esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 3 Materials and Methods
### Test Data  
In order to test the ACS module, it is necessary to use pairs of images that resemble images taken by stereo cameras in real applications. As presented in the introduction, images from testbeds do not fulfill these criteria because of their superior quality in terms of calibration. Therefore, in order to prepare test data for the ACS module, the author of this paper implemented software to transform the images from testbeds into a form that makes them similar to images taken by a regular stereo camera. This software is based on reversing, to some extent, the calibration process. Every point \(\mathbf{p_{10}}\) of a side image is transformed as per Equation (8).  
\[\mathbf{p_{10}}=\begin{bmatrix}cos(\omega)&-sin(\omega)&0\\ sin(\omega)&cos(\omega)&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}1&0&0\\ 0&1&g\\ 0&0&1\end{bmatrix}\mathbf{p_{1}} \tag{8}\]  
where \(\omega\) is a rotation angle, \(g\) is the extent of the translation of an image in the y-axis, and \(\mathbf{p_{10}}\) is an image obtained as a result of the transformation.  
Reversing calibration involves identifying the extrinsic parameters of a stereo camera. Therefore, the transformations performed are rotation and translation in the y-dimension. These transformations do not cover all possible miscalibrations that occur in real-world applications. However, they were selected because they can be addressed using the addi  
Figure 3: Points affecting rotation estimation in the ACS module.  
tional calibration performed by the ACS module with the stereo matching algorithms. Only a side image is modified. Modifying only a side image is sufficient for adjusting extrinsic parameters and simultaneously transforming a side image so as to not interfere with the ground truth of a reference image. Thus, the ground truth prepared for stereo pairs can be used for these pairs both with and without the transformations described in this section.  
The extent of the transformations is defined by parameters \(\omega\) and \(h\). These parameters set the extent of the inaccuracies injected into the calibration in order to make it possible to test the performance of the ACS module, which is designed to reduce these faults. The tests are described in the next section.  
The datasets used for performing the experiments with reverse calibration were acquired from Middlebury Stereo Vision [1, 27, 35, 36, 37]. Tests were performed on the images included in the training sets provided by the evaluation SDK, released as a part of the Middlebury Stereo Vision project. These sets are called Adirondack, Jadeplant, Motorcycle, Playroom, Shelves, Vintage, Piano, Pipes, Playtable, Recycle, and Teddy. They contain over 3.5 million test points.
# Higher-Order Belief Propagation Correction Decoder for Polar Codes
###### Abstract
In this article, the BPC decoder is modified by redefining the correction set based on a new metric and the correction operation based on the predefined threshold, abbreviated as the MBPC decoder. Then, the proposed MBPC decoding is extended to a higher-order scheme, named MBPC-\(\Omega\), where \(\Omega\) code bits are corrected in one decoding attempt. The simulation results show that the MBPC decoder shows a slight performance gain but has less complexity and latency than the original BPC decoder. The higher-order MBPC decoders are verified to considerably improve the BLER performance. For polar codes of length \(N=2048\) with 24-bit CRC termination, when \(\mathrm{BLER}=10^{-3}\), the MBPC-2 decoder achieves around a 0.9 dB performance gain compared with the original BP decoder. Compared with existing BP-based multiple flipping decoders, the MBPC-2 performs a similar BLER but approximately half iterations. At \(\mathrm{BLER}=10^{-5}\), the MBPC-2 decoder achieves a gain of 0.2 dB compared with the CA-SCL decoder with \(L=4\), at a latency overhead saving of 95%. The contributions of this article are summarized as follows. * Different from using the stopping tree to reduce the searching range in the original BPC decoder, a new metric is proposed, which combines the reliability of code bits and the size of the stopping tree to which it belongs, to construct the correction set effectively. * We refine the correction operation in the original BPC decoder. Setting a threshold \(\mathcal{V}\), an identified code bit is only corrected to its opposite side when its reliability is lower than \(\mathcal{V}\). The decoding latency is reduced by avoiding invalid decoding attempts, especially in low signal-to-noise ratio (SNR) regions. In addition, the refresh operation is modified by re-initializing the massage to the initial state of the original BP rather than the end state to remove the dispensable store procedures. * The MBPC-\(\Omega\) decoder is proposed to further improve the BLER performance, where the decoder is executed on correction sets of increasing order. The correction set \(\mathcal{S}_{\Omega}\), in which each element contains \(\Omega\) code bits, is established nested based on the preorder set \(\mathcal{S}_{\Omega-1}\) and the corresponding decoding result. The remainder of this article is organized as follows. Section 2 provides brief statements of the polar code, the BP decoder, and the BPC decoding algorithm. Section 3 analyzes the metric and provides the proposed MBPC decoding algorithm. The higher-order MBPC decoder is presented. The simulation results are presented in Section 4. Finally, Section 5 concludes the article.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Datasets  
To evaluate the anomaly detection techniques, researchers use either real, synthetic, or altered real datasets. In this review we found most techniques were evaluated using real datasets, which are publicly available. One of the most commonly used datasets in the field of anomaly detection is KDD99, which has been used in many studies such as [30; 36; 37]. Similarly, the Numenta Anomaly Benchmark (NAB) dataset has gained popularity recently as it consists of seven categories of datasets injected with anomalies, and it has been used in [35]. Yahoo is another real-world data set, which is also publicly available and been used in [40]. Other real world benchmark datasets are also available and have been used by many researchers. However, when the data for some application domains are available, researchers tend to simulate the real data and generate data that will reflect the real-world environment, as in the case of [14; 25; 29]. With synthetic data, researchers have the advantage of having the data labeled, and they can inject outliers/anomalies inside the data to evaluate the detection performed with the techniques used.
# Spatial Correlations of Global Seismic Noise Properties
###### Abstract  
A study of global seismic noise during 1997-2022 was carried out. A property of waveforms known as the Donoho-Johnston (DJ) index was used, which separates the values of the wavelet coefficients into \"small\" and \"large\". For each reference point in an auxiliary network of 50 points, a time series was calculated with a time step of one day for the median of the values at the five nearest stations. In a moving time window of 365 days, correlations between the index values at the reference points were calculated. A decrease in the average values of the DJ-index and an increase in correlations were interpreted as a sign of an increase in global seismic danger. After 2011, there was a sharp increase in the maximum distances between reference points with large correlations. The high amplitude of the response of the DJ-index to the length of the day for 2020-2022 could predict a strong earthquake in the second half of 2023. The purpose of this study was to improve the mathematical apparatus for assessing the current seismic hazard according to the properties of seismic noise.  
seismic noise; wavelet-based entropy; wavelet-based Donoho-Johnston index; correlations; day length +
Footnote †: journal: Physics Letters A
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 2 Related Work
### Robust Principal Component Analysis (RPCA)  
RPCA is arguably the most widely used modification of the procedure of PCA with an improved robustness especially for a gross corruption scenario. The RPCA model decomposes the corrrupted observation data matrix \(\mathbf{D}\) into the low-rank matrix \(\mathbf{L}\) and the \(l_{0}\)-norm of sparse matrix \(\mathbf{S}\). The mathematical expression is as follows.  
\[\min_{\mathbf{L},\mathbf{S}}\text{rank}(\mathbf{L})+\lambda\|\mathbf{S}\|_{0} \text{ s.t. }\mathbf{D}=\mathbf{L}+\mathbf{S} \tag{1}\]  
where \(\mathbf{L}\) is a low-rank matrix; \(\mathbf{S}\) denotes the sparse matrix; \(\lambda\) denotes a regular factor that balances low rank and sparsity; \(\|\cdot\|_{0}\) is the \(l_{0}\)-norm of the matrix; the rank (\(\mathbf{L}\)) is the rank of the matrix \(\mathbf{L}\). It is truly challenging to tackle the NP-hard task of minimizing the matrix rank function and the \(l_{0}\)-norm because of the matrix's highly nonconvex and nonlinear characteristics. [PERSON] et al. (2019) conducted the convex relaxation to facilitate problem (1) as follows  
\[\min_{\mathbf{L},\mathbf{S}}\|\mathbf{L}\|_{*}+\lambda\|\mathbf{S}\|_{1}\text{ s.t. }\mathbf{D}=\mathbf{L}+\mathbf{S} \tag{2}\]  
where \(\|\mathbf{L}\|_{*}=\sum_{i}\sigma_{i}(\mathbf{L})\) is the nuclear norm, which is represented by the sum of the singular values in matrix \(\mathbf{L}\). \(\sigma_{i}(\mathbf{L})\) is the singular value of matrix \(\mathbf{L}\). \(\|\cdot\|_{1}\) is the \(l_{1}\)-norm of the matrix.
# A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data
## 3 Methods
### A Dual Bayesian Linear Regression Model with Feature Selection  
#### 3.1.1 Model Definition  
The proposed model borrows some ideas from the Bayesian Principal Component Analysis (BPCA) [30] and Bayesian Canonical Correlation Analysis (BCCA) [31] algorithms to endow a Bayesian Linear Regression (BLR) [32] with a dual formulation able to carry out automatic feature selection over the primal variables. This relies on including an ARD prior over the weight matrices to automatically infer the feature relevance in the input feature space by assigning higher/lower relevance values when there are more/less relevant features. Meanwhile, the model works with a formulation in the dual space. In turn, this allows the model to efficiently deal with large data problems by working in the data space while it applies a feature selection over the variable space. In addition, we can exploit the DBL-FS Bayesian formulation to facilitate including prior expert knowledge to guide the FS process. This way, we can guide the learning process and compensate the limited number of samples to train the model.  
To define the model, let us consider **X** as the observation matrix with the MRI information of \(N\) subjects; this way, each row, \(\textbf{x}_{n,i}\): for \(n=1,\ldots,N\), is a \(D\)-dimensional vector containing the brain image of the \(n\)-th subject, and each column, \(\textbf{x}_{,d}\) for \(d=1,\ldots,D\), contains the information of the \(d\)-th voxel over the \(N\) subjects. On the other hand, the column vector **y** represents the diagnosis labels (control or schizophrenic) for the \(N\) subjects under study. Although each label, \(y_{n}\), belongs to the set \(\{0,1\}\) (indicating the subject is control or not), for the model formulation, we consider \(y_{n}\in\Re\), and thus, we will generalize the model for regression problems. Later, we will apply a threshold over the model output to classify each subject into one of two categories.
## 1 Introduction
Despite the numerous arrhythmia classifiers in the literature [22; 23; 23; 24; 25; 26; 27; 28; 31; 32; 33; 34; 35; 36; 37; 38], some of which have outperformed state-of-the-art methods [22; 23; 31; 32], the need for high-speed and hardware-compatible classifiers still persists. Figure 1: ECG signal of one heartbeat. Reservoir computing (RC) models [39; 40] provide a potential solution for computation that can overcome the limitations of conventional methods when it comes to execution speed and physical implementation [41; 42]. These models present new opportunities and advantages that can improve automatic heartbeat classifiers and relieve various constraints, such as training speed and hardware compatibility. RC models have been widely applied to various applications that involve the analysis of ECG signals. These applications comprise ECG de-noising [43], ventricular heartbeat classification [44], stress detection [45], and arrhythmia detection [46; 47]. Next-generation reservoir computing (NG-RC) [48], which is based on nonlinear vector auto-regressive (NVAR) transformation, has shown promising results for forecasting complex dynamic systems. Extensive research has been conducted on reservoir computing (RC) [49; 50], providing detailed explanations of the remarkable success of RCs and leading to the development of NG-RC. However, there has been insufficient research focused on the utilization of NG-RCs for arrhythmia classification. RCs are potentially considered for real-time heartbeat classification due to their potential for offering a fast, scalable, and reliable solution [44; 46; 47; 51; 52]. However, they have only been considered for binary classification. Their exploitation for multiclass patient-independent heartbeat classification remains an under-investigated avenue. This study aims to explore traditional RC (linear RC and nonlinear RC) and next-generation RC for multiclass patient-independent heartbeat classification. By cascading RC and NG-RC, the study also aims to improve the classification performance while preserving the inherent RC's computing potentiality and hardware amenability. This means that RC's feature extraction and light training processes must not be burdened by additional trainable models.
# Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price
## 5 Cloud Task Scheduling Algorithm Based on Branch-and-Price
### Generation of Initial Solution  
#### 5.1.1 Algorithmic Procedure  
The quality of the initial solution has a significant impact on the performance of the branch-and-price algorithm. A good initial solution can effectively reduce the number of iterations in the column generation solution process, thereby improving the execution speed of the branch-and-price algorithm. The generation strategy of the initial solution for the root node and child nodes in the branch-and-price algorithm search tree is different. The strategy for the root node usually needs to be constructed based on the characteristics of the model, while the child nodes can use the feasible columns on the parent node as its initial columns. Therefore, the quality of the initial solution generation algorithm at the root node is crucial.  
To improve the solving speed of the algorithm, we first use the improved Bron-Kerbosch algorithm proposed in [25] to identify all task conflict maximum cliques based on the task conflict graph \(G\) at the root node. Then, we add conflict constraints based on maximum cliques to the subproblems of the algorithm, thus reducing the number of constraints and accelerating the algorithm's solving speed. Meanwhile, the improved Bron-Kerbosch algorithm is also used to find the complete partition of the maximal independent set of tasks. According to the characteristics of multidimensional resources in our proposed model, we introduce the idea of the dominant resource proportion [26; 27; 28; 29] in the multiresources fair allocation to improve the famous first fit decreasing [30] algorithm. The improvedalgorithm allocates VMs to the tasks in each independent set partition. The definition of the dominant resource proportion is as follows:  
\[dr_{j}=\max_{r}\frac{d_{jr}}{c_{r}} \tag{17}\]  
A larger \(dr_{j}\) indicates that task \(j\) has a higher requirement for its dominant resources. The steps for generating the initial solution algorithm at the root node are as follows:  
Step 1 Use the improved Bron-Kerbosch algorithm to determine all task conflict maximum cliques and all complete partitions of task maximal independent set.  
Step 2 If all task-independent sets have been processed, the algorithm will be terminated. Otherwise, retrieve the first unprocessed independent set, and sort the tasks by the nonincreasing order of the task dominant resource proportion.  
Step 3 Enable a VM for the current independent set.  
Step 4 Sequentially traverse the tasks in the current independent set and sort the corresponding deployed VMs in ascending order of index number. Schedule task \(j\) to the VM with the smallest index number, and the remaining capacity of each type of resource is not less than task \(j\)'s demand for this type of resource. If all deployed VMs are unable to deploy the task, skip to Step 3. Until all tasks of the independent set have been deployed, skip to Step 2.
## 2 State of the Art
### Methods for Optimal Layout Problems
#### 2.1.2 Heuristic Methods  
In order to obtain results in a reasonable computational budget in comparison to exact methods, a lot of research has focused on heuristic methods. Heuristic algorithms, while lacking a guarantee of optimality, are dedicated to the search for interesting solutions. One of the oldest heuristics is the Construction Method. The principle of construction-type heuristics is to build a single solution from scratch by iteratively selecting and locating the items until the layout is complete. Two representative construction-based heuristic algorithms are the CORELAP [70] and the ALDEP algorithms (Automated Layout Design Program) [31; 32; 33]. Another type of heuristic mechanisms corresponds to the Improvement algorithms, which start with an initial feasible layout and then try to improve it by exchanging items. The most widely used improvement-based heuristic procedure is the CRAFT (Computerized Relative Allocation of Facilities Technique) algorithm, which has been successfully applied to many different optimal layout problems [35; 36; 71]. Certain studies have integrated both construction and improvement heuristics as in [72; 73; 74] through the development and application of a greedy randomized adaptive search procedure (GRASP) to various optimal layout packing problems.
## 2 Engineering Overview and Finite Element Model Establishment
### Bridge Finite Element Modeling  
In this paper, SAP2000 v22 software is used to establish a three-dimensional finite element model of the whole bridge dynamic analysis, as shown in Figure 3. According to the principle of capacity protection, in addition to piers, bearings, and other components entering the plastic state under a strong earthquake, the main beam, cap beam, and other components are in the elastic working area. Therefore, the main beam and cap beam are simulated by a spatial linear elastic beam-column element. For the column pier used in this paper, plastic hinges are often formed at the bottom of the pier, and PMM fiber hinges are used for simulation. When defining the hinge plasticity, the  
Figure 1: Overall arrangement of the continuous girder bridge across the fault (unit: cm).  
steel fiber, confined concrete fiber, and unconfined concrete fiber, as shown in Figure 4. For the material constitutive of the section fiber, the Mander model [30] is used to define the stress-strain constitutive relationship of the constrained and unconstrained concrete, as shown in Figure 5. The Giuffre-Menegotto-Pinto model [31] is used to define the stress-strain constitutive relationship of the steel fiber, as shown in Figure 6. The pile-soil interaction is simulated by adding a spring with 6 degrees of freedom at the bottom of the cap. The six spring stiffnesses are vertical stiffness, anti-push stiffness in the longitudinal and transverse directions, anti-rotation stiffness around the vertical axis, and anti-rotation stiffness around the two horizontal axes. The second stage, dead load, is considered by applying a concentrated mass to the main beam node. The damping ratio of the structure is 5%, and Rayleigh damping is assumed. The nonlinear dynamic time history analysis uses the Newmark-\(\beta\) direct integration method, where \(\gamma\) = 0.5, \(\beta\) = 0.25.  
Figure 3: Three-dimensional finite element model of the bridge.  
Figure 2: Transverse section of the bridge members: (**a**) bridge pier, (**b**) main beam, (**c**) shear key.  
In Figures 5 and 6, the abscissa is the strain of the material, and the ordinate is the stress of the material; \(f_{c}\) and \(\varepsilon\) are the peak stress and peak strain of unconstrained concrete, respectively; \(f_{cc}\) and \(\varepsilon_{cu}\) are the peak stress and peak strain of confined concrete, respectively; \(E_{c}\) and \(E_{scr}\) are the initial elastic modulus of concrete and the secant elastic modulus corresponding to the peak stress, respectively. \(E_{0}\) and \(E_{1}\) are the initial stiffness and yield stiffness of the steel bar, respectively, and \(\varepsilon_{y}\) is the yield strain of the steel bar.
# Failure Mechanism and Structural Analysis of Chain Slings with Non-Standard Connections
## 3 Failure Cause Analysis
### Comprehensive Review of Chain Failure (Cause)
#### 3.6.2 Use of Chains Without Considering Safety Factor Requirements  
Chain slings for lifting operations must be selected with an adequate Safety Factor relative to the working load, in accordance with relevant regulations and technical standards. In this case, the chain slings were used without considering the appropriate safety factor, ultimately resulting in chain fracture when the actual load of the lifted object exceeded the chain's capacity.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 3 Model
### Attacker Model
#### 3.1.1 Link and Path Discovery  
The attacker creates a network diagram based on the existing satellite link and the distribution of controlled nodes. For each controlled node, uplinks and downlinks are added to its neighboring satellites. Then, for any two ordered combinations of controlled nodes, the Dijkstra algorithm is used to determine their shortest transmission path.
## 3 Research Method
### Reliability and Validity Analyses  
Using the results of the pre-test conducted as a part of the pilot study, the issues of questionnaire item clarity, item completeness, and adding more items were worked on. GPP were determined as 47 questions in total, EP and FP was determined as 11 questions, and the questionnaire was composed of 58 questions in total. At the end of interviews, held with one person from each company, the study sample was established with 457 companies, above the minimum sample size of 384. The testing performed using the IBM software package SPSS (Statistical Product and Service Solutions) version 22 resulted in terms of reliability. Through outliers' analysis, the 406 th and 407 th questionnaires were removed, starting from the one with the highest number of extreme values [69], and the repeated reliability testing resulted in Cronbach Alpha (\(\alpha\)) values over 0.70 [38] for all of the variables and required ranges for normality (\(\pm\)2.0) were achieved [70]. Validity testing for the GPP variables was performed using the responses given to 47 survey questions. Items with a factor load below 0.50 were dropped off from the scale as they did not contribute to the applicability of the scale [71]. The repeated validity test after item elimination resulted in a KMO sampling adequacy value above 0.90 for GPP, EP, and FP [50]. According to Bartlett's Test, Sig. 0.000 is meaningful, and a high correlation (\(p<0.01\)) appears to exist between all of the variables [72]. As a result of these analyzes, 22 items were obtained for the GPP, shown in Appendix A.