in real time. Table 1 summarizes the problems in the current technology and the advantages of the AIoT technology developed in this study.
## 3 Results and Discussion
### Diversity of Microbial Communities during SSF of PFPT
The microbial communities were analyzed using a pyrosequencing-based analysis of the bacterial _16S rRNA_ gene and fungal _18S rRNA_ gene in the 12 samples of fermented tea leaves. After removing low-quality and chimeric sequences, a total of 54,561 bacterial and 63,333 fungal high quality sequences with average read lengths of 472 and 497 bp, respectively, were obtained. Each bacterial library contained 3119-6691 reads, with different phylogenetic OTUs ranging from 948 to 2307, which were classified into 5-14 phyla, 38-90 families and 63-158 genera (Table 2). In them, 15,490 sequences were classified as cyanobacteria, which are probably chloroplast sequences that originated from the tea plant. These sequences were removed prior to further analysis. Each fungal library contained 1395-8050 reads, with different phylogenetic OTUs ranging from 30 to 162, and was classified into 2-5 phyla, 2-41 families, and 3-58 genera (Table 2). Rarefaction curves at a 3% cutoff are shown in Figure 2, and the Chao1 estimator, abundance-based coverage estimator (ACE), and Shannon index are listed in Table 2. The bacterial diversity indices were significantly greater than that of fungi (\(p<0.05\)), the bacterial rarefaction curve did not approach a plateau at a similar level, and more bacterial taxonomic groups were identified, demonstrating that bacterial communities in fermented tea leaves have a high diversity level. The fungal diversity indices in raw material (day 0) was significantly greater than that of fermented tea leaves (day 7-35) (\(p<0.05\)), indicating that a decrease of fungal diversity during SSF. This decrease of fungal diversity was also reported by [PERSON] et al., recently [PERSON] et al. (2017). It seems that as soon as water is added to start the fermentation, the fungal diversity decreased. Figure 2: Rarefaction curve analyses of the _16S rRNA_ gene (**a**) and _18S rRNA_ gene (**b**) of the bacterial and fungal communities, respectively, present in the SSF of PFPT. Operational taxonomic units (OTUs) are defined as sequences sharing 97% nucleotide sequence similarity.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 1 Introduction and Motivations
However, the nature of the distributed routing implemented in WSNs makes it difficult to update routing tables quickly and effectively. This problem can be solved by utilizing Software-defined Networking (SDN). Integrating SDN into WSNs in smart cities contributes to the programmability and central management requirements that must be satisfied to provide efficient routing in case of failures. This paper introduces an SDN-based multipath routing scheme, which we refer to as MPResiSDN, that aims to improve the resilience of WSNs in smart cities. The proposed scheme implements a multipath-aided strategy to achieve better routing in case of natural challenges. Implementing this scheme would help rescue efforts, save people's lives, and limit property damage in case of natural disasters, because it enhances the availability of current and correct sensory information that is used by survivability applications in smart cities to make decisions. The primary contributions of this paper are as follows. We introduce the MPResiSDN scheme and its components, implement the scheme using an RYU controller, emulate a sensor network, and apply different challenges to the emulated network under operational conditions. In addition, we evaluate network performance under each challenge in terms of network throughput, actual data delivery, end-to-end delay, and overhead. We compare and analyze the results obtained with and without the proposed scheme. The remainder of this paper is organized as follows. Background information is provided in Section 2. Related work is discussed in Section 3. The proposed system model is described in Section 4. The evaluation method is explained in Section 5, and evaluations of the results are discussed in Section 6. Conclusions and suggestions for future work are presented in Section 7.