esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Test of Several Controllers for Underactuated Underwater Vehicles
## 3 Short Description of Selected Algorithms
### Various Approaches to Trajectory Tracking  
In order to compare usefulness of various control strategies, it is necessary, at the beginning, to mention different approaches to trajectory tracking problem for underwater vehicles. Due to the fact that some methods cannot be clearly classified into one group and also due to the popularity of some methods of trajectory tracking, the division into groups of methods given below is rather a suggestion.  
1. Lyapunov theory-based methods [1; 2; 3; 4].
2. Backstepping (including SMC, output feedback control, Lyapunov method, and others) [5; 6; 7; 8; 9; 10; 11; 12; 13; 14].
3. Sliding mode control (SMC)-based approaches [16; 17; 18; 19].
4. Neural network-based control algorithms and its combination [20; 21; 22; 23; 24; 25; 41].
5. Fuzzy control-based methods [26; 27].
6. Other solutions, e.g., prescribed performance [30; 31], input-output linearization [34], linear algebra approach [35], modified dynamic inversion [36], and model predictive control (MPC) [32; 33; 42].  
The methods of the first group are well-known but often they are used together with the backstepping techniques to obtain better mathematical results or controller performance. The disadvantage of this type of method, however, is very accurate knowledge of the dynamic parameters of the vehicle are needed, which is usually difficult to obtain. The second group, namely approaches using backstepping, lead to reliable and effective controllers and they are easy to understand and implement. Many algorithms need output errors to satisfy constraint requirements to avoid singularity. Additionally, the traditional backstepping method caused complexity. Combinations of this method and other approaches are used to reduce the disadvantages of the control strategy. The third group, i.e., schemes based on SMC, is effective for the AUV trajectory tracking controllers. These techniques offer low sensitivity to uncertainty and good disturbance rejection. The methods have difficulties in solving the chattering associated with SMC. Neural networks control (the fourth group),especially adaptive, is one of the tools for improving the tracking performance of AUVs. However, the control schemes are often combined with other types of methods based on a different mathematical approaches, which is a considerable difficulty. In addition, learning of the neural networks is time consuming because the approach heavily depends on the NN nodes. Fuzzy control (the fifth group) is another effective solution to deal with uncertainties. It has been employed many times for application of control in robotics due to its simple structure and because of the easy implementation. Controllers designed for fuzzy systems depend strongly on the number of the fuzzy rule bases, resulting in computational effort. In spite of that, NN or fuzzy-based approaches are very useful for the control of nonlinear dynamics. The methods collected in the sixth group are less popular although they also lead to effective solutions for the trajectory tracking.
## 4 Conclusions  
To examine the effect of drainage conditions and rubber fibers on the mechanical properties of expansive soil, undrained triaxial, drained triaxial consolidation, and NMR ESR tests were performed in this study. The effects of rubber fibers on the ESR stress-strain relationship, tangential stiffness, maximum dilatation angle and pore size distribution were investigated. The main conclusions are as follows:  
1. With an increase in strain, the ESR deviator stress and pore water pressure for different rubber fiber contents increased nonlinearly. The ESR showed compressive behavior during the shearing process. The rubber fiber-reinforced expansive soil showed a higher initial contraction than the unreinforced expansive soil.
2. The shear strength of ESR was affected significantly by the drainage conditions, which were influenced by the pore water pressure and creep of the fiber-soil interface. Under the consolidated drained conditions, the deviator stress and effective cohesion of the ESR were higher than those under the undrained conditions. The converse was true for the internal friction angle.
3. After the addition of rubber fibers, the shear strength of the expansive soil improved. However, its stiffness reduced. After a significant addition of rubber fibers to the  
Figure 14: Comparison of dilatancy equations with test results (ESR-7).  
unreinforced soil, the stretching effect of the rubber inhibited the formation of a face-to-face arrangement of soil particles. This alleviated the yield phenomenon of the ESR.
* The addition of an appropriate amount of rubber fiber (5-10%) reduced the proportion of small pores and micropores in the ESR, increased the proportion of ultra-micro pores, and inhibited the development of soil cracks. When the rubber content was excessively high (15%), the fibers interweaved into clusters. This resulted in an increased porosity and a weakened internal structure of the ESR.  
This study provides a reference for the application of rubber fibers in the backfill, and the beneficial reuse of recycled tires provides a sound environmental alternative to the safe disposal problems associated with waste materials.  
Conceptualization, Z.Y.; data curation, writing--original draft, R.W.; methodology, W.S.; supervision, Z.S.; project administration, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42177153, and the National Major Scientific Research Instrument Development Project, grant number 41627801.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors thank the School of Civil Engineering, Qingdao University of Technology for providing the NMR instrument.  
The authors declare no conflicts of interest.
## 3 Results
### Change in Size Structure  
The size structure of the _Spirulina_ strain varied among salinity treatments (Figure 3a). At lower salinity treatments (10-22 ppt), the densities exceeded 30,000 filaments mL\({}^{-1}\), with the majority measuring less than 400 \(\upmu\)m. On the other hand, densities at higher salinities (27-45 ppt) had lower densities (<20,000 filaments mL\({}^{-1}\)), but the composition of medium-sized filaments (400-799 \(\upmu\)m) is greater relative to lower salinity treatments.  
This shift in size structure is further illustrated through Principal Component Analysis (PCA) (Figure 3b). The first two principal components accounted for 85.9% of the total variance in the size structure. PC1 (51.5%) distinguished the lower salinity treatments (10-22 ppt) with higher relative abundance of small- (<200 \(\upmu\)m and 200-399 \(\upmu\)m) and large-sized filaments (800-999 \(\upmu\)m and >1000 \(\upmu\)m) from the higher salinity treatments (27-45 ppt) with greater abundance of medium-sized filaments (400-599 \(\upmu\)m and 600-799 \(\upmu\)m). PC2 (34.4%) separated treatments by smaller size fractions.  
The PERMANOVA analysis revealed a significant effect of salinity on the size structure (F(1, 6) = 8.47, \(p\) < 0.01). The model explained 59% (Sum of Squares = 0.03, R\({}^{2}\) = 0.59) of the variation in size composition, while the residual accounted for 42% (Sum of Squares = 0.02, R\({}^{2}\) = 0.42), indicating that substantial unexplained variation remains.  
Figure 2: Effects of salinity on the productivity of the _Spirulina_ strain. (**a**) Gross primary production (GPP) of _Spirulina_ strain as a function of light intensity under different salinity treatments. Each panel represents a specific salinity level (10–45 ppt), showing the response of GPP (mg O\({}_{2}\cdot\)L\({}^{-1}\cdot\)d\({}^{-1}\)) to increasing light intensity (\(\upmu\)mol photons\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)). Black symbols (\(\bullet\), \(\blacktriangle\), and \(\blacksquare\)) represent individual observations, while blue lines indicate fitted linear regressions with confidence intervals. (**b**) Bar plots showing the differences in photosynthetic efficiency of _Spirulina_ strain across salinity treatments. Error bars represent standard errors.
## 2 Related Works
### TSR Process
#### 2.1.2 Preprocessing  
Preprocessing is a critical step in the TSR process, as it helps to enhance the quality of the input images and improve the accuracy of the recognition system. There are several preprocessing techniques that can be applied to traffic sign images, depending on the specific characteristics of the image data and the requirements of the recognition system. In  
Figure 1: Road sign categories.  
Figure 2: Traffic Sign Recognition (TSR) process.  
fact, these techniques depend on the requirements of the system and the characteristics of the data, like removing noise, standardising input data, and enhancing the features of the image that are relevant to classification.  
Some of the most common preprocessing techniques used in traffic sign recognition include:  
* Image cropping involves selecting a region of interest (ROI) that contains only the traffic sign and removing any background information. This can help reduce the amount of irrelevant data that the machine learning model needs to process, leading to faster and more accurate classification;
* Resizing images to a fixed size can help standardise the input data, which is important for some machine learning algorithms that require consistent input dimensions. It can also reduce the computational load and improve the speed of the system;
* Image normalisation involves scaling the pixel values to a common range, such as [0, 1] or [\(-\)1, 1]. This can help improve the accuracy of the system by reducing the impact of lighting variations and enhancing the contrast of the image;
* Image smoothing techniques like Gaussian blur can help reduce noise in the image and make the edges of the traffic sign more distinct. This can help improve the accuracy of edge detection algorithms and enhance the features that are relevant to classification;
* A colour space conversion such as HSV or YUV can help separate the colour information from the luminance information. This can help improve the accuracy of colour-based segmentation and enhance the relevant features for classification.
## 2 Considerations in Selecting a Chemical Crosslinker  
The fundamental aim of chemical crosslinking is to improve the mechanical properties and stability of the final processed collagen product. However, the selection of an appropriate crosslinker may depend on several factors. Since collagen is often used in biomedical applications to replicate the biochemical environment experienced in vivo, amine-based crosslinkers [1; 2; 3; 4; 5] are often chosen in order to mimic the lysine-based crosslinks which are naturally present in collagen [6]. The choice and mechanism of crosslinking can also inadvertently modify other structural attributes and the corresponding biological response. For instance, chemical crosslinkers may be broadly classified by their potential to incorporate the crosslinker directly into the protein. This results in the 'zero length' crosslinkers, which do not remain a part of the protein structure post-crosslinking, or 'non-zero length'crosslinkers, where some or all of the crosslinker is incorporated. While zero-length crosslinkers may modify the local chemical structure, resulting in non-native like cellular interactions [7], there is also some concern with the potential of non-zero length crosslinked collagen films to release cytotoxic products when metabolised [8].  
This review is centred around an assessment of some of the most common crosslinking agents which are predominantly amine-based, and cover a range of zero length as well as non-zero length crosslinkers. These include traditional crosslinkers such as glutaraldehyde(GTA), a non-zero length crosslinker, and EDC-NHS, ultraviolet radiation (UV), dehydrothermal treatment (DHT), which are zero-length crosslinkers. Novel and emerging crosslinking methods include the use of genipin as a non-zero length crosslinker, as well as riboflavin and tissue transglutaminase 2 (TG2) as zero-length crosslinkers. This review also focuses on the effect of crosslinking on Type I collagen, and uses a standardised _w/w_% notation to indicate the mass of the crosslinker used per gram of collagen. Following an assessment of these crosslinkers on various physical, chemical and biological properties in collagen-based constructs, an appropriate crosslinker can be therefore be chosen on the basis of the properties demanded by the application of interest.
## 3 Proposed Scheduler to integrate the MES and AIV
### Scheduler Development
#### 3.3.1 Check Status of Pickup Locations  
Firstly, _'WebserviceA'_ of the MES is imported into LabVIEW, and then an equivalent VI is edited and configured accordingly, as discussed in Section 3.1. This webservice provides product availability information at pickup locations. The operation of this step is shown in Figure 10. The scheduler sends a request to the MES, this request is in terms of operation and resourceld fields. Here, the PICK operation is requested, and as a response the scheduler receives many parameters from the MES, as shown in Figure 10. The information about the total number of active pickup locations (supported by MES) and the number of active products at each pickup location is not directly provided from the MES. This information is extracted by applying the _'Array Size'_ function of LabVIEW on resourceList (for pickup locations) and sfcList (for active products) response. The scheduler continuously looks for products available for pick. If there is no product at any pickup location, the scheduler will send the AIV to a temporary holding location (PICK_Holding_Bay) and the scheduler will continuously keep looking for active products again. The PICK_Holding_Bay is a predefined location, which is relatively near to the pickup locations. This feature was introduced to save AIV travel time as the AIV can reach selected pickup locations faster. If the product will be available at any pickup location, then the scheduler will transfer product details to the next step.  
Figure 9: Steps of one complete cycle of scheduler operation.  
Figure 10: Check status of pickup locations.
# A Fundamental Study on an SAP Mixed Asphalt Mixture for Reducing the Urban Heat Island Effect
## 3 Results and Discussion  
### Strength Characteristics of the SAP Asphalt Mixtures  
#### 3.1.1 Marshall Stability Results  
Figures 6 and 7 present the results of Marshall stability tests of asphalt mixtures manufactured by SAP type and content. The Marshall stability of all specimens satisfied the Korean asphalt mixture design standard of 7500 N. In addition, the flow values of all specimens, except for the specimens with an SAP-3500 content of 7%, satisfied the design standard of 20-40 mm. In the case of the Marshall stability test, the specimen is immersed in a 60 \({}^{\circ}\)C water bath for 30 min before the test is conducted. Therefore, when the asphalt and SAP are mixed, the SAP that absorbs water may expand and the Marshall stability value may decrease. The Marshall stability of the SAP-500 mixture decreased rapidly at 7% because the SAP absorbed and expanded while being coated on the aggregate together with the asphalt binder, weakening the bonding strength between the binder and the aggregate. The SAP absorbs and expands water to fill the internal voids, but when it exceeds a certain amount, expansion stress acts inside the specimen, widening the gap between the aggregates and weakening the stability [19]. The Marshall stability results of the specimens exposed outdoors for 30 and 90 days showed that the stability decreased after 30 days and increased again after 90 days. The asphalt mixture was affected by repeated expansion and contraction due to the active reaction between SAP and moisture during the 30-day outdoor experiment, but after that, the reactivity decreased and the effect on the mixture decreased, so the Marshall stability increased as shown in the figures.  
Figure 5: Outdoor experiment appearance.
## 5 Evaluation
### Metrics
#### 5.2.2 Robustness Metrics  
The robustness of the estimation algorithm is determined by its ability to withstand poisoning attacks, in which a poisoning attempt fails to alter the estimation outcomes. We measured this robustness using the concept of \"frequency gain\", which is defined as the cumulative difference between the estimated and poisoned frequencies for the targeted keys. Formally, the frequency gain is expressed as  
\[G_{f}(Y)=\sum_{k\in T}E[\Delta\hat{f}_{k}], \tag{13}\]  
where \(\Delta\hat{f}_{k}=\tilde{f}_{k}-\hat{f}_{k}\) represents the distance between the estimated and poisoned frequencies for key \(k\), in which \(\tilde{f}_{k}\) is the estimated frequency when key k is targeted by a poisoning attack.  
Similarly, the \"mean gain\" is calculated as the aggregate difference between the estimated and poisoned values:  
\[G_{m}(Y)=\sum_{k\in T}E[\Delta\hat{m}_{k}]. \tag{14}\]  
Here, \(\Delta\hat{m}_{k}=\hat{m}_{k}-\hat{m}_{k}\), with \(\hat{m}_{k}\) representing the estimated mean value when key \(k\) is subjected to a poisoning attack.
# Robust Template-Based Watermarking for DIBR 3D Images
## 4 Experimental Results
### Image Quality  
As shown in Figure 11, the quality degradation of the watermarked image is not noticeable. For more accurate image quality measurements, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [52] were measured. The average PSNR and SSIM are shown in Table 4.  
[PERSON]'s method has a lower PSNR despite it exploiting a spread spectrum-based watermarking method that is similar to the proposed method. Since the 2D-DCT is not invariant to DIBR, [PERSON]'s method must insert three watermarks in a superimposed manner in order to protect the left, right and center image. For this reason, the inserted watermark energy is very large.  
In [PERSON]'s method, the images are seriously blurred, because this method cuts the coefficient off excessively. As a result, both the PSNR and SSIM values were low.  
The proposed method increases imperceptibility by taking advantage of the curvelet and 1D-DCT, which are robust against DIBR. Due to this robustness, the proposed method does not require excessive insertion of the watermark. In particular, the message watermark using the 1D-DCT does not require watermark insertion in a superimposed manner, so the insertion energy can be reduced to about one-third as compared with the 2D-DCT. In addition, since the template and the message can be inserted into different blocks after dividing the block, it is possible to prevent the invisibility degradation caused by the overlapping of templates and message watermarks. For these reasons, PSNR outperforms the other methods and shows similar performance to the best methods according to SSIM.  
\begin{table}
\begin{tabular}{l c c} \hline  & **PSNR** & **SSIM** \\ \hline Proposed method & 50.03 & 0.9942 \\ [PERSON]’s method & 45.12 & 0.9936 \\ [PERSON]’s method & 44.02 & 0.9878 \\ \hline \end{tabular}
\end{table}
Table 4: Average PSNR and SSIM.  
Figure 10: Test image sets. (**a**) Aloe; (**b**) baby; (**c**) ballet; (**d**) bowling; (**e**) breakdowners; (**f**) cloth; (**g**) flowerpot; (**h**) lampshade; (**i**) interview.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 4 Results
### Performance Evaluation of the Proposed Feature Selection  
A set of five classification algorithms, Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron, and Logistic Regression were used to evaluate the efficiency of the optimal feature set obtained using FCM-based feature selection. Table 3 represents the accuracy values obtained for different classification algorithms when the datasets taken into consideration were evaluated after FCM-based feature selection. The results depict that the Random Forest algorithm tended to produce maximum accuracy values as compared to other classification algorithms. Random Forest algorithms use bootstrap aggregation and randomization in selection of data nodes during the construction of decision trees to obtain a high degree of predictive accuracy. The Random Forest algorithm also tends to have the capability to handle large datasets more efficiently compared to other classification algorithms; therefore, Random Forest was chosen as the classification model to evaluate the performance and efficiency of the proposed feature selection method. An ensemble of individual decision trees comprises the Random Forest algorithm. Each tree in the random \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Vector** \\ **Slicer** \\ \end{tabular} & **RFormula** & \begin{tabular}{c} **ChiSq** \\ **Selector** \\ \end{tabular} & \begin{tabular}{c} **Univariate** \\ **Feature** \\ **Selector** \\ \end{tabular} & \
\begin{tabular}{c} **Variance** \\ **Threshold** \\ **Selector** \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Model** \\ \end{tabular} \\ \hline \hline Pima Indians & 3 & 4 & 5 & 3 & 4 & 3 \\ Diabetes dataset & & & & & & \\ Page Blocks & 4 & 5 & 6 & 3 & 4 & 3 \\ Classification Data Set & 5 & 4 & 6 & 7 & 6 & 5 \\ US Congressional & 12 & 10 & 9 & 11 & 10 & 10 \\ Voting Records dataset & 10 & 11 & 9 & 7 & 8 & 6 \\ Zoo dataset & & & & & & \\ Climate Model & & & & & & \\ Simulation Crashes & 11 & 10 & 9 & 8 & 7 & 9 \\ Data Set & & & & & & \\ kcl & 13 & 10 & 12 & 9 & 11 & 10 \\ pc1 & 10 & 12 & 11 & 13 & 9 & **8** \\ Parkinson Disease & 10 & 11 & 12 & 13 & 9 & 10 \\ Detection dataset & & & & & & \\ Lung Cancer Data Set & 13 & 12 & 11 & 10 & 9 & 12 \\ Breast Cancer & & & & & & \\ Wisconsin (Diagnostic) & 21 & 24 & 20 & 18 & 23 & **20** \\ Data Set & & & & & & \\ cm1 & 25 & 22 & 20 & 17 & 15 & **12** \\ Polycystic ovary & & & & & & \\ syndrome (PCOS) & 25 & 35 & 27 & 36 & 20 & **17** \\ dataset & & & & & & \\ Connectionist Bench & & & & & & \\ (Sonar, Mines vs. & 30 & 40 & 35 & 31 & 42 & **30** \\ Rocks) Data Set & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of features selected by different feature selection algorithms. forest produces a class prediction, and the class with the most votes becomes the prediction of the model.