esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Modal Sonar Mapping of Offshore Cable Lines with an Autonomous Surface Vehicle
###### Abstract  
Monitoring offshore infrastructure is a challenging task owing to the harsh ocean environment. To reduce human involvement in this task, this study proposes an autonomous surface vehicle (ASV)-based structural monitoring system for inspecting power cable lines under the ocean surface. The proposed ASV was equipped with multimodal sonar sensors, including a multibeam echosounder (MBES) and side-scan sonar (SSS) for mapping the seafloor, combined with a precisely estimated vehicle pose from navigation sensors. In particular, a globally consistent map was developed using the orthometric height as a vertical datum estimated based on the geoid height received from the GPS. Accordingly, the MBES and SSS generate a map of the target objects in the form of point clouds and sonar images, respectively. Dedicated outlier removal methods for MBES sensing were proposed to preserve the sparse inlier point cloud, and we applied the projection of the SSS image pixels to reflect the geometry of the seafloor. A field test was conducted in an ocean environment using real offshore cable lines to verify the efficiency of the proposed monitoring system.  
autonomous surface vehicle; sonar mapping; multibeam echosounder; side scan sonar; offshore cable lines +
Footnote †: journal: Marine Science and Engineering  
(MBES) provides an array of sonar range and bearing information regarding the seafloor, and can be converted into a point cloud. In contrast, a side-scan sonar (SSS) obtains a series of sonar intensities from fan-shaped beams from the sonar head; usually, two sonar heads are installed on opposite sides. These intensities can generate sonar images of the seafloor. Significant research on using MBES and SSS with ships or autonomous underwater vehicles (AUVs) for ocean mapping has been conducted [16; 17; 18; 19; 20]. However, ASVs have rarely been used in offshore environments, and a few applications have been developed in inland and shallow waters.  
In this study, we developed an ASV for inspecting the offshore power cables that connect wave power plants and offshore electrical substations built in the middle of the ocean. The ASV was equipped with multimodal sonar sensors to acquire the position and shape of the power lines lying on the seafloor. The vehicle performs the inspection task autonomously, and the users only remotely monitor the real-time mapping procedure.  
The remainder of this paper is organized as follows. In Section 2, the system design and overall control architecture of the developed ASV are described. In Section 3, the precise motion estimation and consistent mapping are proposed, followed by the experimental results of the field test in Section 4. Finally, Section 5 concludes this study.
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 3 Materials and Methods
### Disease Dataset and Configuration of the System  
In this study, we approach the problem of maize leaf disease quantification by building and testing a wide range of deep learning models on the publicly available PlantVillage maize leaf dataset. The original PlantVillage dataset for maize leaf diseases contains 4188 images spread across 4 different maize leaf diseases. In total, 1162 of the images are healthy, 574 are Gray Leaf Spot, 1306 are Common Rust, and 1146 are Blight. Images in the dataset are raw RGB color images of different sizes. Since deep learning algorithms require large amounts of training data, we build an augmented dataset from the existing PlantVillage dataset by applying six different augmentation methods, and we obtain a total of 46,896 images. We apply the following transforms:* Resizing--(224 \(\times\) 224);
* Shear--level 0.2;
* Rotation--90 degrees;
* Flipping--up_down flip;
* Saturation;
* Mean filtering;
* Flipping meaned image;
* Cropping--central_fraction = 0.8;
* Cropping--central_fraction = 0.5.  
The resulting augmented PlantVillage dataset contains 9379 training images and 2345 test images for each class. Figure 3 shows the histogram of the train-test distribution of the final images, and Figure 4 shows the four different maize leaf disease classes randomly selected from the training set.  
We then use this augmented dataset to train several well-known deep learning models, along with the proposed shallow CNN that contains only three covnet layers. The purpose of the CNN model is to classify the input image into the four maize leaf disease classes. The CNN identifies the specific class of the image by using class activation maps. The next process is leaf disease severity estimation using class activation heatmaps. The method accepts the class activation heatmap as a 2D matrix and then identifies the number of pixels that have contributed to the class decision. These pixels are expressed as a percentage of the total leaf area. Finally, the method returns the percentage area of the affected region.  
Figure 3: Training and test image distribution. The final dataset for maize leaf disease classification and quantification contains 9379 training images of each class and 2345 test images of each class. This represents an 80–20% train–test split. In total, there are 46,898 images in our augmented PlantVillage maize leaf disease dataset.
## 3 Results and Discussion  
### Molecular Identification of C. meneghiniana  
As a typical representative of diatoms, _Cyclotella meneghiniana_ Kutzing 1844 (Basionym: _Stephanocyclus meneghinianus_ [PERSON] 1975) is one of the most insightful explored diatoms with a wide distribution and is able to rapidly absorb nutrients from the water column because of its large cell surface area to volume ratio [51; 52]. The algal species selected for this study (_Cyclotella meneghiniana_) were collected from the field at the mouth of the Rongjiang River, China, site: \(23^{\circ}17^{\prime}5^{\prime\prime}\) N, \(116^{\circ}47^{\prime}11^{\prime\prime}\) E (Figure 1).  
The SEM image revealed that it was circular (centric), and the striae, marginal full-toportula almost on every costa, central filutoportula, rimoportula, spine, and interstice were clearly distinguished by scanning electron microscopy (SEM). The sub-microscopic structure pictures of the algal cells obtained by SEM were basically the same as the results of the study on the identification of the same algal species [53] (Figure 2). Morphological features of _Cyclotella meneghiniana_ have been reported, but there have also been reports of the erroneous extrapolation of _Cyclotella cryptic_ to _Cyclotella meneghiniana_ under morphological observation, and the morphology by itself has proved insufficient to detect the species boundaries in this algal group [54; 55]. The sole reliance on morphological techniques forestablishing species limits has proven inadequate for precise species recognition; thus, employing molecular research approaches can enhance the detection and discernment of the nuanced variances in diatom species classification and demarcation studies [52; 55]. The SEM images identified the diatom as _C. meneghiniana_ (Figure 2), which was further confirmed by PCR using primers designed in this study targeting the ITS.  
Figure 1: Collecting site of _C. meneghiniana_. The red dot on the map represents study area of this research. The purple dot on the map represents sample site of _Cyclotella meneghiniana_.  
Figure 2: SEM pictures of _C. meneghiniana_. Arrows indicate important morphological features, such as striae (S), marginal fuloportula (mFP), central fuloportula (cFP), rimoportula (RP), spine (SP), and interstriae (IS).  
In conducting the phylogenetic study, we retrieved the Internal Transcribed Spacer regions of _C. meneghiniana_ and related sequences from the NCBI database and discovered that the ITS gene sequences in this study were highly similar (100%) to _Cyclotella meneghiniana_ BIMS-PP0014 [46]. A phylogeny with closely related species constructed using ITS1 sequences of _Cyclotella meneghiniana_ (_Stephanocyclulus meneghiniana_) is shown in Figure S1. The sequences of the organism have been deposited in GenBank under accession number STUMBI KY200339.1, named _Stephanocyclus meneghinianus_ STUMBI under the nomenclature Santhoshkumar Chinnappan (Figure 3).
# Viscoelastic Dampers for Rotors: Modeling and Validation at Component and System Level
## 1 Introduction
The choice of GA is motivated by the need of adopting an identification technique that can be easily implemented in industrial electronic control units, since the dedicated machine is supposed to be adopted in the production line for testing operations. In the authors' knowledge, the adoption of GA for the identification of viscoelastic parameters is not yet reported in literature. The designed GA is based on the survival principle of the fittest and aims to search the optimal parameters by minimizing a fitness function that is expressed as the weighted sum of the Mean Square Error (MSE) between the magnitude and phase of the numerical and experimental transfer functions. Three different selection methods are proposed and compared: Normalized Geometric, Tournament, and Roulette Wheel Selection. All of the procedures converge to the same values and the parameters from Normalized Geometric Selection are chosen and translated in classical stiffness and damping components to be useful for the rotordynamic model of assembled rotors. The approach at component level is effective since a good match between the numerical model and the experimental results is shown, even with a relatively low order model of the viscoelastic supports. The methodology is then validated at system level in a real application that is represented by a turbo-molecular pump, a type of multi-stage vacuum machine. The two dampers identified in the first section are the same installed in the pump. The resulting model of the dampers is introduced in the full model of the pump as a connection between the free-free models of the rotor and of the stator. The validation of the proposed approach is performed by impact tests at null speed and by the measurement of the unbalance response. The close correlation between model response and experimental data at standstill, and with the shaft at full speed, demonstrates the effectiveness of the approach also at system level.
# Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration
## 4 Results
### RQ2: What Are the Automated Solutions for Diagnosis in Feature Models from 2010 to August of 2019?  
Table 5 shows that a no-normal tendency exists from 2010 to August of 2019 concerning the research work for diagnosis detection in FMs. In the next lines, we answer each sub-question of RQ2.  
* Sub-question 1: Such as for sub-question 1 of RQ1, we classify the obtained papers regarding their main focus in the next classes: (i) Non-Consistent Feature Model (NC FM); (ii) Non-Consistent Configuration (NC Conf); (iii) Non-Consistent Feature Model and Configuration (NC FM/Conf); (iv) Mutation or Evolution of Feature Model (Mut/Evol); and (v) Specific Fault (Spec Fault) such as void FM, empty FM, dead feature, false optional, partial dependencies, and orphan features. We appreciate that the number of papers for diagnosis detection in FM is much less than the number of papers for conflict detection in FM. Figure 5 summarizes a classification of the obtained papers for diagnosis detection in FMs.
* Sub-question 2: Almost all the articles present new solution proposals. The work of [PERSON] et al. [52] proposes FastDiag, and [28] present its application for the FM analysis. The work of [53] presents FlexDiag, base works for [54]. Likewise,  
Figure 5: Resume of published papers for diagnosis in FMs from 2010 to August of 2019.  
[PERSON] [76] highlights efficient results of FastDiag and FlexDiag solutions. The work of [65] focuses on identifying features necessary to fix for obtaining valid product configurations. Other selected articles present automatic solutions, and then they require a diagnosis process.
* Sub-question 3: Table 5 resumes the following results for articles that focused on diagnosis detection only:
* One article in 2011 using tools and solvers.
* Three articles in 2012: one article using tools, and two articles using tools and solvers.
* One article in 2013 using tools and solvers.
* One article in 2015 using tools and solvers.
* One article in 2016 using tools and solvers.
* One article in 2018 using tools and solvers.
* Sub-question 4: No article presents results on large-scale FMs, that is, FMs with 5000 or more features.
* Sub-question 5: All the reviewed solutions present new or similar performance results concerning their base solutions; that is, those works based in previous works are applied to new data sets, and their results preserve their base ones.  
Figure 6 depicts a summary regarding the papers for this study that focus in conflict and diagnosis detection.  
RQ3: What Are the Automated Solutions for the Completion of Products in Feature Models from 2010 to August of 2019?  
Concerning the completion of partial products, we did not find research works in the search process. The work of [PERSON] et al. [40] focus on the partial feature model, but they did not consider the completion of those models.
## 3 Results and Discussion
### Analysis of Oil Displacement Performance Evaluation  
#### 3.3.1 Analysis of Polymer Injection Capability Experiment  
EOR using polymer solution is closely related to the RF and RRF. The RF is the main parameter reflecting the mobility control and injectivity of polymer solution, and the RRF is an important parameter for the reduction of the reservoir permeability due to the retention of polymer molecules on the rock surface and porous media. At the same time, it also indirectly reflects the concentration loss in the polymer. The larger the RF is, the stronger the ability of the replacement fluid to improve the water-oil flow ratio, which is conducive to expanding the wave volume; the larger the RRF, the greater the EOR after polymer injection.  
Table 6 lists the results of the polymer injection capability experiments. Figure 10 shows the variation in pressure and effluent viscosity with injection volume from the polymer injection capacity experiment. The 2000 mg/L polymer solution was injected into 300 mD and 600 mD cored, respectively. The experimental results show that the polymer could be stably injected into cores with porous media of 300-600 mD. The injection pressure remained stable after a rapid rise. The injection pressures of cores A and B were 2.5 MPa and 1.08 MPa, respectively. After rising rapidly, the viscosities of the effluents were stable at 23.7 mPa-s and 28.2 mPa-s; the viscosity retention rates were 75.7% and 90.1%. The polymer had high viscosity retention. This indicates that the polymer solution could be effectively conducted to the deeper part of the reservoir and establish certain RFs and RFFs. As shown in Table 5, the RF and RRF of core A were higher than those for core B. This indicates that the polymer solution was more likely to be retained in the low-permeability core. When the polymer was adsorbed in core pores, the effective pore volume decreased, the permeability decreased, and the RF and RRF increased. The RF and RRF could still be kept at high values (the values were 353.4 and 98.9 and 259.5 and 81.7). This shows that QJ75-39 had strong abilities to improve the fluidity ratio and to absorb and replenish under the conditions of high temperature and high salinity.  
We can observe that the polymer had good injectivity and conductivity at the salinity of 53,376.8 mg/L and temperature of 95 \({}^{\circ}\)C at 300-600 mD.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **K\({}_{\text{g}}\) (mD)** & **K\({}_{\text{w}}\) (mD)** & **Porosity (\%)** & **Effluent Viscosity** & **Equilibrium** & **RF** & **RRF** \\  & & & & **(mPa-s)** & **Pressure (MPa)** & **RF** & **RRF** \\ \hline A & 300 & 102 & 13.81 & 23.7 & 2.50 & 353.4 & 98.9 \\ B & 600 & 208 & 16.61 & 28.2 & 1.08 & 259.5 & 81.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of results of the polymer injection capability experiment.  
Figure 10: Pressure distribution diagram for polymer injection capability.
# Analysis of Measured Temperature Field of Unpaved Steel Box Girder
## 4 Temperature Gradient Curve  
### Temperature Gradient in Various Specifications  
Table 1 shows the TB 10092-2017, GB/T 51234-2017, BS 5400, BS EN1991-1-5-2003, AASHTO, JTG D60-2015, the New Zealand Bridge Design Code, and the Australian Code [18; 19; 20; 21; 22; 23; 24] for the provisions of the temperature gradient.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Curve Form** & **Characteristics** \\ \hline GB/T 51234-2017 & \multirow{2}{*}{Polyline} & Unpaved steel beams, considering the significant shielding effect of the upper track structure \\ JTG D60-2015 & & With paved steel beams, amendments to the AASHTO code are obtained \\ TB 10092-2017 & \multirow{2}{*}{Exponential Curve} & Concrete beam \\ AASHTO & & Double Line \\ BS EN 1991-1-5-2003 & & With paved steel beams, taking into account \\  & & the temperature extremes of the return period \\ BS 5400 & \multirow{2}{*}{Polyline} & Unpaved steel girders, with paved steel girders \\ The Australian Code & & Double Line \\ The New Zealand Code & & Quintic Parabola \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of temperature gradient loads in various specifications.  
Figure 8: Measured temperature difference curve of the steel box girder.  
AASHTO, the Australian Code, the New Zealand Code, BS EN1991-1-5-2003, and JTG D60-2015 all pertain to steel beams with pavement and are unsuitable for pavement without bridge decks. TB 10092-2017 applies only to concrete structures. All of the six codes above lack provisions for the form of unpaved steel beams. The factors considered in BS 5400 are also comprehensive and give the temperature gradient load values under various working conditions of steel box girders without pavement, and pavement with different thicknesses. However, considering the differences in factors such as sunshine intensity, meteorological conditions, and the geographical environment, BS 5400 is not suitable for structural design in China. GB/T 51234-2017 gives the temperature gradient for pure steel beams, but the specification is based on BS EN1991-1-5-2003, considering a reduction factor of 0.8. The value of BS EN1991-1-5-2003 considers the pavement layer of 40 mm, but the bridge deck of the rail transit bridge is the lower layer; the width and thickness of the upper track structure are relatively large, and its shading effect has an impact on the vertical temperature gradient of the beam section. The reduction effect is noticeable and the value is too small. It can be seen that these two types of specifications also do not apply to the gradient temperature load design of unpaved steel beams in the monorail travel system.  
### Temperature Gradient in Various Specifications  
This section discusses the positive and negative temperature difference curve forms suitable for the temperature gradient of unpaved steel beams, based on the measured data.
## 5 Experimental Evaluation
### Sensitivity Analysis  
We can now consider the sensitivity analysis we performed. Recall that, apart from the full set of features, we also considered the best 80%, 65% and 50% of features, on the basis of the weight defined in Formula 1.  
Figures 5-7 depict the results so far obtained, respectively, with the 80%, 65% and 50% of features. In the 80% case (Figure 5), no significant variations appeared: the performances obtained by all classifiers were, more or less the same. This is also confirmed by looking at the tables, that show very small reductions of accuracy. Nevertheless, the general behavior of the four classifiers remained exactly the same as for the full set of features. Consequently, we could argue that it was not the case to use the full set of features for training the classifiers, so as to save time and computational power.  
Considering the 65% case (depicted in Figure 6), and the 50% case (depicted i Figure 7), we still observed a very slight reduction of accuracy. Only the kNN classifier behaved significantly worse with uni-gram patterns in the 50% case; nevertheless, looking at Figure 7, we notice that with patterns  
Figure 4: Comparing accuracy of classifiers for different feature patterns with 100% features.  
Figure 5: Comparing accuracy of classifiers for different feature patterns with 80% features.  
**U,B,T** and **U,B,T,Q**, the combined feature patterns that contained uni-grams helped the classifier to obtain good results.  
In effect, looking at Table 5, we can see that both precision and recall strongly penalized the kNN classifier, with respect to the other competitors. This happened with all feature patterns.
## 4 Implementation of Design
### Design of Multi-Suspension Unit  
In reference to Section 3.1, the values of \(h_{1}\) and \(h_{2}\) are set to 34 mm and 10 mm, respectively. These dimensions are based on the typical PV array installation structure observed in Vietnam. To satisfy Equation (4), angles \(\gamma_{1}\) and \(\gamma_{2}\) were chosen as 55.6 and 44.4 degrees, and corresponding link lengths \(BC\) and \(CD\) are detailed in Table 6. Consequently, the coordinates of the connecting point \(C_{0}\), as determined by Equation (5), are (257.13, \(-\)9.18). In line with Section 3.2, the positioning of \(AB\) to be perpendicular to \(BC\) is selected when the freewheel is in its highest position, taking into consideration the available installation space for the SPCR at the study site.  
In Section 3.3, the reaction force \(F_{r}\) is determined to have a value of 63.06 N. By substituting Equations (18) to (21) and \(F_{r}\) into Equation (16), the force \(F_{i}\) can be derived. The value of \(F_{i}\) (with \(i=0\)) along with Equation (22) is used in Equation (23) to determine the spring stiffness. The \(Z\) value is chosen as 9.5 [16], from which the spring parameters are determined.  
Table 6 outlines the parameters of the multi-suspension unit, crafted through the design process. Table 7, on the other hand, delineates the adjustment range, denoted as \(L\), for the shock absorber. The specific value of \(l_{d}=92\) mm is selected, taking into account the installation space available for the SPCR in the study area. Once the design parameters are established, the model for the multi-suspension unit is fabricated, assembled, and integrated with the track-wheeled SPCR, as depicted in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline Length of link \(BC\) & 42 & Mean coil diameter \(D_{c}\) & 19 \\ Length of link \(CD\) & 56 & Wire diameter \(d\) & 2 \\ Angle \(\gamma\) & 100 & Number of active coils & 8.5 \\ Spring stiffness \(k\) & 2689 & Free length of the spring & 56 \\  & \(l_{0}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of multi-suspension unit after design.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Dimensions of PV Panel & 1956 \(\times\) 992 \(\times\) 40 & mm \\ Grid size & 10 \(\times\) 10 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical model parameters and simulation limitations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Degree of the Polynomial** & **Equation** & **Correlation Coefficients** \\ \hline
2 & \(z=3.500\times 10^{-5}x^{2}-0.0208x-75.2729\) & 95.92\% \\
3 & \(z=1.677\times 10^{-9}x^{3}+2.6882\times 10^{-5}x^{2}-0.1014x-11.3009\) & 95.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters for design process of single suspension unit.