esa-sceva/satcom-chunk-collection huggingface dataset:
# A Brief Survey of Fixed-Parameter Parallelism
## 7 Parallel Algorithms Using FPT Methods
### Kernelization  
Another common approach for designing \(FPP\) algorithms, introduced in [1], is based on the use of \(FPP\) kernelization. This technique was also used in [2] where \(FPP\) reductions are used to kernelize Vertex Cover instances using the crown reduction technique [34; 35; 36; 37].  
If all steps of the kernelization algorithm take \(FPP\)-time, when the instance becomes a kernel of size bounded by a function of \(k\), any subsequent fixed-parameter algorithm applied on this kernel would run in \(FPP\)-time, resulting in a global \(FPP\)-time algorithm. Since modifying the input instance is \(FPP\)-time, what remains is to ensure that the number of modifications done to kernelize the instance is a function of the parameter \(k\) only. We can even make use of the above color coding techniques to find a particular structure in the input instance.  
The simplest Vertex Cover kernel, for example, is obtained by removing all vertices of degree more than \(k\)[16]. Each such operation takes \(FPP\)-time (refer to the input representation discussion above), and these will iterate for at most \(k\) times, after which the solution size would exceed \(k\) (obviously resulting in a NO instance). All isolated vertices can be removed simultaneously. Overall, the kernelization takes \(FPP\)-time and either determines the instance to be a NO instance, or results in a graph of order \(O(k^{2})\). Whatever algorithm applied to this new instance will therefore take \(FPP\)-time. In fact, even a brute force algorithm would solve the problem in \(O(2^{k^{2}})\) time. Moreover, the verification of Vertex Cover (which has to consider the original input) takes \(FPP\)-time as it was shown earlier.  
We note that fast, and more effective, \(FPP\) kernelization algorithms exist, as the one in [2], where maximal matching is used to perform crown reduction and obtain a \(3k\) kernel for an arbitrary Vertex Cover instance. It is also shown that maximum matching parameterized by an upper bound on the matching size is \(FPP\) and can be performed in \(O(k*log^{3}n)\) using \(O(m)\) processors for a graph with \(n\) vertices and \(m\) edges. This allows for a crown reduction that produces a \(3k\) kernel, which leads to better results when a regular sequential algorithm is applied later.
## 3 Results and Discussion
### In Silico Analysis of the Relevant Positions
rugosa_ lipase 1, the substrate specificity was changed by directly blocking the substrate channel where the residues P246, L304, and L410 were identified to be the key residues. An exchange with bigger hydrophobic residues increased the steric hindrance for long-chain substrates. However, the corresponding residues in the PCI_Lip model are far away from the active site [16]. Unbiased, all eight positions (F91, F129, S163, I245, L300, L302, L305, and I529) were swapped in silico with all 19 other amino acids, and all six substrates were docked to the mutants (Supplementary Tables S2-S49), and their binding to a total of 152 mutants was analyzed to identify crucial residues for tailoring the size and shape of the binding pocket. For the mutants F91L, L302G, and L305A, the docking of pNP-esters with different chain lengths is exemplified in Supplementary Figures S6-S11. Considering the criteria that more suitable binding modes of small- to medium-chain length substrates should be found and the binding affinity of those variants should be increased for those substrates, compared to long-chain pNPP, 35 mutants were predicted to have a higher selectivity for short- to medium-chain fatty acids (Table 1). A similar approach was followed for the lipase from _Geobacillus thermoleevorans_, where the mutants were ranked and analyzed by their calculated binding affinity [34]. With the used workflow, the laboratory screening effort for the PCI_Lip mutants was thus reduced to about 23% compared to an approach where all eight positions were fully saturated. Through this smart but small library, a more effective analysis can be performed. A similar approach was used for increasing the enantioselectivity of a bacterial esterase towards tetrahydrofuran-3-yl acetate [35].
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 3 Methodology
### Application of Machine Learning Models for Spoofing Attack Detection
#### 3.4.2 k-Nearest Neighbors (KNN)  
The k-nearest neighbors method is an unsupervised machine learning method based on the idea that data patterns belonging to the same class often tend to be spatially close to each other. This technique is used for classification and regression, where the decision on the prediction depends on the k-nearest neighbors, that is, the data points that are closest to the observed sample according to some distance metric, such as the Euclidean distance. The classification is based on the majority vote of the adjacent tags, whereas regression is performed by calculating the average value of the neighbors. The KNN method is computationally simple because it does not require the creation of an explicit model during training, but all data are stored in memory. This is the so-called lazy learning method, which means that the calculations take place only in the prediction phase.  
Despite these challenges, the KNN method is frequently used in practice due to its simplicity and intuitiveness [41; 42].  
The KNN algorithm consists of several steps [43]:  
1. Select \(k\) that defines how many neighbors will be checked to determine the class of a particular data point. For example, if \(k=1\), the instance is assigned to the same class as its nearest neighbor.
2. Determine the distance metric. In order to determine which data points are closest to the point for which the class is to be determined, the distance between the observed point and the other data points should be calculated. These distance metrics help form decision boundaries, which divide the question points into different regions. Given the new data point, the algorithm calculates the distance between the new point and all points in the training dataset to find the nearest neighbors of the new data point.
3. Based on the calculated distance, k-nearest neighbors are selected. The neighbors are the \(k\) training points that are closest to the new data point. In the case of binary classification, the decision boundary is the line that separates the two classes. On the other hand, in the case of a multi-class classification, the boundary is the hyperplane that separates the different classes.
4. Assign a class to a new data point. After finding the k-nearest neighbors, the algorithm assigns a new data point to the class that is most common among its k-closest neighbors. In other words, the algorithm uses the majority class k-nearest neighbors as the predicted class for the new data point.
5. The algorithm repeats the previous steps for each new data point.
# Predicting Location of Tweets Using Machine Learning Approaches
## 4 Methodology
### Pairwise Distance Matrix  
Geo-location could mean detecting up to a block, a district, a city, or even a state. The distance between the actual location of the tweet and predicted location can be misleading. For instance, Abha is a city in the southern region neighboring another city of AlKhamis, with an approximately 5-mile distance. The accent and the lifestyle is exactly the same in both cities. However, Twitter geo-tagging distinguishes between them. Thus, there is no clear ground truth when evaluating the model. If we evaluated against the actual exact location, we would end up with very low performance even if the distance is 1 mile away. Therefore, we created a pairwise distance matrix \(\mathbf{D}\) between cities from the class label for evaluation purposes. \(\mathbf{D}\in\mathbb{R}^{m\times m}\) is a square symmetric with a zero diagonal matrix, where \(m\) is the total number classes (illustrated in Figure 11).  
The distance matrix was employed in the evaluation process by calculating the distance between the predicted and the actual targets. For example, if the model predicted the location of a tweet in city \(a\), while it is in city \(b\), the distance between \(a\) and \(b\) was retrieved from the distance matrix. Then, we set a distance threshold \(l\) for the prediction to be true if it is less than the threshold. In other words, this can be interpreted as creating a circle around each target with the threshold being the radius. Any tweet predicted to be in the circle can be assigned to the same class.  
\[\mathbf{D}=\begin{bmatrix}d_{11}&d_{12}&...&d_{1m}\\ d_{21}&d_{22}&...&d_{2m}\\...&...&...&...\\ d_{m1}&d_{m2}&...&d_{mm}\end{bmatrix}\]
## 4 Discussion
### Uncertainty Expressions and ISO 19157  
Uncertainty concepts/expressions/measures as presented in ISO 19157 [25] are not generally applicable to the point Big Data measured. ISO 19157, as a common standardization framework for ISO,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Value** & **Predni Prostřř n n [\%]** & **Pivováraka [\%]** \\ \hline less than 0.10 & 36.2 & 16.8 \\
0.10–0.20 & 30.8 & 27.1 \\
0.21–0.35 & 21.1 & 29.2 \\
0.36–0.5 & 7.8 & 13.4 \\ more than 0.5 & 4.1 & 13.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representation of the percentage of uncertainty values per field.  
Figure 11: Detail of a ’supernormal strip of fertile land’ in a form of a valley where relative yield reaches up to 180% of the mean yield for the whole plot.  
OGC, and W3C, relies on a comparison with a dataset with a higher quality of the measure in question (e.g., positional accuracy, thematic classification correctness, the accuracy of time measurement). A dataset with higher quality with respect to yield in the given time and space was not available for our study. None of the following ways could be implemented: another Big Data measurement with more precise yield values or (as foreseen by ISO 19,157 [25]) its subset with more detailed sample field measurements. For that reason, the concept of uncertainty expression was developed by the authors in this paper.  
Transferability, scalability, and extensibility of the uncertainty expressions developed to other applications are some of the advantages of this approach. The uncertainty expression developed is a proof-of-concept that was successfully tested on the point Big Data obtained from yield measurements on two plots at a fully operational Czech farm. Uncertainty expressions, as well as their cartographic visualization, were verified using a total of 32,625 points displayed to a user at the same time. The presented uncertainty expression could be further developed:  
1. If an aspect(s) of uncertainty other than the one used in this study is to be presented (a sudden change of measured yield).
2. If the objective is to express, relativize, and combine partial uncertainties.
3. If the objective is to present point Big Data of a different nature than the data measured by field harvesters.
## 5 Conclusions  
The uplift capacity behavior of the strip anchor plates in reinforced and unreinforced sands was investigated using centrifuge model tests and a nonlinear finite element program. The main results of this study are summarized below.  
* The uplift capacity of the anchors in the unreinforced soil is significantly affected by the embedment ratio and soil density. The uplift response tends to be brittle, particularly for anchor plates embedded in dense sand that show a significant post-peak reduction in uplift capacity.
* The uplift capacity is significantly improved when geogrid is used. In this study, up to 98% increases in uplift capacity were obtained with reinforcement. The use of the geogrid with an inclination angle of 45 degrees as the reinforcement configuration gave more effective results than the use of the geogrid horizontally and vertically.
* Finite element analyses were performed with hyperbolic soil models using parameters obtained from drained triaxial tests. The results obtained using the finite element method were found to be close to the results of centrifugal model tests. It was observed that the two parameters that significantly affect the uplift capacity of the strip anchor plates embedded in the unreinforced sand are the embedment ratio and the unit weight of the soil.
* The fact that the study was carried out with the centrifuge technique is thought to contribute to obtaining results closer to the actual uplift capacity values of the anchor plates. In addition, the study provides preliminary evidence that the uplift capacity of the anchor plates can be further improved by placing the geogrid layers at different angles.
* The experimental results presented in this study are not specific to a known field subsoil and are likely to be affected by the boundary conditions applied in the experimental study. However, it is thought that the test results are significant and determine the potential advantage of geogrid reinforcement of the sand on the uplift behavior of the anchor plates.  
Figure 15: Comparison of breakout factor with numerical and experimental results in reinforced dense sand.  
**Author Contributions:** Conceptualization, project administration, supervision: E.A.D.; methodology, data curation, software, validation, and formal analysis: S.B., M.L. and K.I.; writing--review and editing and writing--original draft: M.S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Data Preparation and Generation Module
#### 4.2.2 Share Generation of Boolean Secret Sharing  
Bloom filters enable fast queries while preserving privacy but are vulnerable to frequency attacks. To mitigate this, participants split each bit into Boolean secret shares and generate masks via smart contracts, distributing them to different computing nodes to ensure no single node accesses complete data, enhancing distributed privacy protection [25].  
For the \(j\)-th bit \(a_{ij}\in\{0,1\}\) of Alice's Bloom filter \(BF_{A,i}\), Boolean secret sharing generates \(t\) shares \(s_{ij,1},s_{ij,2},\ldots,s_{ij,t}\in\{0,1\}\), satisfying  
\[a_{ij}=s_{ij,1}\oplus s_{ij,2}\oplus\cdots\oplus s_{ij,t} \tag{1}\]  
Similarly, the \(j\)-th bit \(b_{ij}\) of [PERSON]'s \(BF_{B,i}\) follows the same condition. Here, \(\oplus\) denotes the XOR operation, which ensures that all \(t\) shares are required for reconstruction, with fewer than \(t\) shares revealing no information about the original value, and randomness obscures frequency patterns.  
The generation steps, using [PERSON]'s \(a_{ij}\) and [PERSON]'s \(b_{ij}\) as examples, are as follows:  
1. Determine \(t\) shares (a system parameter, e.g., \(t=3\)), randomly generating the first \(t-1\) shares and adjusting the last to satisfy the XOR condition.
2. For \(k=1\) to \(t-1\), randomly generate uniformly distributed \(s_{ij,k}\), introducing randomness to conceal frequency patterns.
3. For [PERSON], set \(s_{ij,t}=a_{ij}\oplus s_{ij,1}\oplus s_{ij,2}\oplus\cdots\oplus s_{ij,t-1}\) (same for [PERSON]), ensuring the XOR result is correct.
4. The generated shares form shares\(s_{A,ij}=\left\{s_{ij,1},s_{ij,2},\ldots,s_{ij,t}\right\}\) and shares\({}_{B,ij}\), which are uploaded to IPFS, with CIDs stored on the blockchain.
# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 4 Results and Analysis
### Simulation  
The UWB positioning simulation platform include four base stations and one label, which was designed according to the UWB positioning principle. The local position frame, in which the positioning coordinates of the four base stations were BS1 (1 m,1 m), BS2 (14 m, 2 m), BS3 (16 m, 19 m) and BS4 (2 m, 21 m), respectively, was established. In terms of setting the ranging-error parameters, the distance measureing error from the base station to the label was \(\pm 15\) cm. In the simulation process, labels moved uniformly in a straight line within the area enclosed in the four base stations. The starting position of the label was (1 m, 10 m), and the whole process lasted for 500 s. Details are shown in Figure 2. In order to demonstrate the effectiveness of the MCUKF method, the least squares method (LSM) was introduced to compare with the CKF.  
Based on the simulation conditions above, the error curve of the positioning results was drawn, as shown in Figure 3. The LSM is marked with a black dashed line, the UKF method is marked with a blue line and the MCUKF method is marked with a red line. As can be seen from the figure, the MCUKF method proposed in this paper is superior to the LSM and the UKF method in positioning errors in both the X direction and the Y direction.  
In order to comprehensively compare the positioning accuracy of the three methods, the horizontal position error curve, shown in Figure 4, was drawn. This figure shows that the horizontal positioning accuracy of the MCUKF method was better than that of the LSM and the UKF method.  
Figure 3: X\(-\) and Y\(-\)direction position-error simulation curve based on MCUKF method.  
Figure 2: Simulation of carrier-movement track and base-station distribution.  
The results in Figures 3 and 4 gave the statistical table of the root mean square error shown in Table 1. This table shows that the MCUKF method is superior to the LSM and the UKF method no matter the root mean square error of the X or Y direction or the root mean square error of the horizontal position. Compared with the LSM, the root mean square error results of the X direction, the Y direction and the horizontal position found with the MCUKF method decreased by 54.50%, 52.52% and 53.78%, respectively. Compared with the UKF method, the MCUKF method values were reduced by 28.89%, 31.76% and 31.34%, respectively. Therefore, the proposed MCUKF method has better positioning accuracy.
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 3 Results
### Influencing Factors of SOC Storage Change  
When primary forests are converted to farmland, soil organic carbon declines rapidly. Different climatic zones have different degrees of impact on SOC loss. SOC storage in tropical areas decreased by 6.70 Mg/ha, and the ratio decreased by 23.6% (\(p<0.05\)). However, SOC storage in temperate regions decreased by 12.09 Mg/ha, and the ratio decreased by 30.6% (\(p<0.05\)), that is, the variation of SOC storage in tropical regions was less than that in temperate regions.  
The conversion of primary forest to farmland caused significant changes in SOC storage in soils at different depths. The differentiation degree of SOC storage in the surface layer (0-20 cm) was the highest, followed by the middle layer (20-30 cm), and the bottom layer (above 30 cm) was the lowest.
## 4 Simulation of the Response of the Used Pile under New Pile Penetration  
### Model Setting  
In this section, a 3D numerical model of the existing pile under new pile penetration is employed, as shown in Figure 6. The soil is 30 m high with a radius of 10 m. The new pile has a diameter of 0.5 m and a length of 20 m. The new pile penetrates at the center of the soil. The existing pile has a length of 20 m and a diameter of 0.5 m. The existing pile's center is 2 m away from the new pile's penetration position, which is 4 times the diameter of the pile. To ensure computational stability, the model is presented with a small hole with a radius of 0.1 m at the center of the new pile's penetration position, and an auxiliary pipe with a radius of 0.1 m is added at the bottom of the pile end, as shown in Figure 7. Thus, the equivalent radius of the new pile model can be calculated as:  
\[\pi\Big{(}R_{\text{m}}^{2}-r_{\text{t}}^{2}\Big{)}=\pi(D_{0}/2)^{2} \tag{45}\]  
where \(R_{\text{m}}\) and \(r_{\text{t}}\) are the radii of the new pile and auxiliary pipe, respectively; \(D_{0}\) is the theoretical radius of the new pile. Therefore, \(R_{\text{m}}\) can be written as:  
\[R_{\text{m}}=\sqrt{(D_{0}/2)^{2}+r_{\text{t}}^{2}} \tag{46}\]  
To verify the validity of the theoretical result, the pile, pipe and soil are simulated by a linear elastic model, which can be described by [PERSON]'s modulus and Poisson's ratio. The material parameters of the soil and pile are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Young’s Modulus, \(E\) (MPa)** & **Poisson’s Ratio, \(\
u\)** \\ \hline Soil & 6 & 0.3 \\ Existing pile & 20,000 & 0.2 \\ New pile & 20,000 & 0.2 \\ Auxiliary pipe & 20,000 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of soil and pile.  
The proposed numerical model contains two steps: ground stress equilibration and new pile penetration. To eliminate the initial soil deformation, the ground stress equilibration is achieved by the geostatic step, where the soil gravity is applied through body force.  
The second step is to simulate the new pile penetration, and the displacement control method is employed, which means a vertical downward displacement is imposed on the top of the penetrated pile. In this step, the underside boundary is fixed, and the lateral displacement of the side boundary is constrained. Meanwhile, the interface soil boundary is free, and the lateral displacement of the penetrated pile is limited.
# A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics
## 4 Experimental Setup
### Dataset Description  
The proposed service ranking algorithm is evaluated using the Google cluster-usage traces dataset [28], which provides detailed information on resource utilization across a large-scale Google data center. The dataset contains over 700,000 task instances distributed across approximately 12,000 physical machines. The InstanceUsage table from the dataset was primarily utilized, focusing on the following seven key features: start_time, end_time, collection_ID, instance_index, average_usage, maximum_usage, and random_sampled_usage. These features provide essential insights into task-level resource usage, which are leveraged in our ranking algorithm to evaluate service performance.