esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 4 Robotic Maintenance Technology in Other Industries
### Mobile Manipulators for Inspection and Repair Tasks
#### 4.2.5 Application of Mobile Manipulator in Agriculture  
The world population continues to increase, but cultivatable land and the workforce are decreasing [115]. Food production methods demand exact and efficient robotic solutions. Examples include a dredger robot that can perform maintenance tasks such as removing debris, weeds, and sediments [116].  
Mobile manipulators can assist and even replace humans in labor-intensive fruit or vegetable picking. To succeed, they should be able to locate the plant, determine the ripeness of fruits, navigate autonomously, and pick the fruits or vegetables without damaging them [117; 118; 119; 120]. As shown in Figure 15(a), most of these mobile manipulators are made of ground-based mobile-base robots equipped with a vision system for fruit detection [119].  
Another complex task conducted by mobile manipulators is detecting weeds and spraying pesticides while navigating the traversal of terrain. [PERSON] et al. proposed a pesticide-spraying robot with a skid-steering mobile manipulator [121]. Tracked wheels  
Figure 16: Mobile manipulator for agricultural application: (**a**) strawberry picking robot [117]; (**b**) pest detection and spraying robot for a greenhouse [15].  
Figure 15: Proposed mobile manipulator system with multiple sensors [110].  
with skid-steering mechanisms were used instead of pneumatic wheels, as tracked wheels improved the navigation capability on deformable terrain. [PERSON] et al. demonstrated a mobile manipulator with a robot operating system (ROS), shown in Figure 16b, fitted with multiple sensors (RGB camera, RGB-D camera, GNSS, IMU, LiDAR, and laser scanner) on a Segway RMP Omni Flex mobile robot [122; 123] with mecanum wheels for pest detection and spraying inside a greenhouse [17].
## 2 Materials and Methods
### Modelling Techniques  
The experimental data were loaded, checked and divided into training and test sets (Appendix A). The values for the input track overlapping and hatch spacing from the experimental data were omitted from the data used for modelling, as these values did not change during the experiment and could lead to skewing the model with invariant features. The relative density compared to the volumetric mass density of magnesium of 1.738 [g/cm3] was selected as the output value and expressed as a percentage for better readability. The modelling workflow is presented in Figure 3.  
As mentioned, XGBoost was used as a fundamental method for the modelling framework. XGBoost is known as a powerful and scalable tree-boosting algorithm that has become a popular tool for regression tasks in ML for engineering applications. It uses a gradient boosting framework to iteratively refine prediction accuracy, outperforming traditional regression methods [23]. The specific parameters for the XGBoost used are defined in Table 3.  
These parameter settings were chosen with the aim of achieving a balance between model performance, generalisation and computational efficiency. The range of values for each parameter allowed a comprehensive search in the hyperparameter space to determine the optimal combination.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values** & **Description** \\ \hline N estimators & [100, 200, 500] & Number of boosting rounds (trees) to fit \\ \hline Max depth & [3; 5; 7] & Tree maximum depth, where a higher depth increases the model complexity \\ \hline Learning rate & [0.01, 0.05, 0.1] & Step size shrinkage used to prevent overfitting, where smaller values slow down the model’s learning \\ \hline Subsample & [0.6, 0.8, 1.0] & Proportion of the training data that was randomly selected for each boosting round; this was used to reduce overfitting \\ \hline Alpha & [0, 0.1, 0.5] & L1 regularisation term used to control the parsimony of the model, where larger alpha values lead to a stronger regularization \\ \hline Lambda & [1, 1.5, 2] & L2 regularisation term used to control the resulting model complexity, where larger lambda values penalise large coefficients \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used for modelling with XGBoost.  
Figure 3: Modelling workflow.
# Applications of Plasma Produced with Electrical Discharges in Gases for Agriculture and Biomedicine
## 3 Review of Plasma Applications in Agriculture and Medicine
### Plasma Applications in Agriculture
#### 3.1.3 Cold Plasma Applications in Pre-Sowing Seed Treatment  
Cold atmospheric plasma (CAP) exposure in combination with electromagnetic field (EMF) treatment was found to be an innovative technique for the enhancement of seed germination (even up to 20%), removal of surface contamination, and early seedling growth [141; 142; 143]. This approach results in long-term changes in plant metabolism, increasing of biomass production; it also increases plant disease resistance [144; 145; 146; 147]. Moreover, CAP application improves agricultural performance of crops as reported in [109; 142].  
Cold plasma is quite an effective tool for pre-sowing seed treatment [141; 148; 149; 141]. Lithuanian and Belarusian scientists reported Norway spruce germination and growth after CAP seed treatment [141]. A DBD planar geometry plasma reactor with an RF power supply system was used in the experiment [148]. The researchers found that most treatments accelerated the germination rate of spruce seeds in cassettes, whereas in vitro germination decreased. The seeds were processed for 2, 5, and 7 min. Such treatment in combination with the application of EMF induces changes in H\({}_{2}\)O\({}_{2}\) concentration in germinating seeds. [PERSON] et al. used the same approach to study changes in the growth and production of non-psychotropic cannabinoids induced by pre-sowing treatment of hemp seeds [149]. In this case, plasma stimulated the in vitro germination rate under laboratory conditions even up to 25%. The authors noticed that the treatment accelerated growth in male plants and inhibited growth in female plants.  
The Belarusian group investigated the effect of pre-sowing plasma treatment of maize, narrow-leaved lupine, and winter wheat seeds on germination, disease resistance during vegetation, and crop yield in laboratory conditions [146]. [PERSON] et al. employed the DBD technique using a capacitively coupled 5.28 MHz plasma reactor. The seeds were treated in an air atmosphere with an ambient pressure of 200 Pa, and a temperature of 37 \({}^{\circ}\)C. Treatment duration varied from 2 to 7 min. The researchers reported the decrease in contamination of maize seeds with _Penicillium_ spp. by approx. 21% after plasma treatment. In addition, positive changes in germination and maize sprout length were observed. In the case of lupine seeds, the plasma treatment caused the death of all _C. gloeosporoides_ and _K. caulivora_, and significantly reduced the amount of _Cladosporium_ and _Alternaria_ fungi. After plasma treatment, winter wheat seeds demonstrated a reduction in contamination with _Alternaria_ and _Fusarium_ fungi. [PERSON] et al. summarised the plasma treatment effects as stimulation in germination and growth of seeds, fungal contamination reduction, improvement in the yield of plants, and crop plant resistance enhancement.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 5 New Localization Module: GNSS/SLAM Fusion
### GNSS Subsystem  
The GNSS system provides geolocalization in world coordinates (world frame) but to perform the fusion with the subsystem described above we need to convert the global coordinates to map coordinates. For this we use the navsat node available in the ROS robot localization package [41]. In the initialization step, this node receives a position expressed in the frame we want to use as reference--in  
Figure 8: _ROS nodes_ scheme that shows the second implementation of our framework which incorporates our novel GNSS fusion localization algorithm. This new implementation has been completed just substituting the previous localization module and adding the GNSS sensor driver. We show these modifications with solid fill in the nodes added or replaced with respect to Figure 3.  
Figure 7: ROS nodes scheme that shows the combination of subsystems that make up the new localization module that will be tested using our framework. This scheme corresponds to the “global navigation satellite system (GNSS)-a Monte Carlo localization (AMCL) fusion” node shown in Figure 8. The “Kalman filter” node uses odometry as a control action, and odometry-GNSS and pose AMCL as observations.  
our case the position calculated through AMCL depicted as a green dotted line in Figure 7--and generates a \(T\) transformation between the pose in world frame to the map frame. Then \(T\) is applied to all the measurements made by the GNSS. By using only the navsat node, we could only read the GNSS fix and generate a 2D position in the map frame, but without orientation. To overcome this issue we generated a new ROS node called odometry GPS (Figure 7) in which we receive the velocity fix provided by the GNSS and the position and the \(T\) calculated by navsat. The output of this node is the orientation in the map frame, obtained using the velocity vector (provided by GNSS velocity fix) and the transform between the global frame and the map frame given by the navsat node.
# Biosorption of Heavy Metals by the Bacterial Exopolysaccharideaccharide FucoPol
## Materials and Methods
### Effect of Dosage, pH and Temperature on Pb\({}^{2+}\) Biosorption Ability of FucoPol  
The effect of FucoPol dosage (1, 2, 3, 5 and 10 g/L) on FucoPol metal binding capacity towards Pb\({}^{2+}\) was investigated. Moreover, various initial concentrations of Pb\({}^{2+}\) solutions (3, 10, 48 and 100 mg/L) were tested for a FucoPol concentration of 5 mg/L. The effect of the pH value (2.5, 3.3 and 4.3) was also tested by adjusting the initial pH of the metal solution with HCl (1.0 M) or NaOH (1.0 M). The impact of temperature (5, 21, 30 and 40 \({}^{\circ}\)C) in the metal removal abilities of FucoPol was also studied by keeping the orbital shaker with the flasks in an incubator set at different temperatures (21-40 \({}^{\circ}\)C) and in a cold chamber (5 \({}^{\circ}\)C).
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## Appendix B ADMM Algorithm  
The minimization problem in (19) is described as a standard form for ADMM [26], and the iteration algorithm is directly obtained by \[\begin{split}\begin{bmatrix}Z_{1}[i+1]\\ Z_{2}[i+1]\end{bmatrix}&=\operatorname*{arg\,min}_{Z_{1},Z_{2}}f_{1}(Z_{1},Z_{2 };\tilde{Z}[i],W[i]),\\ \tilde{Z}[i+1]&=\operatorname*{arg\,min}_{\tilde{Z}}f_{2}(\tilde{Z};Z_{1}[i+1],Z_{2}[i+1],W[i]),\\ W[i+1]&=W[i]+Z[i+1]-\begin{bmatrix}X[i+1]&I\\ I&Y[i+1]\end{bmatrix},\\ i&=0,1,2,\ldots,\end{split} \tag{24}\]  
where  
\[f_{1}(Z_{1},Z_{2};\tilde{Z},W) \triangleq\|Z_{1}-X_{1}\|^{2}+\|Z_{2}-X_{2}\|^{2}+\frac{\rho}{2} \left\|\tilde{Z}-\begin{bmatrix}Z_{1}&I\\ I&Z_{2}\end{bmatrix}+W\right\|^{2}, \tag{25}\] \[f_{2}(\tilde{Z};Z_{1},Z_{2},W) \triangleq\mathcal{I}_{r}(\tilde{Z})+\frac{\rho}{2}\left\|\tilde {Z}-\begin{bmatrix}Z_{1}&I\\ I&Z_{2}\end{bmatrix}+W\right\|^{2}. \tag{26}\]  
First, for function \(f_{1}\), we have  
\[f_{1}(Z_{1},Z_{2};\tilde{Z}[i],W[i])=c\left\|Z_{1}-c^{-1}\bigg{(} X_{1}+\frac{\rho}{2}M_{11}[i]\bigg{)}\right\|^{2}\\ +c\left\|Z_{2}-c^{-1}\bigg{(}X_{2}+\frac{\rho}{2}M_{22}[i]\bigg{)} \right\|^{2}+\text{const}. \tag{27}\]  
where \(c\triangleq 1+\rho/2\), and \(M_{11}[i]\), \(M_{22}[i]\) are defined in (28). From (27), we have the first two steps (24) and (25).  
Then, by the definition of projection, a minimizer of \(f_{2}(Z;Z_{1}[i+1],Z_{2}[i+1],W[i])\) in (26) is obtained by the right-hand side of (26).
## 3 Method
### Accuracy Assessment  
#### 3.2.1 Simplification Error  
Distribution envelope simplification can lead to deformation and area change. The overall error (OE) takes into account both the increase and decrease in the deformation compared to the original distribution envelope. Therefore, the overall error was adopted to evaluate the deformation error. The OE formula is as follows:  
\[\text{OE}=(S_{\text{i}}+S_{\text{d}})/S_{0}, \tag{2}\]  
where \(S_{0}\), \(S_{\text{i}}\), and \(S_{\text{d}}\) denote the areas of the original envelope, and the increased and decreased parts of the simplified envelope, respectively.  
The algebraic error (AE) is the ratio of the area difference between the simplified and the original envelope to the original envelope area, and can be used to evaluate the area change. The AE formula is as follows:  
\[\text{AE}=(S-S_{0})/S_{0}, \tag{3}\]  
where \(S_{0}\) and \(S\) denote the areas of the original and simplified envelope, respectively.  
Figure 3: Side lengths and angles of green tide distribution envelope on 17 May 2021.
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 5 Conclusions  
This article presents a stable and high-precision autonomous orbit determination scheme for LLO spacecraft based on inter-satellite ranging and lunar laser ranging. This orbit determination scheme removes the dependence on ground measurement and control stations and mainly relies on lunar corner reflectors. The simulation results show that compared to autonomous orbit determination which depends exclusively on inter-satellite measurement, this scheme has a faster convergence speed, higher convergence accuracy, and better robustness of the navigation system to initial orbit errors and orbit dynamic model errors. Overall, it can provide valuable engineering references for the autonomous navigation of lunar-orbiting satellites.  
Conceptualization, S.C. and W.W.; Methodology, S.C., S.L. and J.P.; Software, S.C.; Validation, S.C.; Investigation, S.C. and Y.X.; Resources, W.W.; Data curation, S.C.; Writing--original draft, S.C.; Writing--review & editing, W.W. All authors have read and agreed to the published version of the manuscript.  
This research received the funding support form the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA 300103000.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
###### Abstract
[27] compared Landsat-7 and Landsat-8 using similar algorithms and found that the latter showed a higher accuracy than the former satellite. Similarly, [PERSON] et al. [29] recorded a higher accuracy on ALOS-2 dual-polarization bands than the Landsat-8 optical imagery data with a maximum likelihood classifier. [PERSON] et al. [30] tested Sentinel-1 and Sentinel-2 satellite imagery to enhance mapping accuracy and found a higher accuracy for Sentinel-2 data than Sentinel-1 data in conjunction with the SVM algorithm. The above-mentioned results proved that the accuracy of LULC maps depends on the choice of satellite and the classification algorithms used. Due to the high cost associated with the collection of ground truth points and the heightened demand for efficient natural resource management, the objective of this study was to evaluate the efficiency of ML algorithms in limiting the use of ground truth data for LULC maps. This will be accomplished by extracting LULC information from Sentinel-2 and Landsat-8 satellite images and by generating remote sensing indices used to train ML algorithms. The results of this paper are divided into three parts. First, results from the ML algorithms were evaluated against ground truth data. Second, standard statistical measures were used to evaluate the performance of each ML algorithm. Third, algorithms were compared to each other to understand their performance better. The paper has been divided into two main sections, i.e., the materials and methods section and the results and discussion section. The materials utilized in this investigation and their processing details are mentioned in the materials and methods section. The accuracy of the ML algorithm is examined and compared in the results and discussion section with the findings of prior studies.
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 3 Vehicular Communication
### Nomenclature  
The following nomenclature is used: _ad hoc communication_ describes the communication based on IEEE 802.11p. _LTE communication_ denotes communication using the cellular infrastructure only, where the Long-Term Evolution (LTE) standard is applied. _C-V2X communication_ refers to direct vehicle-to-vehicle communication either in Mode-3 or Mode-4 in LTE.
###### Abstract  
Q-learning has been primarily used as one of the reinforcement learning (RL) techniques to find the optimal routing path in wireless sensor networks (WSNs). However, for the centralized RL-based routing protocols with a large state space and action space, the baseline Q-learning used to implement these protocols suffers from degradation in the convergence speed, network lifetime, and network energy consumption due to the large number of learning episodes required to learn the optimal routing path. To overcome these limitations, an efficient model-free RL-based technique called Least-Square Policy Iteration (LSPI) is proposed to optimize the network lifetime and energy consumption in WSNs. The resulting designed protocol is a Centralized Routing Protocol for Lifetime and Energy Optimization with a Genetic Algorithm (GA) and LSPI (CRPLEOGALSPI). Simulation results show that the CRPLEOGALSPI has improved performance in network lifetime and energy consumption compared to an existing Centralized Routing Protocol for Lifetime Optimization with GA and Q-learning (CRPLOGARL). This is because the CRPLEOGALSPI chooses a routing path in a given state considering all the possible routing paths, and it is not sensitive to the learning rate. Moreover, while the CRPLOGARL evaluates the optimal policy from the Q-values, the CRPLEOGALSPI updates the Q-values based on the most updated information regarding the network dynamics using weighted functions.  
wireless sensor network; routing; network lifetime; energy consumption; reinforcement learning; path optimization; least-squares policy iteration
# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 3 The Proposed Method
### The Extension of BM3 DELBP to 3D (BM3 DELBP_3D)  
We proposed in [28] the extension of the BM3 DELBP operator to volumetric texture feature extraction. Figure 4 illustrates the functional block scheme describing the computation of this operator by considering only one observation scale.  
Figure 4: Block scheme of the 3D version of BM3 DELBP for a single scale (© [2020] IEEE. Reprinted [adapted], with permission, from [28]).  
Figure 3: The block scheme of the proposed feature extraction technique.  
For each 3D input image, the BM3 DELBP codes are extracted separately from each of the three orthogonal planes XY, XZ and YZ. As the input image is a cube, for each plane, there are several image slices. The BM3 DELBP operator is thus applied separately on each slice of each plane. For each slice, the BM3D denoising technique is applied and then, by considering the responses to the applied filter, the three components of the BM3 DELBP operator are computed: BM3 DELBP_CI, BM3 DELBP_NI and BM3 DELBP_RD. The feature vector corresponding to one orthogonal plane results from the concatenation of the histograms obtained for each volumetric component. A multiresolution strategy is used to attain a degree of invariance to the change of the observation scale based on the same strategy described in Section 2.2. Two scales were considered given by their radius: R = 2 and R = 4, respectively. In [22], four scales were used for 2D image classification. Since we deal with volumetric images and the computation complexity is challenging, we proposed reducing the number of scales to two in order to minimize the feature vector size and to avoid the classic curse of dimensionality problem. Figure 5 presents the considered multiresolution strategy for the 3D case.  
The operations involved are the ones from Figure 4 and they are considered for both scales of interest, the resulting feature vector being obtained by concatenation.
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 3 Weather Conditions and OCDMA Code
### ZCC Code Construction  
The ZCC code is characterized by number of users (\(U\)), the code length (\(N\)), code weight (\(W\)), and zero cross-correlation. The basic matrix \(U\times N\) of ZCC code with \(W\)= 1 is given as in [31]:  
\[M\text{=}\begin{bmatrix}0&1\\ 1&0\end{bmatrix} \tag{5}\]  
To obtain ZCC code with \(W\) = 2 from basic matrix \(M\), we perform a transformation as in [32; 33]:  
\[T\text{=}\begin{bmatrix}[1,&W(W-1)]\text{ matrix of zeros }&W\text{ replication of matrix }M\\ &\text{Duplication of matrix }W-1&D\end{bmatrix} \tag{6}\]  
where \(D\) consists of diagonal pattern with alternate column zeros matrix.  
In this study, we use ZCC code with \(W\) = 2. According to \(T\), the matrix of ZCC code with \(W\) = 2 will be:  
\[H\text{=}\begin{bmatrix}0&0&0&1&0&1\\ 0&1&0&0&1&0\\ 1&0&1&0&0&0\end{bmatrix} \tag{7}\]  
The relation between \(N\), \(U\), and \(W\) is given as in [34]:  
\[U=W+1 \tag{8}\]  
\[N=W\times U \tag{9}\]