esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
They stated that the utilization of the SSI effect makes the entire isolation system more flexible, its natural vibration frequency slightly decreases, and its seismic response increases substantially. [PERSON] et al. [15; 16] further compared the seismic responses of isolation structures among rigid, hard soil and soft interlayer foundations using the shaking-table test. The results showed that the SSI effect has different influences on the seismic responses of the isolated structures under different foundations; however, the effect on hard soil seismic-isolation structures was not obvious. [PERSON] et al. [17] studied the seismic-response characteristics and isolation effects of interlayer isolation structures under the rigid and soft soil foundation conditions using the shaking-table test. Their results showed that the natural vibration frequency and isolation efficiency of the isolated structures decreased significantly while the damping ration increased significantly with the softening of the foundation soil. [PERSON] et al. [18] conducted a bidirectional shaking-table test of eccentric-foundation isolation structures with rigid and soft soil foundations. The results showed that the interlayer torsional response of the isolation structure decreases to some extent while the torsion angle of the isolation layer increases significantly when considering the SSI effect. In summary, theoretical and numerical simulation studies on soil-isolation structure interactions have advanced, and the corresponding model tests have progressed. However, most of the relevant studies are based on the type of foundation isolation, and few have been conducted on interlayer isolation structures. Nevertheless, studies on the seismic-response laws of interlayer isolation structures coupled with long-period ground motions and the SSI effect are insufficient and lack data supported by the shaking-table test. In this study, the shaking-table test and numerical simulation were used to investigate the seismic-response changes of rigid interlayer isolation models and soft soil interlayer isolation models under near-field pulse, far-field quasi-harmonic ground motions (collectively referred to as long-period ground motions), and near- and far-field ordinary ground motions (collectively referred to as ordinary ground motions). The research results can effectively support the construction of seismic-isolation buildings on a deep soft soil foundation in the coastal area of southeast China, and the application range of the propose methods can be expanded to building isolation technology.
# Priority Multi-Server Queueing System with Heterogeneous Customers
## 4 Stationary Distribution. Performance Measures
\right)\) gives the probability that an arbitrary time all server are busy with priority customers and the underlying process of the \(MMAP\) is in the state \(\
u,\
u=\overline{0,W}\). Multiplying this vector by \(\frac{D_{1}\mathbf{e}}{\lambda_{1}}\), we obtain the probability that at the moment of the priority customer arrival all servers are busy. In this case, the priority customers is lost. * The probability that a non-priority customer will be lost due to lack of free servers at the moment of his/her arrival \[P_{loss,2}^{input}=\frac{1}{\lambda_{2}}\mathbf{p}_{N}\left( \begin{array}{ c}I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{0}M_{2}^{N}}\\ I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{1}M_{2}^{N-1}}\\ \vdots\\ I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{N}M_{2}^{0}}\end{array} \right)D_{2} \mathbf{e}.\] (4) Formula (4) is explained similarly to Formula (3). * The probability that an arriving priority customer pushes out a non-priority customer from the server \[P_{loss,2}^{serv}=\frac{1}{\lambda_{1}}\mathbf{p}_{N}\left( \begin{array}{ c}I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{0}M_{2}^{N}}\\ I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{1}M_{2}^{N-1}}\\ \vdots\\ I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{N-1}M_{2}^{1}}\\ I_{\tilde{W}}\otimes\mathbf{0}_{M_{1}^{N}M_{2}^{0}}\end{array} \right)D_{1} \mathbf{e}.\] (5) The brief explanation of Formula (5) is as follows. The probability \(P_{loss,2}^{serv}\) is calculated by considering the situation at the moment of an arrival of a priority customer which meets all servers busy and at least one server occupied with a non-priority customer. The \(\
u\)th entry of the vector \(\mathbf{p}_{N}\left( \begin{array}{c}I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^ {0}M_{2}^{N}}\\ I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{1}M_{2}^{N-1}}\\ \vdots\\ I_{\tilde{W}}\otimes\mathbf{e}_{M_{1}^{N-1}M_{2}^{0}}\end{array} \right)\) gives the probability that at an arbitrary time all servers are busy, at least one server is occupied with a non-priority customer and the underlying process of the \(MMAP\) is in the state \(\
u,\
u=\overline{0,W}\). Multiplying this vector by \(\frac{D_{1}\mathbf{e}}{\lambda_{1}}\), we obtain the probability that at the moment of a priority customer arrival all servers are busy and at least one server is occupied with a non-priority customer. In this case the priority customer pushes out a non-priority customer from the server and takes his/her place. * The probability that an arbitrary customer arriving to the system will be lost due to the lack of free servers \[P_{loss}=\frac{\lambda_{1}P_{loss,1}+\lambda_{2}P_{loss,2}^{input}}{\lambda}.\] (6) The numerator in the right hand side of Formula (6) is the rate of lost customers of two types and the denominator is the total input rate. The probability \(P_{loss}\) is calculated as the ratio of these rates.
## 3 Results and Discussion
### Tungsten Uptake by Plants
The soil plant barrier concept [39] does not appear to be entirely applicable to tungsten. In fact, tungsten is not strongly held by the soil surfaces and can be absorbed by the plant roots. Figure 1: Trend of translocation factor at increasing tungsten concentrations in soils. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**W in Soil (mg kg\({}^{-1}\))**} & \multicolumn{2}{c}{**Shoot**} & \multicolumn{2}{c}{**Root**} \\ \cline{2-7}  & **Soil A** & **Soil B** & **Soil C** & **Soil A** & **Soil B** & **Soil C** \\ \hline
10 & 0.12 \(\pm\) 0.02a & 0.45 \(\pm\) 0.04b & 2.69 \(\pm\) 0.02c & 1.30 \(\pm\) 0.07a & 2.38 \(\pm\) 0.6b & 5.10 \(\pm\) 0.4c \\
30 & 0.34 \(\pm\) 0.08a & 1.26 \(\pm\) 0.3b & 5.42 \(\pm\) 0.4c & 2.04 \(\pm\) 0.3a & 6.50 \(\pm\) 0.9b & 19.8 \(\pm\) 1.1c \\
40 & 0.98 \(\pm\) 0.07a & 3.05 \(\pm\) 0.5b & 11.7 \(\pm\) 0.8c & 4.51 \(\pm\) 0.4a & 9.80 \(\pm\) 2.3b & 45.3 \(\pm\) 3.8c \\
50 & 1.26 \(\pm\) 0.08a & 3.98 \(\pm\) 0.3b & 13.5 \(\pm\) 0.6c & 5.15 \(\pm\) 0.8a & 13.6 \(\pm\) 2.1b & 52.6 \(\pm\) 3.0c \\
100 & 1.80 \(\pm\) 0.04a & 5.43 \(\pm\) 0.6b & 21.0 \(\pm\) 1.3 c & 7.10 \(\pm\) 0.9a & 18.6 \(\pm\) 4.2b & 95.5 \(\pm\) 6.1c \\ \hline \hline \end{tabular} Note: At each tungsten soil concentration, values with different letter in the same rows separately for shoots and roots are statistically different at \(p<0.05\).  
\end{table} 
Table 3: Tungsten concentration in shoots and roots of _Zea mays_ grown in the different spiked soils. Values are the mean \(\pm\) standard deviation.
## 1 Introduction
They demonstrate that time series can be decomposed into trend, seasonal, and remainder components and the time and number of changes can be detected at high temporal resolution (_i.e.,_ 16 days). In a recent study, [PERSON] _et al._[16] proposed a user-friendly program, Detecting Breakpoints and Estimating Segments in Trend (DBEST), to characterize the time and magnitude of changes in vegetation trends from NDVI time series using a segmentation algorithm. They tested the program with bimonthly NDVI time series at 8-km spatial resolution. The objective of this study is to develop a new approach for detecting change points of abrupt land-cover changes at sub-annual scale using dense time series of satellite observations. We propose a sub-annual change detection approach to detect the dates of change (change dates, henceforth) and account for seasonal variations in time series of image data. Using reference and simulated time series of MODIS 16-day NDVI from 2000 to 2012, we assessed the performance of the proposed method on the accuracy of change date detection and compared the results with the trend and seasonal changes detected by the BFAST [15] method.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.8 Impact of Mobility Pattern  
Figures 12 and 13 study the impact of the underlying mobility pattern on coverage and average end-to-end delay, where the linear mobility pattern is defined as:  
\[\begin{cases}V_{x}=k_{1}t+k_{4}\\ V_{y}=k_{2}t+k_{5}\end{cases} \tag{17}\]  
Here, \(V_{x}\) and \(V_{y}\) represents the velocity of a sensor node along the \(x\) and \(y\) axis, respectively. \(k_{1}\) and \(k_{2}\) are parameters characterizing environmental factors such as tides and bathymetry. \(k_{4}\), and \(k_{5}\) simulate additional random factors such as rates of drift. For the purpose of simulation, \(k_{1}\) and \(k_{2}\) are normal random variables with mean \(\pi\) and variance \(0.1\pi\), while \(k_{4}\) and \(k_{5}\) are normally distributed with mean 0.1 and variance 0.01. Whenever the velocities reach a value greater than 5 m/s, the sign of \(k_{1}\) and \(k_{2}\) are reversed to become negative. When the velocities reach zero, \(k_{1}\) and \(k_{2}\) will change, becoming positive. The sign reversal occurs to simulate the movement of tidal.  
IMM is applied to track the moving of sensor nodes, in this simulation, the same IMM model is used to track sensor nodes with three different moving patterns, including random walk, which is less predictable, to show the impact from different tracking accuracy. The results reveal that the linear mobility model enables better performance than the kinematic model in Equation (16) and random walk in terms of coverage and end-to-end delay because the IMM predicts the linear pattern more easily, enabling better tracking. Thus, the future node positions are predicted more precisely, resulting in better performance. Despite the complexity of the kinematic model and total randomness of the random walk approach that affect the accuracy of predicted positions, the IMM performs very well in these nonlinear cases. Thus, PADP achieves performance comparable to the linear mobility model despite the introduction of these more sophisticated patterns of sensor node mobility.  
Figure 11: Impact of monitoring interval.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Preference Classification Algorithms
DNNs are gatherings of the artificial neurons organized in layers to estimate the nonlinear resolution border. The most popular type of DNN used for BCIs is the multi-layer perceptron (MLP), which normally consists of only one or two hidden layers. Other DNN types have been explored less frequently, such as the Gaussian classifier neural networks or learning vector quantization neural networks [57]. Furthermore, [PERSON] et al. [55; 56] proposed deep learning approaches for preference recognition by using 3D rotating objects. The results prove that the use of the deep network could obtain a higher accuracy compared to that of the other machine learning classifiers, such as the SVM, RF, and KNN algorithms. In their extended research, [PERSON] _et al._[23] improved the result accuracies by using a deep network plus dropout architecture, with rectified linear units and tanh for activation at 79.76%. Table 6 presents some neuromarketing studies that used different classification algorithms to obtain the most accurate results in predicting the consumer preferences. The review highlights the need to use more features and hybrid classifiers to improve the accuracy results of the predictions [22; 44].
## 2 Materials and Methods
### Frost Experiments  
A mold measuring 28 cm in diameter and 23 cm in height was used for the frost experiments. The mixtures were prepared and placed into a mold following the sample preparation method used to measure the dry unit weight of the mixtures. Thermocouples were inserted at specimen heights of 30% and 70% to monitor the temperature change in the mixture (Figure 4). As the mixtures are gravelly soils, most of the added water (15 \({}^{\circ}\)C) fills the void of the mixture from the bottom of the experimental mold rather than being uniformly distributed throughout the entire specimen. A scour pad was placed on the surface of the mixture. A specific volume of water was prepared based on the target degree of saturation (\(S\)). Half of the prepared water was sprayed over the mixture surface at an infiltration rate of 0.001 cm/s to simulate uniformly wet conditions of the mixture. The remaining water was percolated along the inner wall of the mold to prevent the mixtures from forming excessive preferential hydraulic paths and washing.  
The preliminary tests showed no frost heaving when \(S\) was less than 55%. This is because when the volume of the pore fluid is low, water merely freezes and migrates to unsaturated pores, causing no increase in volume [25]. In addition, during freezing, water theoretically expands by \(-9\%\) compared to its initial volume. Therefore, unsaturated conditions of \(S=70\%\), 80%, and 90% were selected in this study to examine the frost action of gravel ballast-subgrade soil mixtures. The saturated condition (i.e., \(S=100\%\)) was omitted because gravel ballast is rarely saturated.  
Figure 3: Dry unit weight and porosity of mixtures.  
After preparing the specimens, they were transferred to a cold chamber and kept at \(-25\)\({}^{\circ}\)C for 65 h under undrained conditions. For the frost heaving test, a plate was placed on the surface of the mixture, and a displacement transducer (_PY2 LVDT_, _Gefran_, Provaglio d'Iseo, Italy) was used to measure the upward displacement at the middle of the plate, assuming 1D heaving (Figure 4a). For the frost heaving pressure test, the mixture was placed in the same manner as the frost heaving setup; however, the plate on the mixture surface was confined by a load cell connected to a rigid reference frame (Figure 4b). The change in pressure was monitored during the freezing phase. The assumptions on the confinement were intended to measure the maximum frost heaving and maximum frost heaving pressure.  
The experimental details are summarized in Table 2. For instance, 'h-M10-S70' indicates a frost heaving test for a mixture with a mixing ratio of 10% (10% w/w subgrade soil and 90% w/w gravel ballast) and a degree of saturation of 70%. For the frost heaving tests, \(M\) = 0%, 5%, 10%, 15%, 20%, and 25% with \(S\) = 70%, 80%, and 90% were used. For the frost heaving pressure tests, reduced cases such as \(M\) = 0%, 5%, 15%, and 25% with \(S\) = 70% and 90% were used because of limitations with respect to the laboratory conditions.
## 4 Materials and Methods
### Exploratory Data Analysis (Step 1)  
We performed exploratory data analysis (EDA) on the PROMISE repository from the year 2020. The essential purpose of EDA is spotting the missing and erroneous data, mapping and understanding the underlying structure of the data, and identifying the most important variables in the dataset. The PROMISE is an open-source repository that is widely used for defect prediction studies. The PROMISE repository has a total of 45 versions consisting of 11 different projects. Among these 45 projects, we have ignored all those versions of projects whose output is binary class, as we are conducting an experiment on a multi-class problem. After removing the binary projects, we are left with 35 versions of 11 projects. These projects include Ant, Camel, Ivy, Jedit, Log4j, Lucene, Poi, Synapse, Velocity, Xalan, and  
Figure 1: Our proposed methodology. Xerces. Each version of the project contains 20 features that are described in Table 2. These object-oriented attributes are used as the independent variables and bugs are used as the dependent variable representing the defective class. After the EDA, we came to know that the PROMISE repository is of a multi-class instead of a binary class and has an issue of noise, a distribution gap, and a class imbalance. The following figures show the variation in the data along with the presence of multi-class data in multiple versions of the projects. In Figures 2-4, the x-axis represents the class of the bug, and the y-axis represents the total number of rows for the respective class type. The bar height across each class mentioned the total number of instances for the specific class in Ant version 1.6, Xerces version 1.3, and Xerces version 1.4, respectively. From Figures 2-4, it is obvious that the PROMISE dataset is multi-class in nature, so, we treat the dataset as multi-class during the data normalization.
## 3 Method
### Feature Extraction and Fault Diagnosis  
The fault diagnosis and prognosis methodology for the DC motor's gear system involves a detailed analysis of features extracted from the motor's current signals. Various techniques for feature extraction, such as time-domain and frequency-domain analysis, are explored. This study's proposed methodology focuses on utilizing a boosting algorithm with weak classifiers for fault diagnosis and prognosis. During diagnosis, an ensemble classifier is trained on the extracted features from the motor current signal. The ensemble classifier is adept at classifying the present fault state of the machine based on the extracted features. As for prognosis, the ensemble classifier is utilized to anticipate future fault severity conditions. By amalgamating predictions from multiple weak classifiers trained during the diagnosis phase, the boosting algorithm computes the most probable next state and assesses the severity of potential faults. The weights of the weak classifiers are determined during the training phase of the boosting algorithm, where classifiers exhibiting better performance are assigned higher weights. This meticulous weight allocation ensures that well-performing classifiers contribute more significantly to the ensemble's decision-making process. The proposed methodology offers a reliable and comprehensive approach to fault diagnosis and prognosis, leveraging ensemble learning techniques to provide accurate predictions of fault severity in machine health monitoring, thereby enhancing reliability and efficiency in industrial operations.
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 1 Introduction
* We propose the structure of the feature-attended LSTM, design a novel algorithm to classify the features into different groups, and present the detailed schemes to extend LSTM from a single training channel into multiple training channels so that the correlation among the features can be fully utilized. * We apply the federated learning architecture to solve problems like the lack of training data faced by single detection devices and enhance the synergy of detection devices. According to the traffic characteristics of each detecting node, the correlation is analyzed, and the parameter aggregation strategy in cooperative training is optimized to improve the detection models. The remainder of this paper is organized as follows. The related work is introduced in Section 2. In Section 3, the architecture of feature-attended federated LSTM is demonstrated. Section 4 shows the detailed schemes in the proposed architecture about feature-attended LSTM, while the specific framework for feature-attended federated learning is described in Section 5. In Section 6, the UNSW-NB15 dataset is simulated as a case to verify the performance of the proposed architecture which is compared with standard LSTM in individual training and in conventional federated average training. Finally, Section 7 concludes this paper.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 3 Materials and Methods  
This section is arranged as follows: Octree and 3D R*-tree are described firstly, a new encoding method is proposed to associate 3D R*-tree with octree. Furthermore, then the hybrid structure is proposed to improve the performance of the spatial index. Finally, a \(kNN\) searching algorithm is designed refer to the hybrid structure. The \(kNN\) searching is a basic algorithm to process Point Cloud data, such as normal estimation, feature extraction, etc. So, \(kNN\) is mainly used as a performance evaluation index of different structures. Two types of Point Cloud data are used in the next section, randomly generated and acquired by an RGB-D camera scanning a lab. Random data is used to test the performance under different data sizes (1K, 4K, 16K, 64K, 256K), where 1K = 1000 points.
# A Simhash-Based Integrative Features Extraction Algorithm for Malware Detection
## 1 Introduction  
In recent years, the quantity of malware has increased significantly, and new types of malware [1; 2; 3] have steadily emerged, creating severe challenges for cyberspace security. Therefore, it is critical in the field of malware detection to quickly analyze malicious samples and extract their real and effective features to form the detection model [4]. The existing sample analysis technology mainly includes static analysis and dynamic analysis. The static and dynamic features of malicious samples can be extracted separately by analysis [5; 6].  
The static feature is formed by analyzing the structure and format of the sample and then extracting the hash value, string information, function information, header file information, and resource description information. The technology obtains most of the malware information from the malware itself, thus the analysis results are relatively comprehensive. However, static features cannot correctly discriminate malware when the static information is packed or obfuscated or compressed [7], making it difficult for static features to express the true purpose of malware, thus affecting the accuracy of detection.  
Dynamic features are the behavior of the sample execution and the features of the debug record, such as file operations, the creation and deletion of processes, and other dynamic behaviors. Since the malicious behaviors of malware at dynamic runtime can't be concealed, the extracted dynamic features provide a more realistic description than the static features. However, the extraction of dynamic features needs to be run in a virtual environment [8], which will be reset and restored to the previousstate after each malicious sample has be analyzed to ensure that the virtual environment is a real user environment. As a result, features extraction efficiency is much lower than for static features.  
It can be seen from the above that it is difficult for one aspect of training and testing to make up for the other defects. Therefore, the research of integrative features aims to integrate the advantages of various types of features; this is a current trend in the development of malware detection technology [9; 10; 11]. Integrating the efficiency of static features and the authenticity of dynamic features can optimize both the performance of features and the spatio-temporal performance of features for classification training. To this end, this paper extracts integrative features of static and dynamic features based on simhash. On the basis of the simhash algorithm, the API function information of the static analysis is merged with the file, process, registry and network behavior information of the dynamic analysis to form integrative features. This algorithm can further improve the training speed of the classification model, as well as achieve better classification and obfuscated-detection results.