esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Intruder Reconnaissance Behavior through State Diagrams to Support Defensive Deception
## 1 Introduction
On those paths, a number of metrics are calculated with respect to the probability that the intruder obtains or does not obtain specific information about the network, along with the estimated time until this happens. The contributions of the present paper are the following:  
* A hierarchical set of state diagrams that model all different actions of an intruder during reconnaissance. * A flexible syntax to model the protected network structure and existing partial knowledge about the methods an intruder follows. * An algorithm that uses the previously mentioned models to produce a set of possible attack paths an intruder may follow. * The definition of metrics for the probability that an intruder obtains a specific piece of information and for the number of steps required to do so. It has to be noted that the proposed methodology is part of a wider research effort to construct an automated deception mechanism that will mitigate reconnaissance in a network. The present paper is the first step towards that goal and sets the foundation for modeling and measuring the reconnaissance activity to apply deception techniques to mitigate it. To the extent of our knowledge, there is no other systematic approach to model the behavior of an attacker during the reconnaissance step of an attack and we hope that this work serves as the basis for multiple future research efforts regarding network reconnaissance. As mentioned above, this paper forms the basis of a mechanism to deceive an intruder. By knowing all the possible paths for host discovery techniques for a computer network, we can influence the attacker to take different paths before they reach their target. By applying deception techniques, we can achieve the following:  
1. Increase the intruder's workload;
2. Allow defenders to better track attacks and respond before adversaries succeed;Exhaust adversary resources;
* 4 Increase the sophistication required for an attack;
* 5 Increase the intruder's uncertainty. This paper is structured as follows. Section 2 presents related work in the fields of host discovery and port scanning operations. In Section 3, the main techniques for host discovery and port scanning reconnaissance activities are presented. In Section 4, our modeling approach that aims at enabling a detailed representation of an intruder's reconnaissance activity is presented. In Section 5, an implementation based on the model described in Section 4 is presented. In Section 6, we performed a series of experiments in order to find out how fast the mechanism described in Section 5 produces its results. Finally, Section 7 presents our conclusions and our future work based on this paper.
## 2 Data and Pre-Processing  
### Sentinel-2 Images  
Sentinel-2A MSI data are acquired in an approximately 290 km swath (20.6\({}^{\circ}\) field of view from an altitude of 786 km) with global coverage every 10 days [9]. The data are available as geolocated L1C top of atmosphere (TOA) products defined by splitting each MSI swath into fixed 109 \(\times\) 109 km\({}^{2}\) tiles in the Universal Transverse Mercator (UTM) map projection. Pairs of predominantly cloud free L1C images acquired before and after major fire events were selected by comparison of the Sentinel-2A data archive with the global daily Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km active fire detection (MODIS Near Real-time Collection 6) available in the Fire Information for Resource Management System (FIRMS) web system [37]. Due to the sometimes rapid change in post-fire reflectance, attempts were made to ensure that only pairs of Sentinel-2 images acquired within the shortest interval, i.e., 10 days, apart were selected. However, this was not always possible due to cloud obscuration and Sentinel-2 data unavailability.  
Pairs of images at five sites located in the Northern Territory, Australia; S.E Cambodia close to the Vietnam border; over the recent Fort McMurray fire in Alberta, Canada; in central Guinea; and in north central Colombia, were selected. The sites are in tropical, sub-tropical and boreal regions. No temperate sites were available because the first Sentinel-2A data became publicly available in November 2015, which was after the end of the main global temperate fire seasons [38]. The predominant land cover, as defined by the 2013 Collection 5.1 MODIS 500 m land cover product [39], over each site where unburned and burned pixels were analyzed is summarized in Table 1.
## 5 Experiments and Result  
### Dataset  
We used the VoxCeleb2 dataset [9] for training, and the VoxCeleb1 dataset [10] for evaluation. The VoxCeleb2 dataset comprises over one million utterances from 6112 speakers. We exploited three test sets. The original test set (Vox1-O) consists of 37,611 enrollment-test utterance pairs from 40 speakers. Second, the extended test set (Vox1-E) contains a list of 579,818 pairs. Finally, the hard test set (Vox1-H) comprises a list of 550,894 pairs with the same nationality and gender. The Vox1-E and Vox1-H test sets contain 1251 and 1190 speakers from the entire VoxCeleb1 dataset. Every utterance is recorded in mono with a 16 kHz sampling rate and 16-bit resolution. In addition, we augment the input data using room-impulse response simulation and MUSAN corpus [37].
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 3 Reliability Analysis Process of Gas Monitoring System Based on Fuzzy-FTA  
### Failure Factor Analysis of Gas Monitoring System  
The main causes of hardware failure in coal mine gas monitoring systems are industrial computers and monitoring substation failures, sensor failures, communication line failures, power failures, etc. According to the analysis of data from mine gas monitoring systems, a number of accidents were taken as the top events, and their causes were analyzed [24].  
In Table 1, B1-B5 are the intermediate events of the accident. B1-B4 are the hardware factors of the gas monitoring system failure, B5 is the human factor of the gas monitoring system failure. B1 is the monitoring computer failure, including the industrial computer host failure, display failure, and monitor substation failure. B2 is the monitoring system sensor failure, including the gas sensor failure, temperature sensor fault, wind speed sensor fault, power sensor failure, switch sensor failure, breaker failure, and PLC controller failure. B3 is monitoring system line failure, including the broken line, self-aging short circuit, and poor line contact. B4 is the monitoring system power supply and communication failure, including the abnormal power failure, high communication error rate, and short circuit to ground. B5 is the monitoring staff mistakes, including the lack of responsibility of the staff, system lack of spare parts, and insufficient maintenance technology.
# Parallel Computation for Inversion Algorithm of 2D ZTEM
## 1 Introduction  
The ZTEM (Z-axis tipper electromagnetic method) method is a native electromagnetic field exploration method. The ZTEM method is developed based on the MT. The abbreviation of magnetotelluric is MT. We can collect magnetic data in a helicopter using the ZTEM method. The method has special advantages in mineral deposit exploration and environmental exploration [1, 2, 3]. We need to spend a great deal of time collecting MT data on the ground when the MT method is applied to explore the area. If we can collect data in a helicopter, we can save more time and money. It is very difficult to collect data on the electric field, but tipper data are available from a helicopter. The tipper is the ratio of the vertical magnetic field and horizontal magnetic field.  
With the development of this instrument, MT data processing technology has matured. The ZTEM method became possible. Domestic and foreign researchers have conducted research on the ZTEM inversion. [PERSON] performed research on 2D ZTEM inversion [4], [PERSON] performed research on 3D ZTEM [5], [PERSON] performed research on a parallel algorithm of 3D MT inversion [6], [PERSON] conducted research on 3D ZTEM inversion [7], and [PERSON] conducted research on 3D ZTEM [8]. The research on the parallel algorithm of 2D ZTEM inversion is limited, so it is essential to perform research on the parallel algorithm of 2D ZTEM inversion. Two-dimensional conjugate gradient inversion is an effective method. The model is divided into an columns and n rows. When the number is large, more computation time is required. The model is divided into 400 \(\times\) 400 in this paper, and the run time is 9804 s. We need to develop a parallel algorithm to solve the problem. First, we introduce the parallel algorithm.  
The rest of this paper is organized as follows. Section 2 introduces 2D modeling and inversion. Section 3 introduces the MPI technology and how to realize the parallel algorithm. Section 4 shows the results and analysis of the parallel algorithm. Section 5 summarizes this paper and proposes an outlook for future work.
# Biomedical Applications of Polymeric Cryogels
## 3 Cryogels in Medical Applications
### Cryogels for Tissue Engineering
#### 3.4.1 Bioreactors
In a study, incorporation of polyallylamine plasma on the polyvinyl alcohol-tetraethylorthosilicate-alginate-calcium oxide (PTAC) biocomposite cryogel surface was examined. Plasma polymerization was applied for obtaining different physical and chemical surface functionality to make changes on bone regeneration potential of human osteoblast cells [63].
# Guidelines towards Information-Driven Mobility Management
## 4 Mobility in ICN
### Consumer Mobility  
To explain how ICN supports consumer mobility, this section provides two examples: MN acting as consumer and CN acting as consumer. The explanation provided is based on the functional entities described in Section 3, which are the basis for today's mobility management reference architectures, onto ICN. The purpose is to explain limitations that may arise from such mapping. MN is an ICN producer that is directly connected to the NDN router B, and in active communication with a consumer CN. Both MN and CN reside in mobile nodes. Between NDN routers A and B there is a distance of \(k\) hops. Router B and D are connected via a distance of \(N\) hops. Connectivity can be intermittent.
## 3 Numerical Analysis Conditions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Unit** & \multicolumn{3}{c}{**Value**} \\ \hline winding & No. turns & & 23 & \\ slot opening & mm & 2.4 & 3 & 3.6 \\ maximum torque & Nm & 17.91 & 17.78 & 17.62 \\ maximum torque rotation range & rpm & 119.9 & 124.3 & 128.1 \\ maximum cogging torque & peak-peak, Nm & 0.0113 & 0.00295 & 0.00774 \\ THD of cogging torque & \% & 0.978 & 2.82 & 1.03 \\ maximum torque ripple & peak-peak, Nm & 0.1175 & 0.0741 & 0.0665 \\ maximum flux density & T & 0.490 & 0.497 & 0.492 \\ power & W & 225 & 231 & 236 \\ efficiency & \% & 28.5 & 29.1 & 29.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Numerical data of 23-turns-per-coil model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Unit** & \multicolumn{3}{c}{**Value**} \\ \hline winding & No. turns & & 25 & \\ slot opening & mm & 2.4 & 3 & 3.6 \\ maximum torque rotation range & rpm & 94.1 & 97.6 & 100.5 \\ maximum cogging torque & peak-peak, Nm & 0.01130 & 0.00296 & 0.00773 \\ THD of cogging torque & \% & 0.978 & 2.80 & 1.03 \\ maximum torque ripple & peak-peak, Nm & 0.1288 & 0.0838 & 0.0740 \\ maximum flux density & T & 0.515 & 0.512 & 0.510 \\ power & W & 191.7 & 197.3 & 201 \\ efficiency & \% & 23.9 & 24.4 & 24.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Numerical data of 25-turns-per-coil model. Figure 13 demonstrates the variation in torque ripple. The solid line represents a 21 turns, the dashed line a 23 turns, and the dotted line a 25 turns model. The maximum torque ripple increased with an increase in winding number but decreased with an increase in slot opening. When the number of turns per coil increased from 21 to 25, the maximum torque ripple increased by up to 29.1%, while increasing the slot opening from 2.4 mm to 3.6 mm resulted in a maximum decrease of 44.0%. This is because the concentration of the induced electromotive force generated in the winding inside the slot weakens as the slot opening increases. Figure 11: Torque variation for each of the number of turns per coil and slot opening conditions. Figure 12: Cogging torque variation for each of the number of turns per coil conditions. Figure 14 shows that mechanical power decreases as the number of windings increases, but increases as the slot opening widens. When the windings increase from 21 to 25, mechanical power decreases by as much as 26.4%. In contrast, when the slot opening is widened from 2.4 mm to 3.6 mm, mechanical power increases by up to 5.03%. Mechanical efficiency decreases as the number of windings increases, but improves as the slot opening widens. When the number of windings increases from 21 to 25, mechanical efficiency drops by up to 28.5%. However, when the slot opening is increased from 2.4 mm to 3.6 mm, mechanical efficiency rises by a maximum of 3.78%. It can be observed that as the number of windings increases, both the motor's output and efficiency decrease linearly. This decrease is likely due to the interaction between torque ripple and permanent magnets caused by an increase in the induced electromotive force.
## 3 Results  
### Distribution of Large Yellow Croaker Aquaculture Area (LYC-A)  
The cage aquaculture regions (Figure 4) for large yellow croakers are distributed across the entire coastal stretch of Zhejiang Province, with the northernmost area situated in Shengsi County, Zhoushan, at approximately 30\({}^{\circ}\)42\({}^{\prime}\)N latitude, and the southernmost located on Nanji Island in Wenzhou, Zhejiang Province, at about 27\({}^{\circ}\)30\({}^{\prime}\)N latitude. The cage aquaculture areas exhibit a compact arrangement. Typically, the horizontal configuration is generally linked by 2 to 4 cages, while the vertical count is determined based on the extent of the culture area.  
Furthermore, based on the spatial resolution of the satellite imagery, it can be deduced that there is variability in the size and shape of the cages within the large yellow croaker cage culture areas (Figure 4). The cages can be categorized by shape into circular (Figure 4) and rectangular designs (Figure 4). The circular cages can be roughly classified by radius into two sizes, approximately 6.5 m and 15 m in diameter (Figure 4). The rectangular cages can be approximated by the side length, with one size being around 44 m (Figure 4). This indicates diversity in the structural dimensions of the cage systems utilized in the aquaculture of large yellow croakers.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 3 Proposed Method
### Kalman Filter-Based Navigation Algorithm  
Once the initial estimates of biases are determined, acceleration and angular rate data provided by the cube can be exploited to determine the navigation parameters of the redundant inertial unit. Different approaches can be adopted to update the values of the biases cyclically; in particular, an extensive review of techniques based on the integration of low-cost INS and GNSS can be found in [31]. In the realized redundant prototype, a loosely coupled integration algorithm, exploiting an Extended Kalman Filter (EKF), has been adopted as data fusion technique. In particular, INS outputs are processed by means of traditional navigation equations in order to achieve an a priori estimate of position, velocity, and attitude of the moving body; the a priori estimates are then updated and corrected each time a corresponding measures is available from the GNSS [32; 33].  
The first operation accounts for the initialization of the values of position and velocity, given by GNSS data. Moreover, initial attitude is estimated through leveling operations [21].  
Successively, the adopted integration Kalman filter algorithm estimates the 15-dimension state vector (involving position, velocity, attitude, bias of accelerometers and bias of gyros) by iteratively operating according to two steps:  
1. Prediction/integration. At this step, the inertial navigation equations are integrated. To this aim, measures of accelerations and angular rates provided by the cube are first corrected from the last available values of bias. This stage provides the so-called a priori estimates of the state vector, i.e., a state vector updated by only integrating the corrected accelerations and angular rates; possible uncompensated biases effects make the estimated navigation parameters diverge from the actual values;
2. Correction through GNSS data. When new measures of position and velocity are available from the GNSS, their values can be exploited to correct the a priori estimates of the state vector. In particular, a suitable matrix, Kalman gain, allows us to weight the confidence between integration and GNSS data and evaluate the so-called a posteriori estimate of the state vector. The higher the values of the Kalman gain, the greater the confidence and successive correction from GNSS measures with respect to the result of the integration stage. Moreover, in this step, the biases responsible for the difference between the integration and GNSS navigation parameters are estimated and given as input for the successive prediction/integration step.  
Figure 4: Initial bias estimation procedure.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 4 BCSO Application to UC
### Applied BCSO to UC
#### 4.2.1 Initialization  
* Set the parameters of the power system such as the load demand, the fuel cost coefficients of the unit, generation capacity and minimum up/down time, etc.;
* Initialize for the parameters of BCSO such as \(\phi\) and maximum iteration time;
* Initialize and boundary check of the particles and their velocity, generate the value of the particle according to Equations (10) and (11), then check the constraints;
## 2 Materials and Methods
### The Design of NeoNet Classifier  
The previous step explained how a network-based training model was derived from a given set of news articles. Here, we present the algorithmic steps that led to labeling a new article that was yet to be seen by our algorithm. The algorithm was controlled by a configuration parameter, which we called: minimum support, which was inspired by the Apriori algorithm [52; 53; 54; 55; 56; 57]. The minimum support guaranteed a certain number of bigrams to be present in each article; otherwise, it would be labeled as suspicious. It ensured that the article contained sufficient contents that contributed to the training model. If the article did not meet this condition it would not be classified as \"safe\". Clearly, an article that does not have a minimum number of TF-IDF features also does not communicate significant facts that makes it worthy of reading [58]. As for the percentage generated by  
Figure 4: Shows a simplified version of the network training model that is constructed from 100 bigrams features (also produced by TF-IDF).  
Figure 3: Shows a wordcloud representation of the top-100 features selected using TF-IDF.  
1. Set the minimum support parameter
2. For each new article to be classified
3. Preprocess as previously described
4. Extract TF-IDF bigram features
5. For each component of the bigram (unigram): split into left-unigram and right-unigram
6. Add the left-unigram to the model: if it connects then add the right-bigram.
7. Otherwise, add the right-unigram to the model: if it connects, add the left-unigram and preserve the bigram order
8. Continue until all bigrams are tested
9. Calculate actual support score
10. If support value is above the threshold parameters classify as POSITIVE and assign a COVID19 label
11. Otherwise, classify as NEGATIVE.  
```
0:\(mini\_sup\), the minimum support value v needed
0:\(G\), a graph training model
1: Initialize \(min\_sup\_id=v\), for all \(d\in D\)
2: Initialize \(position(d)=0\)
3: Initialize \(sup\_count(d)=0\)
4: pt:
5:for each\(d\in D\)do
6:\(bigram\_list\leftarrow extract\_bigrams(d)\)
7: for each \(bigram\in G\_list\)do
8:if left_unigram\(G\in\)then
9: add \(G\gets right\_bigram\)
10: else
11: if right_unigram\(\in G\)then
12: add \(G\gets right\_bigram\)
13: if \(sup\_count\geq min\_sup\)
14:\(positive\leftarrow\) +1
15:\(d\gets COVID19\) label
16:until no more document to classify
```  
**Algorithm 1** NeoNet: A Noun-phrase Bigram-based Classification Algorithm  
Algorithm 1 shows the steps of the NeoNet algorithm in pseudocode staring from when the bigram features were extracted until a classification label was generated.
# Coseismic Stress Change and Viscoelastic Relaxation after the 2008 Great Sichuan Earthquake
## 2 Method and Models
### Model of the Lower Crust and the Upper Mantle  
In this study, the coseismic (static) Coulomb stress changes (\(\Delta\)CFS) caused by earthquake rupture [58; 59] are calculated in an elastic half-space using a Poisson's ratio of 0.25 and a friction coefficient \(\mu^{\prime}=0.6\). Furthermore, to explore the stress evolution over time, a lithologic model is used in which the lower crust and the upper mantle are viscoelastic [49; 55; 56]. Thus, dislocation source models with the code PSGRN are used to calculate the Green's function of the multilayered viscoelastic half-space [60].  
The viscoelastic structure of the lithosphere is incorporated in the Tibetan Plateau, especially in its eastern boundary [61; 62; 63], and the data from geophysical inversion in the Longmenshan region are considered [64]. In this model, the crust and upper mantle are divided into 6 layers. Layers 1 and 2 represent the upper crust, consisting of two elastic layers. Layer 3 is the middle crust, which is a low-velocity layer [64; 65]. Layers 3 to 6 are Maxwell viscoelastic bodies, and layer 3 is identified by geophysical inversion [64; 65]. The layered model and other parameters used in the following calculations are listed in Table 1, where \(\rho\) is the density, \(\mathrm{V_{P}}\) is the P-wave velocity, \(\mathrm{V_{S}}\) is the S-wave velocity, \(\mathrm{H}\) is the thickness, and \(\eta\) is the viscosity of each layer. The shear modulus is assumed to be \(3.2\times 10^{10}\) Nm\({}^{-2}\).