esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
###### Abstract  
With the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. On weekends, traffic usage is smaller than on weekdays, but the distribution does not change much. We find that the self-similarity of IPv4 traffic is close to that of IPv6 traffic, and there is a strong positive correlation between IPv6 traffic and IPv4 traffic. Based on our findings on traffic patterns, we propose a new IPv6 traffic prediction model by combining the advantages of the statistical and deep learning models. In addition, our model would extract useful information from the corresponding IPv4 traffic to enhance the prediction. Based on two real-world datasets, it is shown that the proposed model outperforms eight baselines with a lower prediction error. In conclusion, our approach is helpful for network resource allocation and network management.  
IPv6 traffic analysis; self-similarity; IPv6 traffic prediction; SARIMA; LSTM Article Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai.  
Future Internet 202214353. [[https://doi.org/10.3390/fi4120353](https://doi.org/10.3390/fi4120353)]([https://doi.org/10.3390/fi4120353](https://doi.org/10.3390/fi4120353))
# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 2 Materials and Methods
### Machine Learning Classification Algorithms
#### 2.3.4 Support Vector Machine (SVM)  
A Support Vector Machine (SVM) represents a supervised machine learning algorithm functioning as a data classifier. Employing an SVM allows for the classification of observations through the selection of two parallel hyperplanes (\(p_{1}:=w^{T}x-b=1,\ p_{2}:=w^{T}x-b=-1\)). The area enclosed between the two hyperplanes is referred to as the margin, which measuresa length of \(\frac{2}{||\psi||_{2}}\), where \(||\cdot||\) represents the Euclidean distance. Our objective is to optimize the selection of these two hyperplanes to maximize the margin and hence, the accuracy of the model. Furthermore, by employing a mapping function, denoted as \(\psi(\cdot)\), the data can be transformed into a domain where they become linearly separable. Utilizing the Radial Basis Function (RBF), we can establish a decision boundary that may exhibit non-linearity in the original space.
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
###### Abstract  
The electrocardiogram (ECG) was the first biomedical signal for which digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (QRS complexes and other waveforms, such as the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, such as the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies a fast sparse inference algorithm to model the signal using the constructed dictionary. As a result, our method is much more efficient from a computational point of view than other existing algorithms, thus becoming amenable to dealing with long recordings from multiple patients. Regarding the dictionary construction, we located first all the QRS complexes in the training database, then we computed a single average waveform per patient, and finally we selected the most representative waveforms (using a correlation-based approach) as the basic atoms that were resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach.  
electrocardiogram (ECG); Least Absolute Shrinkage and Selection Operator (LASSO); overcomplete multi-scale dictionary construction; signal representation; sparse inference +
Footnote †: journal: Applied Mechanics and Applications
## 4 Experiments
### Datasets and Evaluation Metrics
#### 4.1.2 Evaluation Metrics  
On CrowdHuman, we report the performance of our proposed method using various metrics, including average accuracy (\(AP\)), \(Recall\), \(MR^{-2}\), and \(FPS\). On CityPersons, we evaluate the performance of our detector using \(MR^{-2}\) on four subsets: reasonable (\(R\)), bare (\(B\)), partial (\(P\)), and heavy occlusion (\(H\)). These subsets are divided based on the visibilities of pedestrians, as shown in Table 2. More details of the evaluation metrics are given below  
* \(AP\): Average precision, which summarizes a precision-recall curve of detection results, is one of the most popular evaluation metrics in generic object detection. In the subsequent experiments, we follow the \(AP\) metric in PASCAL VOC [44] (the larger, the better) and consider proposals with \(IoU\geq 0.5\) to be positive. This metric effectively measures the accuracy of a detector.
* \(Recall\): The maximum recall, for a fixed number of proposals, represents the proportion of true positives detected by a detector out of the total number ground truths. This metric evaluates the ability of a detector to accurately detect the true ground truths. Larger values indicate better performance.
* \(MR^{-2}\): Log-average miss rate, which is calculated using false positives per image (FPPI) in the range of \(\left[10^{-2},10^{0}\right]\), is a commonly used evaluation metric in pedestrian detection. This metric is particularly sensitive to false positives, especially those with high confidence scores. Smaller values of \(MR^{-2}\) indicate better performance of a pedestrian detector.
* \(FPS\): Frames per second, which represents the number of frames processed per second, is a commonly used metric for measuring the speed of detectors. Larger values of \(FPS\) indicate faster processing speed of a detector.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Objects** & **CrowdHuman** & **CityPersons** \\ \hline Images & 15,000 & 2975 \\ Persons & 339,565 & 19,238 \\ Ignore regions & 99,227 & 6768 \\ Person/image & 22.64 & 6.47 \\ Unique persons & 339,565 & 19,238 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of CrowdHuman and CityPersons training sets.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 5 Simulation Results
### Application to Knapsack
Figure 2: Trend of Cost function obtained from proposed XOR BGSA vs. four other algorithms namely: BGSA [16], improved binary particle swarm optimization (IBPSO) [17], binary particle swarm optimization (BPSO) [15], binary grey wolf optimization (BGWO) [18] and binary dragonfly optimization algorithm (BDA) [19]. Knapsack optimization problems numbers 7 to 12.
## 2 Method
### Dataset Construction and Process with Multi-Role Collaboration
Data Annotation: In this step, the administrator divides the data requiring annotation into distinct annotation tasks based on specific annotation requirements. Each task may have unique specifications and annotation point criteria. Multiple annotators are assigned to complete each annotation task to ensure thorough coverage and accuracy. * 7. Data Review: Following completion of annotation, annotators submit their work to the administrator, who conducts a comprehensive review of the annotated image data. * 8. Data Augmentation: After the initial review, data augmentation is applied, particularly for datasets with limited sample sizes or, when necessary, across the entire dataset. This process includes an additional review phase to monitor the quality of the augmented data at the source, minimize annotation errors, and avoid the compounding of inaccuracies through data augmentation. * 9. Annotation Based on Augmentation Rules: Data augmentation is executed according to well-defined rules. For instance, when performing geometric transformations, such as rotating an image by 90 degrees or flipping it horizontally, annotations are adjusted based on these operations. An automatic annotation tool is then utilized to generate annotations for the augmented data, with the resulting annotation information comprehensively recorded in the annotation dataset. During this process, clear links between each piece of augmented data and its corresponding original data, along with the applied augmentation operations, are documented to facilitate future data usage and management. Crucially, metadata from before and after augmentation are distinguished through a systematic file-naming convention. * 10. Data Review: Automatically annotated data are opened using annotation software (e.g., LabelImg 1.5.1) for a thorough review to verify annotation accuracy. * 11. Data Automatic Distribution: Upon passing the review, the system automatically partitions the data into training and testing sets, providing notifications if certain classes contain insufficient sample sizes. * 12. Data Output: The finalized dataset is outputted for use by model trainers. Finally, the annotated data are employed to train the target algorithm model. The quality of annotated data is primarily evaluated by reviewers, who conduct model tests and relay the test results to model training personnel. Based on the feedback, model training personnel iteratively adjust the model parameters to achieve optimal performance. If the desired model performance cannot be attained despite parameter tuning, this indicates deficiencies in the annotated data. In such cases, reviewers communicate the identified data issues to annotators, who then reassign annotation tasks as necessary, thereby maintaining a closed-loop workflow. Ultimately, reviewers submit the final model performance indicators to product evaluation personnel, who conduct the concluding assessment before deployment.
###### Abstract  
One of the functionalities which are desired in Ambient and Assisted Living systems is accurate user localization at their living place. One of the best-suited solutions for this purpose from the cost and energy efficiency points of view are Bluetooth Low Energy (BLE)-based localization systems. Unfortunately, their localization accuracy is typically around several meters and might not be sufficient for detection of abnormal situations in elderly persons behavior. In this paper, a concept of a hybrid positioning system combining typical BLE-based infrastructure and proximity sensors is presented. The proximity sensors act a supporting role by additionally covering vital places, where higher localization accuracy is needed. The results from both parts are fused using two types of hybrid algorithms. The paper contains results of simulation and experimental studies. During the experiment, an exemplary proximity sensor VL53L1X has been tested and its basic properties modeled for use in the proposed algorithms. The results of the study have shown that employing proximity sensors can significantly improve localization accuracy in places of interest.  
localization; hybrid localization; Bluetooth Low Energy; extended kalman filter; internet of things; proximity sensors +
Footnote †: journal: Applied Sciences
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 2 Micro-Aerial Vehicle Flight
The benefits of this strategy were that it not only improved the reliability of the BEM, but it also enabled various aerodynamic studies (e.g., [98; 99; 100; 101; 102]). Note that the BEM method is widely utilized due to its simplicity and robustness, however, its accuracy has been questioned. In contrast, computational simulations based on the solution of Navier-Stokes equations [103; 104; 105] have been utilized because they provide, with relatively high accuracy and precision, the detailed flow structures, time-dependent aerodynamic forces, and power requirements. However, this strategy needs high-fidelity modeling and a long experiment processing time, which in turn diminishes its usage in practice. A CFD-informed quasi-steady model based on a hybrid of high- and low-fidelity models to reduce the aforementioned complexity was presented in [106]. The systematic development regarding the beetle's and DelFly II's aerodynamics can be found in [107; 108; 109], respectively. In order to update the research carried out in the area of FWMAVs, a comprehensive bibliometric review can be found in [110]. The flight stability of different species of natural fliers, particularly the longitudinal stability, was studied successfully by a number of researchers, for example, the stability of desert locust _Schistocera Gregaria_ was thoroughly presented in [86], the nano-humming bird was focused on in [12], insects mimicking the FWMAV's initial stability was addressed in [111], and bumblebee stability was presented in [112]. In general, the eigenvalue-based analysis resulted in three modes common to all fliers, namely, the unstable oscillatory mode, stable fast subsidence mode, and stable slow subsidence mode. Almost all mathematical models developed in the aforementioned studies fall in the category of modeling by the first principles. System identification approaches have hardly been used. Therefore, the forthcoming section will present, in detail, the techniques devised for system identification approaches, particularly, in obtaining system stability derivatives and control-oriented dynamic models of FWMAVs and natural flapping fliers.
## 3 Data Assimilation
### Pseudo- Lagrangian Assimilation  
A common problem when assimilating Lagrangian data is that there is a nonlinear relationship between the observed variables (i.e., the positions r) and the model variables to be modified (i.e., the Eulerian velocities u). This problem can be circumvented by approximating u as the finite difference of successive positions, u \(\approx\Delta\text{r}/\Delta\text{t}\) (\(\Delta\text{r}=\) position displacement and \(\Delta\text{t}=\) sampling period). This method, known as pseudo-Lagrangian assimilation, works well when the sampling period is much smaller than the Lagrangian correlation time scale [16]. For this study, the drifters were sampled at a frequency of 1 Hz resulting in a sampling period that is at least one order of magnitude less than the Lagrangian correlation scale (50 s) in a channel with similar physical characteristics as Currimundi Lake [9].
## 3 Experimental Setup
### Processing Historical OSM Data  
OSM XML data can be processed in a number of ways. Two of the most popular methods in the OSM community involve the usage of command line tools such as osm2 pgsql[40] and osmosis[41]. However, these tools are developed to process only the most recent version of the OSM data for a given region. [PERSON] and [PERSON] [42] describe a process using Linux command line tools and the OSM web API to extract the history of selected objects in OSM. However this process takes prohibitively long due and can only be used for a small number of features to prevent overloading the OSM web API servers. In this paper we have updated the approach in [PERSON] and [PERSON] [42] and use an alternative method, which has greater efficiency. Every few months OSM makes the \"planet.osm/full\" file available which includes almost all OSM data ever collected [14]. This file is currently \(26\) Gb of compressed OSM-XML expanding to \(600\) Gb uncompressed. We use the open source _osm-history-splitter_ tool [43] to extract the history of edits from the full history dump. The _osm-history-splitter_ allows splitting of the full history dump based on bounding rectangles or polygons. Using a predefined polygon for the UK and Ireland, from the country clipbounds polygons from Geofabrick [44]), the history for all nodes and ways in this region is extracted saved in OSM-XML format. The final step of the process involves loading the history of the objects into a PostgreSQL PostGIS database. The structure of the tables in this database are very similar to the standard way that OSM data is usually stored in databases by tools such as osm2 pgsql. We developed two Python scripts to process the OSM history file by firstly inserting all nodes and then inserting all ways (polygons and polylines). The process took \(305\) h. The Python script must load every node (each node has multiple versions potentially) into the Postgis database. Then for each way (polygon, polyline) the Python script must reference the correct node (with the correct version) for each way version. An edit or contribution is an action which causes a new version of an object to be created. An edit or contribution can include: adding or deleting nodes, moving nodes, editing nodes,adding tags, editing existing tags, _etc_. The contributor must then submit or commit these to the OSM database. Formally, the following information is stored for every object. Suppose that we are storing the object \(P\) at the \(n^{th}\) version. \(P\) is stored as the tuple as follows  
\[P=\left(u,v,\tau,G,T\right)\]  
where the elements of the tuple \(P\) are as defined as follows: \(u\) is the user id of the OSM contributor who created this version of \(P\), \(v\) is the version of the OSM object, \(\tau\) is the timestamp for the edit, \(G\) is the geometry of \(P\), and \(T\) is the set of tags (keys,values) assigned to this version of \(P_{i}\) which are stored as a comma-separated list
## 3 The Time Source Techniques
### Pulsar Star
#### 3.3.3 Flexibility  
Most pulsars are detected and monitored at radio wavelengths, typically utilizing either single large parabolic antennas with diameters of 30 m or more, or arrays of smaller antennas [58], for observing extremely bright pulsars, the antennae size can decrease, but it is still large compared to GPS or LEO antennae, which makes pulsar timing not very flexible. However, deploying a radio telescope at each location is unnecessary, as the accurate timing signal can be distributed using time distribution protocols.  
The implementation of pulsar timing is the most expensive option, requiring a significant investment in telescope receivers and a complex signal back-end processing system.
## 1 Introduction
The distribution parameters are also uncertain due to insufficient data, and the optimum distribution types may be different due to insufficient data from multiple data sources, the fusing algorithm should be researched to obtain an accurate uncertainty representation model of epistemic uncertainty. The data fusion algorithm for multiple sources [41] can only deal with the Bernoulli distribution type. To the best of our knowledge, there is no general uncertainty data fusion algorithm for multiple sources of input data, especially for the uncertainty representation of distribution parameters. For a mechanical system where the input variables are uncertain, sparse or inaccurate data will result in the distribution parameters being unavailable. If a probability distribution is used in the distribution parameters, the distribution family can be used to represent these random variables. However, there is insufficient uncertainty information to obtain the probability distribution of uncertain distribution parameters in some actual conditions. Some sparse and incomplete uncertainty information can be obtained from multiple data sources, and the type and amounts of available uncertain information from multiple data sources may be different. Therefore, a reliability measure method considering the uncertainties of input variables and their distribution parameters simultaneously is proposed. In general, the key scientific contribution of our work is providing a unified uncertainty quantification method considering uncertain distribution parameters, owing to insufficient data being obtained from two sources. The proposed algorithm in this paper can decrease the uncertainty of insufficient data and obtain an accurate probabilistic function for epistemic variables with uncertain distribution parameters, which can increase the accuracy of the reliability index of mechanical systems. The rest of this paper is organized as follows. The reliability measure problem is described in Section 2. In Section 3, the uncertain distribution parameters of epistemic uncertainty are fused using a Gaussian interpolation method, which can fuse insufficient data and evidence theory information from two sources. The epistemic uncertainties are probabilistically represented considering uncertainties of distribution parameters in Section 4. A reliability index that simultaneously describes aleatoric and epistemic uncertainties is calculated, and the unified reliability analysis procedure is proposed in Section 5. In Section 6, the proposed algorithm is applied to the crank-slider mechanism, and conclusions are summarized in Section 7.
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
## 3 Results
### SEM Microscopic Analysis  
Figure 11 shows the observation pictures at 200\(\times\) and 1000\(\times\) magnification. The flat-printed samples performed well, showing uniform melting and fewer surface defects. The vertically printed samples had many pores, with mixed powders, poor quality, and unmelted metal powder. Such defects are often the main cause of stress concentration and will cause catastrophic damage. This situation may be due to two reasons: shrinkage defects caused by too fast cooling during manufacture or incomplete melting caused by fast scanning speed.  
Figure 12 is a surface photograph of stretching results in different manufacturing directions. As shown in the figure, both samples had a well-shaped fracture path when fractured. In addition, at 200\(\times\) magnification, it can be seen that both had unmelted metal powder but the melting quality of the flat-printed sample was significantly better. At the same time, it can be seen that the cracks of the flat-printed sample were small at 1000\(\times\) magnification, while the vertical printing led to greatly expanded cracks on the surface of the sample, with a loose sliding phenomenon.  
In Figure 13, it can be seen that the powder particles with a large number of second-phase particles at the fracture were not melted and showed a lot of dimples at 2000\(\times\) magnification. The dimples were significantly denser and smaller under vertical printing. In terms of materials [36], the appearance of dimples was caused by the plastic deformation of the structure, with larger dimples indicating better ductility. The elongation of the vertically printed samples was lower than that of the flat-printed samples with larger dimples. At the same time, the large pores could also cause stress concentration, resulting in the vertically printed samples being more easily damaged during the test.  
Figure 11: Surface topography before stretching: (**a**) flat-printed sample 200\(\times\); (**b**) vertical-printed sample 200\(\times\); (**c**) flat-printed sample 1000\(\times\); (**d**) vertical-printed sample 1000\(\times\).  
Figure 12: Surface photo of the sample after tensile test: (**a**) overall appearance of the flat-printed sample after stretching; (**b**) overall appearance of the vertically printed sample after stretching; (**c**) particles in the crack of the flat-printed sample; (**d**) particles in the cracks of the vertically printed sample; (**e**) crack morphology of the flat-printed sample; (**f**) crack morphology of the vertical-printed sample.  
Figure 13: Photo of the microscopic topography at the fracture: (**a**) flat-printed sample 200\(\times\); (**b**) vertical-printed sample 200\(\times\); (**c**) flat-printed sample 200\(\times\); (**d**) vertical-printed sample 200\(\times\).