esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation, Performance Evaluation, and Discussion
### Data Sets  
The characteristics of data samples, such as dimensionality, data distribution, and noise, significantly influence the performance of clustering algorithms. Given this, 20 datasets from different domains with various characteristics were used to investigate the proposed algorithm's efficiency. Specifically, among the datasets are 12 synthetically generated high-dimensional datasets, including three A-datasets (A1, A2, and A3), three Birch datasets (Birch1, Birch 2, and Birch 3), two DIM datasets (DIM002 and DIM1024) and four S-datasets (S1, S2, S3, and S4). The A-datasets and the Birch datasets are two-dimensional, with the A-datasets having varying clusters: 20, 35, and 50, respectively, while the Birch datasets have a uniform number of clusters which is 100, each with varying structures. The DIM 002 dataset has 15 dimensions with clusters, while the DIM1024 has 1024 dimensions with 16 clusters characterized by well-separated clusters. The S-datasets are spatial-distribution-based two-dimensional datasets with 15 clusters of varying complexity. Additionally, included among the datasets are two high-dimensional UCI datasets: Letter with 16 dimensions and 26 clusters, and Yeast with 8 dimensions and 10 clusters; two high-dimensional RGB images, Housec5 and Housec8, have 3 dimensions and 256 clusters, each having 34,112 data points. A two-dimensional shape set, D31, with 31 clusters was also included. Two-dimensional Mopsil-location datasets (Finland) with four clusters and one two-dimensional miscellaneous dataset with three clusters completed the number of datasets used. The summary of the 20 datasets is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**GA**[55, 56] & **ABC**[25] & **DE**[25] & **PSO**[25] & **FA**[24] & **IWO**[25] & **TLBO**[55] & **SOS**[57] \\ \hline
**Parameter Value** & **Parameter Value** & **Parameter Value** & **Parameter Value** & **Parameter Value** & **Parameter Value** & **Parameter Value** & **Parameter Value** & **Parameter Value** & **Parameter Value** \\ \hline \(n\) & 25 & \(n\) & 25 & \(n\) & 25 & \(n\) & 25 & \(n\) & 25 & \(n\) & 25 & \(n\) & 25 \\ \(\mathbf{P}_{\alpha}\) & 0.8, & a & 0.009 & CR\({}_{\text{min}}\) & 0.2, & W\({}_{1}\), & 1.0 & \(\beta_{0}\) & 2 & s & 5 & m & 2 & P\({}_{c}\) & 0.2 \\  & 0.3 & a & 0.009 & CR\({}_{\text{max}}\) & 1.0 & W\({}_{2}\) & 0.99 & & & & & & & \\
**MP** & 0.02 & looker & 25 & F & 0.8 & \(\tau_{1}\), \(\tau_{2}\) & 2.0 & \(\gamma\) & 1 & \(\Sigma_{1}\), \(\Sigma_{2}\) & 0.5, & & & & \\  & & bees & & & & & & & & & & & & \\ K\({}_{\text{min}}\) & 2 & m & 2 & K\({}_{\text{min}}\) & 2 & K\({}_{\text{min}}\) & 2 & \(\epsilon\) & 2 & & & & & \\
**K\({}_{\text{max}}\)** & 16 & MaxFE & 50,000 & K\({}_{\text{max}}\) & 256 & K\({}_{\text{max}}\) & 256 & K\({}_{\text{max}}\) & 256 & & & & & \\ MaxFE & 50,000 & & & MaxFE & 50,000 & MaxFE & 50,000 & MaxFE & 50,000 & MaxFE & 50,000 & MaxFE & 40 + 2k \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter Setting of Algorithms.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 1 Introduction  
Ships' traffic is the supreme central factor of maritime shipping safety. The rising number of vessels and their sizes, together with cargo type, is related to increased likelihood of accidents including fires, explosions and losses of life and catastrophic impact on the environment. Such negative effects increase awareness of government bodies, industry sectors and society on the importance of risk assessment. To assess such a risk, domain experts need to know and understand how marine traffic behaves. Traffic trend modeling is an input of high value to construct robust risk assessment policies. AIS (Automatic Identification System) provides a great opportunity for traffic monitoring but also for discovering basic processes which govern the domain of marine traffic in given areas of interest.  
One of the main difficulties posed by marine traffic engineering is to determine the optimum parameters of existing or newly constructed parts of the waterways. Depending on the type of waterway parameters that can be obtained are, for example, density of ships per time period, changes of speed when conditions change on neighboring areas, level of cargo hazard in combination withsea water conditions, etc. Such factors are usually obtained by either an inexpensive but less accurate analytical method or by a more expensive but accurate simulation method [1].
## 4 Design and Implementation
### Implementation  
Based on the properties of persistent memory and the utilization of hashing techniques, we have implemented a mechanism to minimize performance impact and optimize memory usage. Our approach focuses on reducing the frequency of full-table rehashing, which can significantly impact tail latency. In this, we aim to improve overall performance and maximize the efficiency of memory utilization.  
The process of hashing records in this approach is similar to the process of hashing records to a bucket in CCEH for storage. When there is a request to insert a record into a bucket that is already full while the neighboring buckets have available space, the scheme checks if the insertion necessitates segment splitting. This ensures a balanced and comprehensive insertion across all buckets within the same segment before triggering a rehashing process. If there are neighboring buckets that are either empty or partially filled, it is more cost effective to store the records in those buckets rather than initiating directory doubling. This operation is facilitated by storing records in buckets and accessing them as arrays. Directory doubling and rehashing of records are only initiated when there is no space available in any of the buckets within the segment. The insertion process can be identified by a pointer, which assists other operations and serves as an indication that certain records have been shifted to other buckets within the segment.  
The deletion of records in this approach follows the overwrite method employed by the initial scheme, where invalid values can be overwritten with valid ones. In the case where a record is stored in a different bucket due to the bucket being full, the deletion operation will nullify the record in the respective bucket, thereby marking the space as free. For instance, if a record is originally hashed to bucket _b0_ but is moved to bucket _b3_ due to the bucket being full, the deletion process will first search for the record in bucket _b0_. If the record is not found, the search will continue in bucket _b3_ by looping through the records in the bucket. Once the record is located in the intermediate buckets or in _b3_, it will be nullified, rendering it invalid. If a record is deleted from a filled bucket, the system will move the record from another bucket back to its original hashed bucket to facilitate future lookups, unless data access speed is a critical concern. However, this operation has lower priority, as the bucket size is equivalent to the size of the cache line. When all buckets are exhausted, a segment split will be performed to rehash all records into their appropriate buckets.
## 3 Materials and Methods
### Environmental Evaluations and Measures to Reduce Environmental Impact
#### 3.2.2 Noise Pollution  
The operational noise assessment considers the noise generated by the container ship entering the port and when berthed, its loading or offloading, and noise from associated container handling plant and vehicles, and impacts on residential properties in the immediate vicinity of the container terminal. Sound power levels according to type of ships can be estimated according to a regression line developed by [PERSON] [20]. The evaluation of noise associated to yard activities (container handling, cranes, vehicles, auxiliary equipment, etc.) requires the knowledge of sources' location and height, working hours and sound power level. Such data can be obtained by means of direct noise measurements or by using default values and available noise source databases, to be validated with a following direct measurement. Also, traffic-related noise for hinterland delivery should be taken into account; it is mainly influenced by the location of roads and road surface (e.g., asphalt, bricks) and traffic volume and speed in the case of road traffic; location of railway traffic volume, average speed, rail support (wooden or concrete sleepers, etc.) and data on rail tracks (joined rail, switches and crossings, etc.) for rail traffic. Models for noise pollution impact on port communities usually takes into account the sound pressure levels and the distance between the port and the urban area [17]. In this paper we made reference to the formula provided by Lloyd's Register [21], according to Equation (2):  
\[L_{p}=L_{w}-10\log_{10}(4\pi r^{2}) \tag{2}\]where:  
* \(\mathrm{L_{p}}\): sound pressure level in the receiving position, dB
* \(\mathrm{L_{w}}\): sound power level of source, dB
* r. distance between the source and receiver  
Mitigation measures for noise impacts might be related to all the port subsystems [22]. Low noise driving training for drivers (ECO-driving) might be a low-cost solution; yard planning (e.g., moving the noisiest activities away from the port border) and change working period hours (e.g., avoiding nighttime operations) might improve impact in port communities; finally, technological measures, such as isolation of sound intensive components and use of softer grounds in terminal building, might result in an effective mitigation.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### State of-the-Art Onset Detection Algorithms  
To evaluate our proposed algorithm's efficiency and accuracy, eight different onset detection algorithms were selected against which to compare the accuracy of the proposed algorithm. The implemented versions of the algorithms in Python were used. The algorithms were taken from implementations across four different Python libraries, namely Librosa [22; 23], Madmom [24], Aubio [25], and Essentia [26]. The explanations of the algorithms are provided in the following by categorizing them based on the Python libraries.
## 4 Results
### Comparison and Verification of Typical Landslides  
In order to illustrate the accuracy of the recognition results and compare the results of the two systems, three landslides were selected: one that was detected by both data sources (Huijayuan landslide), one that was detected only by ALOS/PALSAR\(-2\) (Lianfengcun landslide), and one that was detected only by Sentinel\(-1\)A (Sansingwan landslide). The locations of the landslides are marked in Figure 8. The deformation along the LOS direction was changed to the vertical direction for better comparison of monitoring results.
## 3 Results
### Thickness of the Adhesive Layer  
Table 5 and Figure 5 present the thicknesses of the adhesive layer in the different adhesive systems under different application methods. Three-way ANOVA revealed that although the application method and adhesive systems used significantly influenced the thickness of the adhesive layer (\(p\) < 0.001), the etching mode did not have any influence (\(p\) = 0.974). The three-way interaction between the etching mode, application method, and type of adhesive system was not significant (\(p\) = 0.987). Although the two-way interaction between the application method and type of adhesive system used was significant (\(p\) < 0.05), other interactions, such as those between the etching mode and application method and etching mode and type of adhesive system used, were not significant (\(p\) = 0.697 and \(p\) = 0.873, respectively).  
The mean adhesive thickness ranged from 8.2 to 20.2 \(\upmu\)m in Group I, 34.4 to 56.9 \(\upmu\)m in Group II, and 105.9 to 158.7 \(\upmu\)m in Group III (Table 5). In Group I, GU showed a significantly thicker adhesive layer than the other adhesive systems in both etching modes. In Group II, although no significant differences were observed between GU and CS, OX showed a significantly thinner adhesive layer than the other adhesive systems in both etching modes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Group I**} & \multicolumn{3}{c}{**Group II**} &  
In Group III, GU showed a significantly thicker adhesive layer than OX in both etching modes. The coefficient of variation (CV) was obtained from the mean adhesive thickness and standard deviation. The CV ranged from 7.6% to 15.9% in Group I, 13.5% to 26.8% in Group II, and 22.5% to 35.8% in Group III. The CV tended to increase with the increase in thickness of the adhesive thickness (Group I < Group II < Group III), regardless of the adhesive system.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 8 Applications
### Choosing Morse Scalar Functions
#### 8.1.2 The Poisson Equation  
The Poisson equation on a triangulated mesh with Dirichlet boundary condition is defined by:  
\[\Delta f=h,\quad f(v_{i})=c_{i}\;\text{where}\;v_{i}\in V_{C} \tag{1}\]  
where \(V_{C}\subset V\) is a set of constrained vertices and \(h:M\longrightarrow\mathbb{R}\) is a known function. The cardinality of the set \(V_{C}\) must be at least 1 in order for system (1) to have a unique solution. With the appropriate choice of \(h\), we can use the Poisson equation for our purpose. Indeed, if we choose the function \(h\) as suggested by [PERSON] et al. in [93], then the solution \(f\) of the Poisson equation gives us a scalar field whose level sets follow one of the principal curvatures of the underlying manifold. Specifically, this can be done by solving:  
\[\Delta f(v)=\kappa(v)\;\text{where}\;f(v_{source})=c \tag{2}\]  
where \(\kappa(v)\) is the mean curvature at the vertex \(v\):  
\[\kappa(v_{i})=\frac{1}{4A_{mixed}(v_{i})}\sum_{j\in N(i)}(\cot\theta_{ij}+ \cot\beta_{ij})||(v_{i}-v_{j})|| \tag{3}\]  
here, the angles \(\theta_{ij}\) and \(\beta_{ij}\) are given in Figure 24, and \(A_{mixed}(v_{i})\) is the surface mixed area around the vertex \(v_{i}\)[94]. Examples of Poisson fields on triangulated meshes are shown in Figure 25.
## 4 Computational Experiments
The results in Tables 1-5 indicate that, for the data considered here, \(\ell^{1}\) MCDA always outperforms standard PCA in accuracy except when the data come from one single Gaussian distribution and that \(\ell^{1}\) MCDA outperforms two robust PCAs in accuracy in nearly all cases in accuracy in nearly all the cases \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Data of** & **Standard PCA** & **Croux+Ruiz-Gazen** & **Ke+Kanade** & \(\boldsymbol{\ell^{1}}\)** MCDA** \\ \hline Table 1 & 3.398 ms & 177.832 ms & 32.921 ms & 107.382 ms \\ \hline Table 2 & 4.411 ms & 198.833 ms & 33.477 ms & 116.504 ms \\ \hline Table 3 & 3.667 ms & 197.221 ms & 48.133 ms & 131.338 ms \\ \hline Table 4 & 5.277 ms & 210.442 ms & 55.672 ms & 147.185 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sequential computing times for generating the results in Tables 1–5 by the four methods. presented here. These computational results are consistent with the theoretically known fact that standard PCA is optimal for one single Gaussian distribution. Interestingly, however, the results in Table 1 indicate that \(\ell^{1}\) MCDA performs quite well--albeit sub-optimally--for one single Gaussian distribution. Thus, there is no major disadvantage in using \(\ell^{1}\) MCDA as a default PCA (instead of standard PCA), since it performs well both for the cases when standard PCA is known to be optimal and, as we have seen, for the many other cases when standard PCA and robust PCAs perform less well, poorly or not at all.
## 4 Boundedness and Stability  
In earlier work, hyperbolic lines were proposed as artificial trajectories between known samples, owing to their stability-preserving nature and the existence of a guaranteed upper bound on the deviation from the known models. Since hyperbolic lines lie entirely within the unit circle, the poles of the interpolated system also remain inside the unit circle, ensuring system stability. In [20], these stability properties and deviation bounds are derived specifically for hyperbolic lines. Here, we extend the proof to cover all trajectories that satisfy the same bound.  
The hyperbolic line, as an artificial pole trajectory, has favorable properties like stability preservation and uniform boundedness, but it never represents the behavior of the model and cannot be adapted to different situations. It is reasonable to assume that the locations of the poles follow some pattern as the parameter changes, and that is not a hyperbolic line. The interpolation method should be able to handle this additional information. In this section, the goal is to find a set of trajectories that have the same properties as the hyperbolic line. Using a larger set of trajectories enables techniques that adapt to specific pole trajectories.
# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 3 Grinding Surface Roughness Feature Extraction and Fusion
### Surface Roughness Prediction Model of Bearing Cylindrical Precision Grinding
The dual form of the optimization problem is as follows:  
\[\underset{\alpha_{i},\alpha_{i}^{*}}{\max}\left\{-\frac{1}{2}\sum\limits_{i,j =1}^{n}(\alpha_{i}-\alpha_{i}^{*})(\alpha_{j}-\alpha_{j}^{*})\langle x_{i},x_ {j}\rangle+\sum\limits_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})y_{i}-\sum\limits_ {i=1}^{n}(\alpha_{i}+\alpha_{i}^{*})\varepsilon\right\} \tag{34}\]  
\[Constraints:\ \sum\limits_{i=1}^{i=1}(\alpha_{i}-\alpha_{i}^{*})=0,0\leq \alpha_{i},\alpha_{i}^{*}\leq C,\ i=1,2,...,n. \tag{35}\]  
Lagrange multipliers \(a_{i}\) and \(a_{i}^{*}\) can be solved, and the weight vector \(\omega\) can be obtained as follows:  
\[\omega=\sum\limits_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})x_{i} \tag{36}\]  
The value of \(b\) can be obtained according to the KKT condition, so the nonlinear function \(f(x)\) can be obtained:  
\[f(x)=\sum\limits_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(x_{i},x_{j})+b \tag{37}\]  
where \(K(x_{i},x_{j})\) denotes a kernel function. Selecting the appropriate kernel function can not only improve the accuracy of the prediction model but also reduce the influence of random noise on the prediction model and reduce the calculation cost. Therefore, radial basis function (RBF) is chosen as the kernel function of the model. The specific formula is as follows:  
\[k(x,y)=\exp\left(-\frac{x-y^{2}}{2\sigma^{2}}\right) \tag{38}\]  
RBF maps the input sample space to the high-dimensional space through the exponential relationship to achieve high-precision nonlinear fitting, which has the characteristics of low sample requirements, wide application, and high flexibility.
## 3 Methods
### Measurement Data  
Figure 3 shows the experimental measurement data, i.e., the free electron density \(\hat{n}_{\mathrm{e}}\) measured using MCRS in panel (a) and the transmittance signal \(\hat{T}\) measured by laser light extinction in panel (b), from the moment of plasma ignition at \(t=0\,\mathrm{s}\) until the end of the second dust growth cycle at \(t=250\,\mathrm{s}\). In this section, we will discuss the general behavior of the signals over the course of time.  
From the free electron density, it can be seen that the electron density is initially about \(\hat{n}_{\mathrm{e}}\approx 12\times 10^{15}\,\mathrm{m}^{-3}\), but quickly decreases by a factor of 6 to \(\hat{n}_{\mathrm{e}}\approx 2\times 10^{15}\,\mathrm{m}^{-3}\) about 20 s after plasma ignition. For the current method, we are mainly interested in the decrease of the electron density with respect to the initial value, \(\Delta\hat{n}_{\mathrm{e}}(t)=\hat{n}_{\mathrm{e,initial}}-\hat{n}_{\mathrm{e}} (t)\) (see Figure 3a), since it can be attributed to the charging of the growing dust particles. Note that here the subscript 'initial' refers to the moment shortly after ignition of the plasma; i.e., when the electron and ion densities have built up, but when there are no dust particles yet. In practice, this corresponds to the values at the time of the first measurement point, which is typically measured \(100\,\mathrm{ms}\) after switching on the plasma. At about \(t=35\,\mathrm{s}\) the transmittance starts to decay linearly. During this period, the electron density changes only slightly up to the point that the transmittance value is at its minimum. This moment in time, corresponding to a minimum in \(\hat{T}\), represents the formation of the void: a volume in the discharge center from which the dust grains are being removed when the outward-pointing ion drag force overcomes the inward-pointing electrostatic force. After this moment, both the electron density and transmittance start to increase again, signaling that the dust grains are effectively being transported toward the plasma edge and will eventually leave the plasma. It is interesting to note that both the electron density (around \(t=110\,\mathrm{s}\)) and the transmittance (around \(t=150\,\mathrm{s}\)) do not fully recover back to their initial values before the subsequent growth cycle starts. This indicates that a new growth cycle commences as soon as the center of the discharge is sufficiently dust-free, even though there is still a substantial amount of dust in the periphery.  
Figure 2: Flowchart for the retrieval of the best fitting dust particle size and dust density from the comparison of experimental MCRS and extinction measurements and the OML theory and the Lambert-Beer law.
As the contamination of reservoirs involves deposition of pollutants in sediments [18; 32], elutriates are an approximation to soil pore water, and as the idea of this study is to expose benthic species suited for living in sediments to BCs, it is expedient to work with elutriates. The purpose of this contribution was to use acute and chronic toxicity tests [33] with the ciliate _Paramecium caudatum_, the rotifer _Lecane quadridentata_, and the cladocerans _Daphnia magna_ and _Mouina macrocopa_ to assess toxicity of hazardous substances identified in four different BCs that have been generated using local feedstock in Aguascalientes, Mexico. First, the BCs were analyzed with regard to heavy metal, PAHs, PCDD/Fs, and PCBs levels. Afterwards, the toxic responsiveness of the invertebrates to the BC elutriates in a controlled laboratory test environment has been checked, to estimate lethal concentration where 50% and 10% of test animals die (LC\({}_{50}\), LC\({}_{10}\).) and define no observed effect concentration (NOEC) and lowest observed effect concentration (LOEC) values. Subsequently, the expected toxicity range due to the concentration of the four BCs in the environmental samples, using the equations obtained from the toxicity tests, was calculated. The present work shows that the application of a toxicological test using aquatic invertebrates could substantially increase BC application safety in and close to sensitive habitats.