esa-sceva/satcom-chunk-collection huggingface dataset:
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM  
An overview of our proposed DK-SLAM system is illustrated in Figure 1. In the front-end, we utilize a neural network-based feature extractor to detect and describe keypointsacross multi-scale image pyramids. To enhance generalization, the local feature extractor undergoes Model-Agnostic Meta-Learning (MAML) during training. To ensure a balanced distribution of keypoints across images of varying scales, we implement an averaging distribution strategy. Subsequently, to enhance the tracking and matching performance of the learned visual keypoints, we incorporate low-level information from the image. Initially, we construct local photometric constraints on the pixels surrounding keypoints to estimate the initial relative pose. Furthermore, we estimate the matching range of keypoints in adjacent frames based on this initial relative pose. Once the front-end completes keypoint tracking and pose estimation, keyframes are selected based on a predefined interval time scale. These selected keyframes are imported into the keyframe database, with the most recent keyframe being integrated into the back-end loop closure module. Memory consumption is optimized by converting floating keyframe descriptors into binary hash codes. Lastly, we introduce an online Bag-of-Words (BoW) module to establish a loop closure mechanism, facilitating closed-loop detection and global map optimization.
## 3 Simulated Results and Analysis
### Simulated Radiation Patterns  
The radiation pattern of the proposed antenna in Figure 1 was simulated in terms of the gain (dB) as well as E-plane and H-plane patterns. This simulation was based on the varied gap between parasitic patches '\(a\)' and '\(b\)' whereas other parameters are fixed as shown in Table 1.  
Figure 11 showed seven cases of the measured variation of the gap between parasitic patches '\(a\)' = '\(b\)' = n\(\lambda\) from 0.2\(\lambda\) to 0.3\(\lambda\) versus the degree. It could be seen from Figure 11 that the wide beam and the earth match beam radiation, showing the gap between the parasitic patch array was n\(\lambda\) = 0.3\(\lambda\). Figures 12 and 13 showed the simulated E-plane and H-plane patterns of the proposed antenna in Figure 1 of the configuration parameters in Table 1. It could be seen from Figures 12 and 13 that the radiation pattern of the proposed antenna could produce the half-power beam width of the E-plane and H-plane around 130\({}^{\circ}\) and 80\({}^{\circ}\), respectively.  
Figure 11: Simulated radiation pattern for different values of the gap between the parasitic patches '\(a\) = \(b\) = \(n\lambda\)’.  
Figure 10: Simulated return loss for different values of ‘\(a\)’ and ‘\(b\)’.
# Bioassays with _Allium cepa_ for the Monitoring of Toxicity in the Groundwater of Yucatan, Mexico
## 3 Results and Discussion
### PAHs  
The results of PAHs quantification are presented in Table 4 PCO analysis explained 53.9% of the variation in the first two axes (Figure 7). PCO1 is highly correlated with anthracene, fluoranthene, and benzo(a)pyrene, and PCO2 is highly correlated with naphthalene, acenaphthene, and benzo(a)anthracene. B(k)fluoranthene and B(b)fluoranthene could not be identified individually, so they the sum was considered for analysis. Acenaphthylene and the sum of B(k)fluoranthene and B(b)fluoranthene presented the highest prevalence and concentration. Benzo(b)fluoranthene is primarily found in gasoline exhaust, tobacco and cigarette smoke, coal tar, soot, amino acids, and fatty acid pyrolysis products. Benzo(k)fluoranthene is primarily found in gasoline exhaust, cigarette smoke, coal tar, coal and oil combustion emissions, lubricating oils, used motor oils, and crude oils. Both compounds are considered potential human carcinogens [40]. Previous works reported report benzo(k)fluoranthene as the PAH with the highest concentration in groundwater collected in shallow wells in the city of Merida between October 2007 and March 2008, with an average concentration of 747.0 ng/L and a maximum of 5100.0 ng/L [5], values higher than those found in this research. The Mexican Official Norm NOM-127-SSA-2021 only includes one PAH, benzo(a)pyrene, with a maximum permissible level of 700 ng/L value, a threshold well above the concentrations measured in this study. PERMANOVA indicated no significant differences between sites (\(p\) = 0.1483, pseudo-F = 1.2261), but results were significant between transects (\(p\) = 0.0195, pseudo-F = 1.89).
# Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System
## 5 ANFIS Model for Vibration Prediction Called ANFISBLAST
### Dataset and the Modeling of the ANFISBLAST Prediction System  
The ANFISBLAST prediction system was constructed to be applied to wide ranges of the charge \(Q\) (kg) and distance \(d\) (m) from the point of measurement to the center of gravity of the blast (see Table 6). In order to predict the \(PPV\) and \(FREQUENCY\), the 40 events measured with a triaxial geophone were used to set the dataset for developing the ANFISBLAST prediction system. The same training data were defined for all \(f\), where \(f\in F\), prediction models: \(PPV\) and \(FREQUENCY\).  
In a basic FIS, the integer number of rules is determined by an engineer/researcher who is acquainted with the engineering problem to be modeled. There are no straightforward procedures to determine the lowest possible number of membership functions in order to obtain a required level of performance. In this paper, the number of membership functions determined for each input variable was chosen systematically by analyzing the input-output data and by using the trial-and-error method. For each \(f\in F\), for the \(f\) prediction model, we chose two membership functions in each input. Figure 7 shows the membership functions for \(Q\) and \(d\) for the prediction model \(PPV\). All the integrated membership functions were Gaussian, expressed by Equations (7) and (8). Several researchers reported that Gaussian membership functions performed best among all given membership functions [42; 43].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variable** & \(Q\) **(kg)** & \(d\) **(m)** \\ \hline Min. value & 32 & 32 \\ Max. value & 63 & 108 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Limit values of each input variable.  
Figure 7: Membership functions for the prediction model peak particle velocity (PPV) for input variable \(Q\), and for \(d\).  
Once the numbers of the membership functions related to every input were established, the initial values of the premise parameters were determined in such a way that the membership functions were uniformly distributed along the range of each input variable. For each \(f\in F\), the \(f\) prediction model contained two rules with two membership functions being determined for each input variable. The total quantity of fitting parameters was 10 (four premise and six consequent parameters). The abovementioned parameters were fitted by applying a hybrid algorithm. As an interface for computational modeling, MATLAB [44] was used. The premise parameters are presented in Table 7 and the consequent parameters in Table 8. The same statistical parameters as for the conventional predictors were used to determine the goodness of fit of the ANFISBLAST model for the training data. The statistical parameter values were SSE = 83.05 mm/s, R-squared = 0.73, adjusted R-squared = 0.73, and RMSE = 1.49 mm/s.
# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 3 Principle of SVR Prediction Model
### SVR Prediction Model Algorithm Design of Grain Depot  
In this study, the depot clearing and inspection steps were combined with the SVR prediction model prediction, which was used in place of the empirical formulation to eliminate the interference of human factors in the correction weight setting and the natural loss computation process. The SVR prediction model was constructed as shown in Figure 2.  
Figure 2: Algorithm flow chart of the grain depot SVR prediction model.  
First, the collection of the grain depot inventory data was sorted, the data related to the grain depot inventory were selected as samples, and a normalization process was performed to eliminate the dimensional differences of different metrics and speed up the network learning. Second, the test samples were selected from the total sample summary, the structure of the prediction model was set, the kernel function and related parameters were selected, the SVR prediction model was constructed, the evaluation index of the SVR prediction model was established, and the index was used to judge whether the model met the accuracy requirements. If this requirement was not met, the kernel and associated parameters were replaced until the accuracy requirement was met and the SVR prediction model was formed under the optimal kernel and parameter configuration. The inventory measurements or samples to be tested were then fed into the prediction model, and the predicted amount of grain store inventory was output. Finally, the difference ratio was calculated based on the predicted inventory volume and the inventory book volume to verify that the relevant provisions were satisfied.  
In this study, after choosing the kernel function and considering the actual usage conditions of grain inspection devices, we found the best parameters by choosing a cross-validation method. Since the penalty factor C was chosen to be larger, the number of support vectors obtained was larger and the computational cost was higher. Thus, the computational time was reduced by preferentially choosing parameter combinations with smaller values of the penalty factor C, under the condition that the performance of the prediction model was not different.
## 1 Introduction
In manufacturing environments, milling operations are still often accompanied by flooding with mineral oil-based coolants. Figure 1: External supply coolant penetration (**a**) and efficiency (**b**) issues [7]. Previously, the authors proposed variations of the alternative coolant supply realized through the internal and combined schemes (Figure 3). The novel criteria were the channel geometry inside the cutting insert and the use of a single circuit for simultaneous internal and external coolant feed (combined scheme). In turning tests with SUS304 stainless steel, both schemes achieved a small decrease in the contact point temperature and significantly reduced the heat distribution inside the cutting insert body. This ultimately had a positive effect on tool life, tending to strengthen with an increase in cutting speed. The tests were carried out with 4% oil-in-water emulsion with a flow rate of \(\sim\)0.7 L/min [17]. The above internal and combined schemes can be introduced to milling as coolant supply options to substitute flooding. Additionally, it reduces the aforementioned lag behind turning in the adoption of alternative supply methods. At the same time, the schemes can be used together with alternative coolants. The problem of organizing their supply to the rotating tool can be solved by designing specialized tooling. The tooling should meet the requirement of bypassing the machine tool's standard supply system, as well as take into account the properties of an alternative coolant being utilized. Therefore, this study aims to develop a milling tool that realizes the alternative internal and combined supply schemes. In parallel, a tooling method is designed to allow the use of an alternative coolant and its delivery to bypass the machine tool's standard supply system.
## 2 Methods
### Method of Signal Dimension Reduction  
PCA is a statistical technique that transforms a set of possible correlation variables to a set of linearly uncorrelated variables by orthogonal transformation. The transformed set of variables is the principal component. A set of related features in high-dimensional data is converted to a smaller subset and named as principal component. High-dimensional \(n\) data can be transformed to low-order \(k\) dimension data (\(n>k\)). PCA does this transformation by finding a \(k\) feature vector, and projecting the \(n\) dimension data onto that feature vector to minimize the overall projection error. PCA can preserve around 0.9 variance of the original data set and significantly reduce the number of features as well as the dimensions. The original high-dimensional data set is projected onto a smaller subspace while preserving most of the information contained in the original data set. Assuming \(\{x_{t}\}\), and \(t=1,2,\ldots,N\), the random dimension \(n\) with the mean (\(\mu\)) inputs the data recording its definition as (1)  
\[\mu=\frac{1}{N}{\sum\limits_{t=1}^{N}{x_{t}}} \tag{1}\]  
The definition \(f\) the covariance matrix of \(x_{t}\) is (2):  
\[\mathbf{C}=\frac{1}{N}{\sum\limits_{t=1}^{N}{(x_{t}-\mu)(x_{t}-\mu)^{T}}} \tag{2}\]  
PCA solves the eigenvalues problem of Covariance matrix \(\mathbf{C}\)  
\[\mathbf{C}\mathbf{C}v_{i}=\lambda_{i}v_{i} \tag{3}\]  
In Equation (3), \(\lambda_{i}\) is the eigenvalue and \(v_{i}\) is the corresponding eigenvector.  
To represent the data record with a low-dimensional vector, only \(m\) pieces of eigenvector (named as the principal direction) are needed, corresponding to \(m\) pieces of the largest eigenvalue (\(m<n\)), and the variance of the projection of the input data in the principal direction is greater than the variance in any other direction. Hence parameter \(v\) is the approximate precision of the \(m\) pieces of the largest eigenvector, so the following relationship (4) is obtained  
\[\frac{\sum\limits_{i=1}^{m}{\lambda_{i}}}{\sum\limits_{i=1}^{n}{\lambda_{i}}}\geq v \tag{4}\]  
The purpose of PCA is to maximize internal information and increase calculation speed after dimension reduction, and to evaluate the importance of the direction by the size of the data variance in the projection direction.
## 5 Discussion
### Impacts of Mixed-Pixels  
The aquaculture maps in the Pear River Basin (Guangdong) were obtained with medium resolution Sentinel-1 SAR and Sentinel-2A multispectral images, and the results showed the advantages of using medium resolution images for large-scale thematic mapping. However, the accuracy and generalization of the proposed approach might be affected by mixed pixels of medium resolution images. The aquaculture pixels are often mixed with embankment among ponds, which often results in ambiguous boundaries and some inevitable errors. Thus, some parts of aquaculture ponds were classified as land, and some embankments were classified as ponds. It is difficult to overcome such errors using medium spatial resolution images, and very high spatial resolution images may easily overcome this problem.  
The step of extracting water surface mask in our proposed approach is also affected by mixed pixels. Our method relies on the assumption that pixels covering maritime facilities are still water-dominated, because the sizes of these facilities are usually relatively smaller than a pixel. Thus, they were first classified as waterbodies, and further been detected at later stages. However, this assumption may be not appropriate when using high spatial resolution images, because many pixels covering maritime facilities are no longer water-dominated mixed pixels. In such case, these pixels will be segmented into  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Proposed Method** & **Aquaculture** & **Others** & **Classified** & **User’s Accuracy (\%)** \\ \hline Aquaculture & 0.231 & 0.044 & 0.275 & 84.10 \\ Others & 0.032 & 0.693 & 0.725 & 95.54 \\ Reference & 0.264 & 0.736 & 1 & \\ \hline Producer’s accuracy (\%) & 87.73 & 90.06 & & \\  & & Overall Accuracy = 92.40\%, Kappa = 0.867 & & \\ \hline \end{tabular}
\end{table}
Table 7: Confusion matrix of proposed approach in this study in typical sites.  
non-aquaculture, affecting the accuracy of final map. Object-based methods might be an optimal solution for high spatial resolution images to overcome this limitation.  
The proposed approach was designed for large scale aquaculture mapping using medium spatial resolution images. Although the boundaries are not detected very accurately, the aquaculture map over a large area can be efficiently obtained. In particularly, the medium spatial resolution images acquired by many satellite sensors (such as Landsat TM series and Sentinel-2A series) for several decades provide great convenience to monitor the development of the aquaculture industry.  
Figure 12: Aquaculture maps obtained using three methods. (**a**,**i**) present the Sentinel-2A true color images, and their corresponding reference maps are presented in (**e**,**m**). The results obtained using [PERSON]’s [42], [PERSON]’s [39], and the proposed approach are presented in (**b**–**d**) and (**j**–**l**), respectively. Their comparisons with the ground truth maps are presented in (**f**–**h**) and (**n**–**p**), respectively.
## 3 X-NETAD: The Idea
### The Irresponsible Forwarding (IF) Protocol
#### 3.3.3 IF in IEEE 802.11 Networks  
Since communications are broadcast, correct packet reception cannot be acknowledged. Therefore, the packets are never retransmitted and the Contention Window (CW) of the backoff procedure of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) medium access control (MAC) protocol is constant and equal to the value specified by the parameter, \(\mathrm{CW}_{\mathrm{min}}\) of the IEEE 802.11 standard [31]. Moreover, it is well known that broadcast communications cannot exploit the Ready-To-Send/Clear-To-Send (RTS/CTS) mechanism foreseen by the IEEE 802.11 standard. Hence, they suffer from the hidden terminal problem, which inevitably reduces the number of packets able to propagate to the farthest reachable node of the network. Note that the Distributed Coordination Function (DCF) mechanism of the IEEE 802.11 standard obviously introduces an additional channel access delay, proportional to the traffic load and to the parameter, \(\mathrm{CW}_{\mathrm{min}}\), thus increasing the overall delay. We remark that while the traffic load is ineffective in an ideal (collision-free) scenario, in an IEEE 802.11 network scenario, it will have a relevant impact.  
According to various simulations studies carried out to characterize the performance of the IF protocol in realistic IEEE 802.11 networks [26, 27], our results show that the IF protocol significantly outperforms a simple flooding protocol, in terms of rebroadcast (and energy) savings. The performance improvement brought by the IF protocol is more relevant for high values of the node density and/or of the traffic intensity, since in these conditions, the IF protocol shows a \"self-regulatory\" capability, keeping the transmission delay short and the number of retransmissions small. In the presence of Nakagami fading, IF becomes very attractive, providing good performance at a low computational cost.
# Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization
## 5 Conclusions  
In this study, a novel learning algorithm, named LC-PSO-ELM, was proposed by means of the frame structure of LC-ELM and the parameter optimization strategy of the PSO algorithm. The parameters of input weights, hidden biases, addresses and radiuses were all adjusted by the PSO for searching the optimal solution in the model.  
Based on the function approximation and classification benchmarks problems, the performance of the LC-PSO-ELM utilizing different fuzzy membership functions was conducted. Meanwhile, the generalization performance of the four algorithms of ELM, LC-ELM, PSO-ELM and LC-PSO-ELM were compared, which showed that the proposed algorithm can produce better generalization performance in most cases, compared with the other alternative ELM-based approaches.  
Although the LC-PSO-ELM can obtain a significantly improved generalization performance, the training time of the algorithm was much longer than the others due to the fact that four parameter values should be optimized in the algorithm. In future, it is necessary to propose a parallel training mechanism for the proposed method for improving the efficiency to solve problems with very large datasets. Correspondingly, it is also necessary to exploit the sensitivities of these chosen activation functions in theory in the future.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fuzzy**} & \multicolumn{3}{c}{**Box and Jenkins Gas Fumrace Data**} & \multicolumn{3}{c}{**Diabetes**} \\ \cline{2-9}  & **Training Time (s)** & **Training Error STD** & **Testing STD** & **Number of Hidden Neurons** & **Training Time (s)** & **Training Accuracy STD** & **Testing Accuracy STD** & **Number of Hidden Neurons** \\ \hline \multirow{2}{*}{Gaussian function} & 163.3120 & 0.0175 & 0.0186 & \multirow{2}{*}{15} & 325.6693 & 0.7753 & 0.7637 & \multirow{2}{*}{17} \\  & 1.7935 & 0.0010 & & \(\begin{array}{c}3.4140\\ 10^{-4}\end{array}\times\) & & 5.7551 & 0.0151 & 0.0293 & \\ \hline \multirow{2}{*}{reversed sigmoid function} & 155.2323 & 0.0178 & **0.0182** & \multirow{2}{*}{15} & 299.1179 & 0.7966 & **0.7918** & \multirow{2}{*}{17} \\  & 4.6814 & 0.0017 & **0.0018** & & 0.6804 & 0.0171 & **0.0198** & \\ \hline \multirow{2}{*}{reversed tanh function} & 162.3003 & 0.0242 & 0.0243 & \multirow{2}{*}{15} & 332.4445 & 0.7263 & 0.7133 & \multirow{2}{*}{17} \\  & 4.7447 & 0.0024 & 0.0035 & & 7.1548 & 0.0498 & 0.0668 & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison of different fuzzy membership function in the LC-PSO-ELM algorithms on regression or classification problems.  
**Author Contributions:** Conceptualization, B.L., X.R. and Y.L.; Methodology, H.G. and B.L.; Software, W.L. and F.Q.; Validation, H.G., W.L. and F.Q.; Formal Analysis, X.R. and Y.L.; Writing-Review & Editing, H.G. and B.L.  
**Funding:** This work was funded by the National Natural Science Foundation of China (No. 61773226) and Key Research and Development Program of Shandong Province (No. 2018 GGX103054).  
**Conflicts of Interest:** The authors declare no conflict of interest.