esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of GNSS Galileo Contribution to the Modernization of CROPOS's Services
## 4 Discussion
### Analysis and Discussion of the VPPS Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution** & **Range (_E_)** & **Range (N)** & **Range (_H_)** \\ \hline VPPS (GGG) & 9.5 mm & 7.6 mm & 17.1 mm \\ \hline VPPS (GAL-only) & 8.1 mm & 11.4 mm & 21.7 mm \\ \hline GPPS (GGGB) & 5.6 mm & 5.4 mm & 5.3 mm \\ \hline GPPS (GAL-only) & 8.7 mm & 7.9 mm & 19.4 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Range values of solutions obtained with VPPS and GPPS.
# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 3 Algorithm Design
### ICP Fine Registration Based on KD Tree  
The iterative closest point algorithm is the most classic point clouds registration algorithm, but it is easy to fall into the local optimal solution. Therefore, it is necessary to provide a good initial position through coarse registration to increase the probability of iterative convergence to the global optimal position. ICP fine registration based on KD tree includes the following steps:  
(1) Perform RANSAC coarse registration on the source point clouds \(P\) to obtain the optimal transformation matrix \(\hat{T}_{0}\), and perform \(\hat{T}_{0}\) transform on the point clouds \(P\) to obtain the point clouds \(P^{\prime\prime}\). For each point \(p_{i}^{\prime\prime}\) in the point clouds \(P^{\prime\prime}\), use the KD tree algorithm to search for the point \(q_{i}\) closest to the point \(p_{i}^{\prime\prime}\) in the target point clouds \(Q\), and form a corresponding point pair (\(p_{i}^{\prime\prime}\), \(q_{i}\)). This makes up a total of \(N\) pairs of points;  
(2) According to the point pair relationship, the rotation matrix \(R\) and the translation matrix \(T\) are calculated by the least square method, and the error function \(G(R,T)\) is obtained. The calculation formula is shown in Formula (9):  
\[G(R,T)=\frac{1}{N}{\sum_{i=1}^{N}{\|R_{p_{i}^{\prime\prime}}+T-q_{i}\|}}^{2} \tag{9}\]  
(3) Transform each point \(p_{i}^{\prime\prime}\) in the point clouds \(P\) with the obtained rotation matrix \(R\) and translation matrix \(T\) to obtain the transformed corresponding point \(p^{\prime\prime}\), \(p_{i}^{\prime\prime}=R_{p_{i}^{\prime\prime}}+T\), and calculate the average distance \(D\) of the point pair (\(p^{\prime\prime}\), \(q_{i}\)), as shown in Formula (10):  
\[D=\frac{1}{N}{\sum_{i=1}^{N}{\|p_{i}^{\prime\prime}-q_{i}\|}} \tag{10}\]  
(4) Set the maximum distance threshold \(\varepsilon_{ICP}\) between corresponding points. If \(D_{m}-D_{m+1}\leq\varepsilon_{ICP}\) is satisfied, the iteration ends. \(D_{m}\) is the average distance of the \(m\)th iteration, and \(D_{m+1}\) is the average distance of the \(m\)+1 th iteration. Otherwise, return to the first step to change the optimal transformation matrix and continue the iteration, at this time the initial transformation matrix is \(T_{0}\times R\times T\).  
(5) According to the final obtained rotation matrix \(R\) and translation matrix \(T\), the source point clouds \(P\) is transformed into the coordinate system of the target point clouds \(Q\) to complete the registration.
# Deep Learning Applications in Geosciences: Insights into Technological Analysis
## 2 Materials and Methods
### Network Architecture
First, we trained a DCNN to differentiate between bioturbated and unbidurbated facies. Next, we extended this task to a 3-class classification problem that sought to model map the input images to one of the three classes of unbidurbated (BI 0), moderately bioturbated (BI 1-2), or intensely bioturbated (BI 3-6) facies. For both experiments, we defined our models (Figure 2) based on the VGG-16 architecture [4], which was a DCNN containing 16 layers composed of 3 \(\times\) 3 convolution filters. The input of the network was a 224 \(\times\) 224-pixel image with 3 color channels (red, green, and blue). From the given inputs, their low-level features (lines, edges, or dots) were learned by the convolution filters in the shallow parts of the network. In the deeper parts, high-level features such as objects were more detectable. This is the reason why feature extraction is more problem-specific in the deeper levels of the network. Therefore, we kept the parameters fixed for the first four blocks from the training of the VGG-16 for ImageNet dataset, and only allowed the weights to be trained for the last block and the classifier parts of our models. Thus, using transfer learning significantly reduced the computation time needed to train the model, and reduced the need for a large number of training examples, as only the last block and the fully connected layers had trainable parameters. It is worth noting that through tests involving popular DCNN architectures such as VGG-16, VGG-19, and custom DCNN models, we found VGG-16 to yield the best classification performance. Moreover, starting with pre-trained weights for the ImageNet dataset, we tested different combinations of frozen and trainable blocks. We found that freezing the first four blocks of VGG-16, while allowing the last block and the fully connected layers to be trainable, resulted in efficient training and improved classification accuracy compared to other configurations. Figure 1: A representative core image showing various bioturbation indices expressed by bioturbation index (BI) in different facies. Moreover, to expand the existing labeled data and to insert regularizing effects into the classification problems, we also used data augmentation during the training stage. Data augmentation is a commonly used technique in model training, wherein new samples are artificially created from the training data by applying some basic operations (rotation, cropping, flipping, scaling, shifting, etc.). This allows a network to detect complex internal features more easily when training data are limited. In order to not distort the particular characteristics of the images, we carefully chose the augmentation techniques and allowed the algorithm to create new samples randomly, applying horizontal flip and 10% shifts in the width and height to the training data.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### KNN Model
#### 3.2.4 Feature Extraction  
Once the images have been segmented, feature extraction provides the KNN model with the most relevant features. We applied the Canny edge detection technique to the grayscale version of the segmented images to achieve this. Subsequently, the skin color mask is applied to the edge image, further refining the features by focusing on the regions of interest. The HOG features are then computed for the masked edge image, generating a feature vector that effectively captures the essential shape and characteristics of the images. L2 normalization is employed to ensure that the HOG features have the same scale and can be effectively compared across images. Illustrated are Steps 4 and 5 in Figure 6.
## 2 Theoretical Background  
### Imputation Methods  
Researchers classified missing data into three categories [5,17]: (i) missing at random (MAR), (ii) missing completely at random (MCAR), and (iii) missing not at random (MNAR). In the first category, MAR, the missingness is relevant to other characteristics rather than the missing characteristics. In the second category, MCAR, the missing is completely random and not related to any characteristics. Therefore, the probability of missing data is the same for all observations. In the third category, MNAR, the missing is relevant to the missing characteristics and not random.  
Data imputation is part of the dataset preprocessing step that is typically performed on structured and unstructured machine learning datasets before model training. The overall goal of the preprocessing step is to facilitate training, reduce model complexity, and improve model generalization [18,19]. The following two subsections provide an overview of imputation methods and commonly used similarity metrics.  
Several methods have been developed for the imputation of missing values, and the techniques are of different complexity and approach. These imputation strategies range from simple to advanced. The type of data, the extent of missing values, and assumptions about the nature of the missing data will influence the choice of an appropriate imputation technique. This subsection discusses some of the most common imputation methods. It categorizes them into single and multiple imputation approaches, highlighting their principles, advantages, and disadvantages.
## 3 Results and Discussion
### Cross-Reactivity and Competition for the Binding Sites  
MIPs for a specific compound uses to be able to recognize other related analytes. Accordingly, the polymers could be used to simultaneously extract CS and PMB since their chemical structures are highly similar with only one different amino acid, which has been identified as D-Leucine for CS and D-Phenylalanine for PMB [33]. Cross-reactivity was estimated by analyzing the retention of CS and PMB both in MIP and NIP. Accordingly, the independent extraction of CS and PMB under optimum conditions was studied by percolating 1 mL of standard solutions at six different concentration levels (1, 2.5, 5, 10, 20 and 50 \(\mu\)g mL\({}^{-1}\)) through both MIP and NIP cartridges. As shown in Figure 4, the amount of CS retained by MIP increases linearly reaching a plateau for concentrations higher than 20 \(\mu\)g mL\({}^{-1}\), which correspond roughly to a maximum capacity of about 120 \(\mu\)g of CS per g of MIP, whereas it drops down to about 80 \(\mu\)g of CS per g of NIP, proving once more the presence of selective binding sites in MIP. In contrast, the amounts of PMB in MIP and NIP increased linearly within the whole range of concentration  
Figure 3: Effects of (**A**) different percentages of MeOH in water (%, _v/v_) as washing solutions and (**B**) methanol (MeOH), 1% ammonia (AM) in methanol and different concentrations of formic acid (FA) in methanol (%, _v/v_) as eluting solutions on the recovery of colistin.  
level studied. Apparently, the adsorption ability of polymers to PMB was higher than that observed for CS, suggesting that peptides skeletal structure have a remarkable effect on recognition capabilities of the polymers under study. However, since the adsorption curve of PMB on MIP had the same trend as that obtained on NIP, it might suggest that PMB retention is mainly governed by non-specific interactions. Nevertheless, recoveries of PMB on MIP were higher than those obtained on NIP, and thus PMB is able to reach specific binding sites as well. Therefore, it can be concluded that imprinted cavities in MIPs would match both CS and PMB, offering the possibility of simultaneous determination.  
Due to the limited number of binding sites in MIP, it is essential to assess whether CS or PMB showed higher binding affinity and also if they could compete each other for them. Accordingly, the competition between CS and PMB for the binding sites was investigated. Mixed standard solutions of CS and PMB at different concentrations (1-50 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 3 UAV-BS Deployment Process (UDP)  
The UDP scheme targets to maximize the number of offloaded UEs from the MBSs to the UAV-BSs and it consists of two subprocesses. The first subprocess is named as user clustering process (UCP) and partitions the \(N\) UEs into \(K\) subsets. Through UCP, the maximum number of required UAV-BSs, as well as their corresponding positions in the 2D plane, are derived. Subsequently, the second subprocess is called the clustering  
Figure 1: The configuration of the considered 5G two-tier heterogeneous cloud radio access network (H-CRAN): (**a**) Operating under normal traffic load and before the placement of UAV-BSs. (**b**) A heavily loaded scenario caused by an unexpected event and network offloading via the deployment of UAV-BSs.  
selection process (CSP), which determines the \(D\leq K\) subsets to be offloaded from the \(M\) MBSs into the \(D\) available UAV-BSs.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
###### Abstract  
Spoofing detection is critical for GNSS security. To address the issues of low detection rates and insufficient coverage in traditional methods, this study proposes an eye diagram detection method based on the multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP). Unlike conventional correlation peak distortion detection techniques, the proposed method uses the MSC-MMP algorithm to perform multiscale edge extraction from the eye diagram generated from the receiver's correlation values. It then calculates the image threshold using minimum misjudgment probability to ensure the accuracy of the eye diagram's edges. This enables the accurate detection of subtle changes in the eye diagram, leading to the better identification of spoofing signals. The results show that the MSC-MMP outperforms traditional edge extraction algorithms by over 0.072 in terms of the optimal dataset scale F score (ODS-F). Compared to signal quality monitoring (SQM) and Carrier-to-Noise Ratio methods, the EDDM-MSC-MMP method increases spoofing detection coverage by over 60%, achieving the highest detection rate in the TEXBAT dataset. Overall, the EDDM-MSC-MMP method improves the reliability and coverage of spoofing detection, providing an effective solution for GNSS spoofing detection.  
Keywords:GNSS spoofing detection; eye diagram detection method; multiscale Canny algorithm; minimum misjudgment probability
## 5 Simulations and Performance Evaluations
### Different Service Rates of MEC-NBS  
As shown in Figure 7, we measured the total time consumption by the system in completing all tasks under the 3 schemes with different service rates of MEC-NBS. In simulations, we increase \(\phi_{k}\) from 2.2 \(\times\)\(10^{6}\) to 2.7 \(\times\)\(10^{6}\) MIPS, while other settings are listed in Table 1, except fixed values \(M=60\), \(R=6\), \(K=6\), \(s_{k}^{\text{C2 NBS}}=80\) MB/s, \(s_{k}^{\text{NBSEC}}=30\) MB/s, \(s_{k}^{\text{B5 NBS}}=s_{k}^{\text{NBS2 BS}}=30\) MB/s, \(\forall k\in\mathcal{K}\).  
Figure 6: \(f\) gained by the 3 schemes with the increase of \(K\).  
\(\phi_{k}\) represents the computation capability of MEC-NBS. Similar to Figure 6, when \(\phi_{k}\) is increased, total utilization time of the system is decreased under OMEC and OCMEC strategies. The line of NMEC is straight since \(\phi_{k}\) has no effect on it. The total time consumed by OMEC and OCMEC is always lower than NMEC, OCMEC is always lower than OMEC. So, computing offloading and data caching can reduce the system delay.  
With the increase of \(\phi_{k}\), the system can accept more tasks and provide better performance through computing offloading and data caching, the total time-consuming gap between OCMEC and NMEC is increasing, and the gain effect of reducing the delay is getting better and better. When \(\phi_{k}\) increases to a certain threshold, the optimization effect for time consumption slows down gradually.  
It is worth noting that the gain of \(\phi_{k}\) to total time optimization is far less than \(K\).  
Therefore, MEC-NBS can effectively reduce the system delay, in which the combination of data caching and computational offloading than one separate computational offloading brings more delay benefits.
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
## 5 Conclusions  
Both the location and the routing problems are investigated simultaneously in this study, i.e., locations depot and assignment of customers with consideration of time windows, depot capacity, and vehicle capacity. This study considers the LRPTW problem as the extension of the LRP problem. We developed a new hybrid method to solve CLRP and LRPTW. Our approach utilizes greedy heuristic to assign open depots and customer selection using a variable neighborhood search for route improvement and path relinking as the intensification method.  
An algorithm named variable neighborhood search with path relinking (VNSPR) can effectively solve large-scale instances of CLRP and LRPTW problems. In order to validate the algorithm's performance, the algorithm was applied to three well-established LRP instances. Furthermore, 85 generated instances of LRPTW were tested. Computational results show that the proposed method can solve NP-hard LRPTW problems with good solutions. The results also demonstrate the efficacy and efficiency of the proposed algorithm, as they indicate that results produced are approximately the same as the best-known results. Moreover, VNSPR obtains promising results on almost all of the instances that were tested, which demonstrates the algorithm's robustness.  
The proposed VNSPR can be embedded into any metaheuristic approach, such as simulated annealing, to tackle the deterministic phase within the VNS in order to obtain a better solution. It has been observed that LRPTW instances with narrow time windows are harder to solve due to its difficulty in finding a feasible solution. We can utilize time window heuristics as the initial solution to tackle this problem.  
Conceptualization, M.F.N.M. and V.F.Y.; methodology, M.F.N.M. and V.F.Y.; software, M.F.N.M. and A.A.N.P.R.; validation, M.F.N.M., A.A.N.P.R. and V.F.Y.; formal analysis, M.F.N.M.; investigation M.F.N.M., A.A.N.P.R. and V.F.Y.; resources, M.F.N.M., A.A.N.P.R. and V.F.Y.; data curation, M.F.N.M., A.A.N.P.R. and V.F.Y.; writing--original draft preparation, M.F.N.M., writing--review and editing, V.F.Y.; visualization, M.F.N.M., A.A.N.P.R. and V.F.Y.; supervision, V.F.Y. and B.N.A.; project administration, A.A.N.P.R.; funding acquisition, B.N.A. All authors have read and agreed to the published version of the manuscript. Funding is provided by LPPM Institut Teknologi Kalimantan research grant 2022. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments: The research of the second author is partially supported by the National Science and Technology Council, Taiwan, under Grant MOST 111-2410-H-011-020-MY3 and the Center for Cyber-Physical System Innovation from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan. Conflicts of Interest: The authors declare no conflict of interest.
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 6 Fault Detection System
### Creation of Classification Models by Learning  
The fault detection system uses a combination of One-Class SVM and ChangeFinder to perform fault detection. In this section, we explain how to construct a classification model for One-Class SVM, which classifies observed data as normal or faulty. One-Class SVM can classify normal classes and abnormal classes by using an abnormality classification model learned using only previously acquired normal data. This makes it effective for plants for which faulty data are difficult to obtain or for which the faulty pattern is unknown. Therefore, training data that have the same dimensions as the data to be input during classification are required during training. The discriminant model is constructed by training the One-Class SVM with previously observed training data. An overview of the process is shown in Figure 9.  
As shown in Figure 9, the training data include the input \(\mathbf{u_{d}}\) of the heat absorption of the Peltier device, the measured temperature of the tube \(\mathbf{y_{f}}\), and the change-point score of \(\mathbf{y_{f}}\) calculated by ChangeFinder. Since the thermal model of the microreactor is a dynamic system, past values affect the current values. In a dynamic system, if a classification model is built using only the current information, the plant characteristics will not be learned correctly and there is a risk of false positives. Therefore, by including time-shifted past data in the learning vector, it is possible to construct the classification model that takes the dynamic system into account. The variable to be trained is as in Equation (43). Note that CF(\(\mathbf{x}\)) denotes the change-point score of time-series data \(\mathbf{x}\) by ChangeFinder.
## 6 Conclusions  
This study proposed a monitored strain-based statistic inverse analysis method for the estimation of stress in different stages of PC beam bridge construction. Uncertainty of the models and several influential parameters were considered, and posterior information of stress was calculated. Some conclusions can be obtained, as follows:  
1. Strain of concrete contains stress-dependent strain (such as elastic strain and creep strain) and stress-independent strain (such as shrinkage strain and temperature strain). Stress could not be directly calculated by the monitored strain.
2. Through a case study, it was found that the inverse analysis method could be used to effectively estimate concrete stress history based on the monitored strain. Error from the monitoring sensor has an effect on the estimation, resulting in a larger standard deviation of posterior information. As much monitored data as possible should be used to eliminate the influence of error data.
3. It can be concluded from the result of the case study that with the increase in the concrete's age, the weight proportion of the concrete creep strain in the total strain increased, reaching up to 71%.
4. Due to the boundary constraint of the monitored information, the uncertainty of the posterior information was smaller to that of prior information. However, the error in monitored data have a great effect on the posterior information. Efforts should be made to eliminate the influence of error on result, and determine the rational requirement of the monitored data to obtain a sufficiently accurate estimation.  
Conceptualization and methodology, B.H. and H.X.; software, W.Y.; validation, P.J.; writing, H.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (grant number: 2019 RC012).  
Not applicable.  
The authors declare no conflict of interest.