esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Fluid Inclusions  
Fluid inclusion studies were conducted on the quartz coexisting with sulfides in three types of ore samples (Type-1, Type-2, and Type-3) from the Hongtoushan Cu-Zn VMS deposit. This quartz typically interpreted to have formed during the main stage of sulfide mineralization. For simplicity, the fluid inclusions in the quartz from three types of massive ores (Q-T1 MO to Q-T3 MO in table); we also directly refer to them as Type-1 to Type-3 fluid inclusions. A total of 51 fluid inclusions were measured, including 13 from Type-1 ore, 11 from Type-2 ore, and 27 from Type-3 ore. All fluid inclusions are two-phase (V\({}_{H_{2}O}\) + L\({}_{H_{2}O}\)), except for two inclusions from the Type-2 ore that contain halide daughter minerals (V\({}_{H_{2}O}\) + L\({}_{H_{2}O}\) + S\({}_{NaCl}\)) (Figure 5F). The primary fluid inclusions in the three types of massive ores are randomly distributed, and occur individually within the quartz crystals. These fluid inclusions are typically 4-12 \(\upmu\)m in size, mainly exhibit polygonal shapes, and have vapor bubbles occupying \(\sim\)10% (Table 3, (Figure 5B,C,E)). In our observations, we noted the presence of secondary fluid inclusions. These secondary fluid inclusions tend to be small, often measuring less than 5 \(\upmu\)m. They frequently align along specific fractures or cleavage planes, occurring in lines, arrays, or string shapes (Figure 5A,D). Due to their relatively small size, we did not perform microthermometric studies on them.  
Figure 5: (**A**) Primary and secondary fluid inclusions in the quartz of Type-1 massive ores. (**B**) Primary fluid inclusions in the quartz of Type-2 massive ores. (**C**) Primary fluid inclusions in the quartz of Type-2 massive ores. (**D**) Secondary fluid inclusions in the quartz of Type-1 massive ores. (**E**) Primary fluid inclusions in the quartz of Type-3 massive ores. (**F**) Halite-bearing fluid inclusions in the quartz of Type-3 massive ores.
## 1 Introduction  
Railway is an important mode of public transportation in contemporary society. With the increase in railway running mileage and speed, the risk of the rail defects affected by repeated extrusion, friction, and impact of train wheel sets becomes higher [1].  
To keep safe, some nondestructive testing techniques, such as ultrasonic, eddy current, and MFL, have been used in the detection of rail defects [2]. Ultrasonic detection is a widely used and mature rail detection technology [3, 4]. It is a convenient method for detection of the internal defects of a rail, but due to the need for a coupling agent, ultrasonic detection cannot complete noncontact detection, so the detection speed cannot be further improved, and it is difficult to detect the surface defects by ultrasound.  
The eddy current detection needs no exchange agent. It is cheap, simple, and reliable; however, traditional eddy current testing has obvious skin effect, which makes it difficult to detect subsurface defects and is susceptible to the specimen surface state [5].  
MFL is an electromagnetic detection technique developed from magnetic particle detection [6]. The advantages of MFL are high sensitivity, high speed, and simple operation. It can detect the surface or subsurface defects [7], but the detection signal is easily affected by the lift-off change, the magnetic field excitation, and others.  
The principle of MFL detection is shown in Figure 1. When magnetized ferromagnetic material has defects in its surface, such as cracks, laminations, or magnetic nonuniformities in the manufacturing process, the refraction of the magnetic induction line changes the magnetic circuit, which leads to the leakage of part of the magnetic flux. By detecting the magnetic field above the rail surface with sensors, a defect in the surface can be found and further information about it can be obtained. Multiple sensors are usually arranged along the width of a rail to detect the defect in the entire surface.  
The vertical distance from the sensor or yoke to the rail is called lift-off. The LMF of a defect near the rail surface changes quickly in the vertical direction. During detection, the lift-off changes affected by vibration and other factors, thus interfering with the detection of the defects. With the development of MFL detection [8; 9; 10], various requirements are proposed for railway detection equipment, such as high speed, high efficiency, and high resolution, so interferences such as lift-off interference must be suppressed.  
The interference can be suppressed by hardware such as differential circuit [11], but as the interference is affected by detection speed, the state of the rail surface, and other factors, a circuit is difficult to apply to various situations. Therefore, in addition to hardware filtering, digital filtering is needed. The rail head upper surface defect is a common early defect, and it is usually defected by MFL. This paper presents a method to suppress the lift-off interference in MFL detection.
## 2 GEO Grid ASTER EVI Products
### Vegetation Index Module
#### 2.2.3 Combined ASTER-MODIS EVI (\(Evi_{c}\))  
As described previously, the ASTER and MODIS sensors are onboard the same Terra platform. Therefore, there are always MODIS data that were obtained simultaneously with ASTER image acquisitions. Although their spatial resolutions are considerably different, the MODIS blue band could be used with the ASTER red and NIR bands to compute the EVI:  
\[EVI_{C}=2.5\cdot\frac{\rho_{ASTER,NIR}-\rho_{ASTER,red}}{\rho_{ASTER,NIR}+ 6\cdot\rho_{ASTER,red}-7.5\cdot\rho_{MODIS,blue}+1} \tag{8}\]
# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## 2 Background
For instance, current corporate networks consist of thousands of devices and applications, without which business processes cannot function, whilst even a temporary unavailability of services rendered may result in large financial losses and reputation damage [35]. However, the critical importance of VM also applies to other entities ranging from office networks, to financial and personnel systems, to very specialized systems (e.g., industrial/process control, weapons, telecommunications, and environmental control systems) [38]. Thus, due to increasing threats and known vulnerabilities, an organization must have a vulnerability management system or a process that provides the latest security patches and updates to the organization's network [39]. In general, the purpose of VM is to monitor and identify new threats and vulnerabilities (hardware and software) that may affect the confidentiality, integrity, or availability of an organization's IT resources. In addition, VM should help system administrators to identify existing and known vulnerabilities and apply appropriate actions to reduce the risk of vulnerability exposure [40]. The undertaken actions may consist of patching vulnerabilities or taking other actions, if a vulnerable system cannot be repaired due to operational constraints, or the patch causes key services to be unavailable. If a vulnerable system cannot be repaired, system/network administrators should create a plan to mitigate every vulnerability that cannot be eliminated [41]. Mitigation plans may consist of blocking rules on Intrusion Prevention system (IPS)/Intrusion Detection System (IDS), moving the system to a separate Virtual Local-Area Network (VLAN), significantly restricting or blocking ports on the firewall, or even removing the system from the publicly available part of the network until appropriate corrective measures are implemented. Further, from a practical point of view it is important to scan as much of the network as possible (preferably the whole network each time). Using the VM system presented in this contribution, if only a fraction of the network is scanned within the VM process then by viewing the scan results and comparing them with the assets via asset management tool, one can estimate the percentage of scanned devices and determine the overall network status and network hygiene level. Finally, in almost every environment it is presumed that devices will be turned off, disconnected from the network or put in a transient state during the scan, so scans should take place regularly (as often as possible) and maintain a vulnerability history.
## 4 Multi-Agent Particle Swarm Optimization (MAPSO) Approach
### Agent and Multi-Agent System (MAS)  
An agent is a physical or virtual entity that accomplishes one or more tasks with the ability of perception, communication and problem solving. Additionally, an agent essentially has the following typical characteristics ([PERSON] et al., 2010; [PERSON] et al., 2010).  
(1) Perception  
Agents can feel the stimulation of environment, and are completely dependent on their own ability to adjust the behavior without the assistance of others.  
(2) Reactivity  
An agent can not only feel the changes of the external environment, but also respond to them with different environmental information.  
(3) Initiative  
An agent is not simply controlled by the external environment, but can take the initiative to complete a task according to its own actions.  
Communication  
An agent communicates with other agents to enhance its efficiency, which is crucial to the expansibility of itself.  
Evolvability  
In other words, evolvability means the learning ability. An agent has certain intelligence and will improve learning ability as actions accumulate, thus solving problems better.  
A single agent has a limited ability to solve problems, and it seems to be powerless when encountering huge and complex problems. Accordingly, it is necessary for multiple agents to collaborate to solve problems mutually. Thus, MAS comes into being. MAS is a network structure composed of loosely coupled and coarse-grained agents which are physically or logically dispersed. They accomplish complex control tasks or solve complicated problems through negotiation and cooperation. Generally speaking, a MAS system usually contains the following four elements:  
1. meaning and purpose of each agent
2. the definition of global environment
3. the definition of local environment
4. the behavioral rules that govern the interaction between an agent and its environment.
## 3 Synthetic Examples
### A Synthetic Seismic Signal  
Next, we apply CWT GST, SSCWT, and SSGST to a synthetic seismic signal for illustrating the effectiveness of separating the different frequency components of seismic signals. The synthetic signal is comprised of a cosinusoid signal \(sig_{1}\), two intermittent signal \(sig_{2}\) and \(sig_{3}\), and a simulated attenuate seismic signal \(sig_{4}\), which is composed of four different frequency Ricker wavelets at 0.2 s, 0.4 s, 0.6 s, and 0.8 s of 100 Hz, 80 Hz, 65 Hz, and 55 Hz, respectively (Figure 5).  
\[sig_{1}=1.5\cos(16\pi t) \tag{23}\]  
Figure 4: Time-frequency spectra of the double hyperbolic chirped signal based on (**a**) CWT, (**b**) GST, (**c**) SSCWT, and (**d**) SSGST.  
\[sig_{2}=\left\{\begin{array}{cc}\cos(80\pi t),&t\in[0,0.5]\\ 0,&\text{otherwise}\end{array}\right. \tag{24}\]  
\[sig_{3}=\left\{\begin{array}{cc}0,&\text{otherwise}\\ \cos(100\pi t),&t\in(0.5\,\ 1\ ]\end{array}\right. \tag{25}\]  
The results of the synthetic signal obtained by CWT, GST, SSCWT, and SSGST are presented in Figure 6. As can be seen in Figure 6, all methods can separate the frequency components well, and the time-frequency representation generated by the SSCWT and SSGST methods provide clearer time-frequency resolution than the CWT and GST methods. Comparing Figure 6b with Figure 6d, SSGST and SSCWT show similar frequency resolution. However, SSGST can depict the 8 Hz cosine signal with higher resolution than SSCWT.  
Figure 5: The components of the synthetic signal.  
Figure 6: _Cont._
## 4 Proposed Algorithm
### Centralised Clustering  
In centralised clustering, each ith node is represented by \(\text{N}_{\text{i}}\), where \(\text{i}\in\{1,2,\ldots,\text{n}\}\), and the base station is represented as \(\text{N}^{+}\). Therefore, the overall entities participating are N= {\(\text{N}_{1}\), \(\text{N}_{2}\), \(\ldots\), \(\text{N}_{\text{n}}\), \(\text{N}^{+}\)}. \(\text{N}^{+}\), based on the sensor's location and residual energy, partitions the network into clusters; for this, the proposed scheme makes use of k-means clustering as shown in Algorithm 2. Once the clustering decision is made, \(\text{N}^{+}\) conveys the clustering information to the sensing nodes (\(\text{N}_{\text{i}}\)). \(\text{N}^{+}\) elects the CHs and sends the corresponding CH parameters to \(\text{N}_{\text{i}}\). On receiving the values from the BS, each node determines whether it is the CH or a normal participating node in the current round. If any node is not selected as a CH in the current round, then the \(\text{N}^{+}\) directly transmits the \(\text{N}_{\text{i}}\) value corresponding to the CH with which that node needs to associate. The k-means clustering initially performs a random partition of the network into centroid based k-clusters; then, it repeatedly performs the task of minimising the distance between each node and the respective centroid. Ultimately, this process ensures that the sensor nodes within the cluster are in close proximity. This leads to reduced energy dissipation  
Figure 4: Timeline showing the overall operation of the algorithm.  
Figure 5: Optimal number of clusters in the network.  
and efficient communication of the CH with the member nodes, as well as supporting efficient monitoring of the cluster.
## 4 Prediction Process
### Factors Selection  
Selecting the proper triggering factors is essential to guarantee the accuracy of a prediction. First, the candidate factors were determined with landslide deformation characteristics. Since displacement rates mainly increased during the rainy season and reservoir water level fluctuation stages (Figure 11), rainfall and reservoir water level were considered the main triggering factors that caused the deformation of the Shengjibao landslide. Additionally, the Wenchuan earthquake that occurred in May 2008 had an insignificant impact on the pattern of the landslide displacement according to the monitoring records shown in Figures 8 and 11. Thus, the events of the earthquake were not considered as candidate factors for prediction. As the values of GPS monitoring data were obtained at the end of each month, the candidate factors should include the precipitation during the current month, the precipitation during the past two months, the average reservoir level during the current month, and the change of the reservoir level during the current month, the change of the reservoir level during the past two months. These candidate factors were considered as \(f_{1}\), \(f_{2}\), \(f_{3}\), \(f_{4}\), \(f_{5}\).  
Slopes in different evolutionary states may respond differently to the same external trigger factors. When the slope is stable, even strong influence factors are usually not enough to cause large deformation. In contrast, if a slope is marginally stable or even unstable, the slightest increase in external \"loads\" may cause disequilibrium and large deformation [40]. Therefore, the state factors were also considered as input values. The candidate factors should include the change of the displacement during the last month, during the last two months, during the last three months, and the average displacement during the last 12 months (annual displacement rate). These candidate factors were considered as \(f_{6}\), \(f_{7}\), \(f_{8}\), \(f_{9}\).  
A resolution coefficient of 0.5 was obtained using the GCAM. The relational degrees \(r_{k}\) between the periodic term and the candidate factors are shown in Table 2. The candidate factors are closely related to the periodic term, \(r_{k}>0.6\), suggesting that the parameters were properly selected [41].  
Figure 11: Rainfall, reservoir water level, and displacement increment of ZK2-1 and ZK2-3.
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 2 Dependence Representation Learning
### Implementation  
To keep the experiments simple, we implemented the visualization method and neural network in the same way each time. The specific implementations of the target and loss functions depend on the task. Figure 2 shows our implementation of dependence representation learning. As training data, feature pairs are transformed into 2D histograms and then input into CNNs. The CNNs are trained by minimizing the loss between the outputs and the targets computed from the feature pairs.  
The visualization method _visualize_: scatter plots are frequently used to visualize dependencies between features. However, scatter plots are usually rendered as images that contain many individually specified pixels. Thus, if we use scatter plots as inputs to CNNs, then we will suffer from the curse of dimensionality. Therefore, we used density-based 2D histograms, which can be considered as compressed scatter plots. Another advantage of density-based histograms is that their representations are almost equivalent for different feature pairs, as long as the features come from the same distribution. However, histograms are often skewed by a small number of outliers. Thus, we remove outliers from each feature before converting the data into a histogram. A sample \(x_{i}\) of a feature is an outlier if \(z_{i}>3.5\), where:  
\[z_{i}=\frac{0.6745(x_{i}-median(x))}{MAD}. \tag{2}\]  
The MAD stands for median absolute deviation, which is defined by  
\[MAD=median(|x_{i}-median(x)|). \tag{3}\]  
The neural network \(\mathcal{N}_{\theta}\): CNNs have been hugely successful in various computer vision applications. We use CNNs because we assume that they are the most advanced model for processing visual information.  
Figure 2: The implementation of dependence representation learning.  
More specifically, we use the DenseNet architecture [21], which is a widely-known image classification model. Here, we used representation vectors with 1734 elements.  
Common implementation details: again, for simplicity, we tried to apply a unified training method as much as possible in all experiments. The common implementation details for the rest of the paper are as follows, though some task-specific details are described in later sections for each task. We use PyTorch [22] to train the CNNs, scikit-learn [23] for machine learning algorithms, and NumPy [24] to generate the 2D histograms and carry out numerical tasks. Here, the number of 2D histogram bins is 16, resulting in an input shape of 16 \(\times\) 16. We trained CNNs using the ADAM optimizer [25] and a mini-batch size \(m\) of 64.
# Random Frequency Division Multiplexing
## 5 Numerical Results  
In this section, RFDM is compared to MMSE detectors in mobile time-varying channels. The multicarrier modulations use the same bandwidth, i.e., the system bandwidth is 1.4 MHz, the subcarrier spacing is \(\Delta f=15\),000 Hz, and the vehicle speed is 120 km/h with a maximum Doppler frequency shift 300 Hz. The number of subcarriers in the MMSE detector is \(N_{s}=128\) and the compression ratio \(\rho=0.75\), which means that the number of subcarriers for our RFDM is \(N_{s}=96\). The maximum number of multipath delay channel taps \(L=9\), and BPSK and QPSK are employed. Furthermore, the antenna number is considered with \(N_{t}=N_{r}=4\). Many simulation setups are from the 3 GPP LTE-OFDM system [14]. We also assume that the channel estimation is perfect at the receiver and so we run 50,000 Monte Carlo simulations to estimate the average BER.  
The sample of the training set is 50,000 to train the DNN model offline. The hidden layer produces an output after weighting (default function 'tansig') the input and summing with the deviation (default function 'learngdm'); the hidden layer produces an input to the output layer after activating the function (default function 'Sigmoid'); and finally, the hidden layer produces an output after weighting the input and summing with the deviation and passing through the corresponding function (default function 'purelin'). We set three layers--the input, hidden and output layers--in our simulations for simplicity. Once the _DNN_ model is trained, then the receiving signal \(\hat{\mathbf{x}}\) is passed as the input to the _DNN_ model to detect the original signal \(\hat{\mathbf{s}}\).  
Figures 3 and 4 present the BER performance of the proposed RFDM and the MMSE scheme when using BPSK and QPSK modulation, respectively. It shows that our solution currently has a performance gap with MMSE, but when SNR exceeds 20, our bit error rate can achieve \(10^{-3}\) which is still competitive, especially since MMSE uses 128 subcarriers while we only use 96 subcarriers.  
Figure 3: BER comparisons of RFDM with BPSK in 4 \(\times\) 4 MIMO.  
Figure 2: A RFDM-based multicarrier MIMO communication system.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### M2V/V2M Operations  
#### 5.2.1 System Configuration  
The bidirectional M2V/V2M operations can be performed by connecting the DC-link of the EV to the DC microgrid. Under the EV idle mode, the DC microgrid can charge the EV onboard battery in the M2V operation, while the battery can support the microgrid in the V2M operation. An available wind SRG/PV-based DC microgrid [67] is employed here for studying. The PV source is neglected for simplicity. Figure 30 shows the system configuration of the interconnected operation of the EV and the DC microgrid, and Figure 31 shows the detailed power circuit of the whole system. The asymmetrical bridge converter is applied to the SRG for the best control flexibility. Through the interleaved boost converter, the SRG output voltage is boosted to 400 V. The DC microgrid system consists of a dump load to prevent overvoltage, a battery energy storage system, and a DC load. The SRG control scheme and the DC microgrid system can be referred to in [69]. Some key parameters and system components of the wind SRG-based DC microgrid are listed below:  
Figure 31: Detailed power circuit of developed M2V/V2M operations.  
Figure 30: System configuration of developed M2V/V2M operations.  
* SRG (4-phase 8/6): rated speed \(=6000\) rpm, rated power \(=2.3\) kW, rated voltage \(=48\) V.
* Power switches: IGBT modules CM100 DY-12H.
* DC-link voltage: \(V_{dc}=400\) V.
* BESS: nominal voltage \(V_{b3}=48\) V, capacity \(=28\) Ah.