esa-sceva/satcom-chunk-collection huggingface dataset:
# Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks
## 3 Malware Propagation Modeling in Multi-Layer Networks
### Topologies
#### 3.1.4 Random Geometric Graph Topology  
A random geometric graph (RGG) is a non-directed graph constructed by randomly placing \(N\) nodes in a metric space and connecting two nodes with a link if and only if their distance is in a certain range. A real application of RGGs is the modeling of ad hoc networks [18]. In addition, they are used to execute benchmarks for graph algorithms.  
Random geometric graphs (RGGs) are commonly used to model networks of systems that depend on the underlying spatial constraints. The probability distribution of an RGG is vital to the study of its random topology and its properties. However, a major obstacle to extracting the graph distribution is that it requires the common probability distribution of \((n-1)/2\) distances between \(n\) nodes that are randomly distributed in a delimited field. For arbitrary \(n\), one can draw a set of upper limits on the entropy of the graph. In particular, the limit involving the entropy of a three-node graph is tighter than the existing limit assuming that distances are independent [57].
## 3 Results
### Fault Diagnosis
#### 3.2.10 Wigner-Ville Distribution (WVD)  
Strictly speaking, WVD has to be ranked among the time-frequency domain techniques, even if it is characterized by a mathematical formulation that is rather similar to that of the cyclostationary techniques and is suitable for the analysis of the same type of signals.  
Looking at the respective plots, the presence of cyclic phenomena related to fault development, totally absent in the initial phase, can be detected, albeit more vaguely than before. Figure 47 shows a series of peaks at the spectral frequency of 5280 Hz (still an exact multiple of 160 Hz), spaced from each other by a period (cycle) equal to 30 ms, i.e., the inverse of the shaft rotation frequency \(f_{r}\). A marked impulsiveness features in Figure 48a at the spectral frequency of 4000 Hz (25 \(\times\) 160 Hz), and its zoom (Figure 48b) is dominated by a train of alternated chromatic bands at the spectral frequencies of 240 Hz (1.5 \(\times\) 160 Hz) and 480 Hz (3 \(\times\) 160 Hz), spaced apart by a time interval equal to the inverse of the BPFO and its double, respectively (about 4 ms and 2 ms).  
Figure 47: WVD applied to the signal from sensor 6 on bearing 3 (last 122 milliseconds).  
Figure 48: Smoothed WVD applied to the signal from sensor 7 on bearing 4 (last 147 milliseconds) (**a**) and its zoomed view (**b**).
## 2 Study Area and Investigated Landslide  
### Regional Geological Background  
Hanyuan County is located in the northern section of the Sichuan Yunnan north-south tectonic belt, with complex geological structures mainly controlled by three major tectonic belts: the NS-trending Hanyuan Zhaojue Fault, the Yiping Meigu Fault, the NETrending Emeishan Fault, and the NW-trending Jinping Fault and Shimian Fault [18]. The topography is mainly mountainous, characterized by high in the northwest and low in the southeast, with the highest elevation of 4021 m, the lowest elevation of 550 m, and the maximum relative height difference of 3471 m. The climate of Hanyuan County is a subtropical monsoon humid climate, with an average annual temperature of 18.0 \({}^{\circ}\)C. According to Hanyuan County Meteorological Bureau statistics, the county's annual average precipitation is 741.8 mm, and precipitation is mainly distributed in June to October, accounting for 74.6% of the annual precipitation. The area where Hanyuan landslide is a typical low- and middle-mountain slope landform, and the topography is in the shape of steps. The landslide is relatively steep, the average slope is 25\({}^{\circ}\), and the steepest slope is 40\({}^{\circ}\), which creates favorable topographic and geomorphological conditions for landslide instability. Figure 1 shows the main strata exposed in the study area (according to the 1:200,000 geological map), including Sinian (Z), Triassic (T), and Jurassic (J). The area where the landslide is located is the Neogene Pliocene Xigeda formation (N\({}_{2}\)X). The lithology is mainly grayish yellow, dark gray sandstone, clay rock, and carbonaceous clay rock.  
Affected by the recent continuous heavy rainfall, in the early morning of 21 August 2020, a landslide occurred in Group 6 of Zhonghai Village, Fuquan Town, Hanyuan County, Ya'an City. The amount of earthwork of the landslide was about 800,000 cubic meters, and the total volume of the landslide was about 5 million cubic meters, causing damage to public houses. Eight people are missing, one person was injured, and traffic on provincial highway 435 was interrupted. The investigation shows that the last continuous rainfall event before the landslide occurred from 19:00 on 18 August 2020 to 10:00 on 19 August 2020, and the cumulative rainfall in the past two weeks before the landslide occurred was greater than 260 mm. On the morning of 20 August, during the investigation of the hidden dangers of geological disasters in Hanyuan County, it was found that there were slope cracks and wrong ditches in Group 6 of Zhonghai Village, Fuquan Town. From 14:30 on the afternoon of the same day, 55 people from 14 households of the threatened masses were transferred by cadres of township and village groups. However, nine villagers were asked to evacuate that night and then returned privately, and they unfortunately suffered the disaster (Figure 2).  
Figure 1: A 3D geological map of the study area (revised according to 1:200,000 geological map).
## 5 Performance Evaluations
### Discussion of Simulation Results  
As we observed from the simulation results, local updates perform very well in terms of ER; on the other hand, local updating is much more energy efficient than global updating in networks having any number of nodes, but, small number of mobile nodes (_i.e._, around 10-15% of the number of nodes) moving with a speed varying from low to high (_i.e._, around 1-20 \(m/s\)). This specification is very much  
Figure 14: Comparison of local and global updates in terms of Communication Overhead.  
valid for mostly static sensor network scenarios in which most of the sensors are static and some sensors are attached to people or vehicles such as firefighters or firetrucks, unmanned aerial vehicles (UAVs) moving at low or medium velocities in an Emergency Response Application.  
It is possible to apply some improvements to local updating mechanism if we assume that the mobile nodes are aware of their direction of movement. Mobile nodes can easily determine their movement direction, if they are aware of the position of the sink node. Some of the acknowledgement packets can be eliminated when a mobile node knows whether it moves towards the sink or in the opposite direction. If mobile node moves towards the sink, there is no need to have acknowledgements from the neighbor nodes in the same hop count level. Since the number of acknowledgement packets increases as the network size gets bigger (_i.e._, the number of neighbors increases), reducing unnecessary acknowledgement packets will have a positive impact on the overall communication overhead.
## 3 MAV Communication
### Sensing  
MAVs necessarily carry a number of sensors. At minimum, these include sensors for stability (e.g., inertial sensors, laser range-finders, _etc._), but additional sensors may fall into one or more categories:  
* Sensors that require significant mobility, e.g., still and video cameras;
* Sensors that are particularly heavy, e.g., chemical sensors;
* Sensors that need to be used only once per location, e.g., thermal sensors, passive infrared (PIR) sensors.  
Many of these, such as thermal or chemical sensors, generate data at low rates, on the order of a few bytes per sample. Also, since the measurements often lack high spatial or temporal variability, samplesare infrequently generated. In this case, throughput is of minimal concern. If the gathered data does not impact the real time mission status, then latency requirements can also be relaxed.  
Other sensors, however, can generate large volumes of data. Of these probably the most widely applicable and thus most common are cameras, which can generate anywhere from kilobytes to megabytes per sample, at rates from minutes per sample up to samples per second. High latency can affect the throughput if data flowing through the network saturates node capacities, thus resulting in dropped data. If the gathered data does not critically impact the real-time mission, latency may not be so much of a concern, but maximizing data throughput still remains critical. It is thus imperative that the network containing the MAV support both rapid data streams, and well as more sporadic, smaller sized packets.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 2 Literature Review
### Gaps in the Literature  
Despite the progress in modeling the yield curve, there is limited research focused specifically on short-term forecasting of the 10-year Treasury yield using multiple ML techniques in direct comparison. Most ML applications tend to focus on forecasting the full-term structure or use highly complex deep learning models such as LSTM (Long Short-Term Memory), which require extensive computational resources and are difficult to interpret [14]. This research seeks to fill that gap by evaluating and comparing the predictive performance of interpretable and widely used ML models, LR, DT, RF, and MLP, on a key individual yield over a short time horizon.
# A Period Training Method for Heterogeneous UUV Dynamic Task Allocation
## 2 Dynamic Task Allocation Formulation
### Objective Functions and Constraints  
The heterogeneous UUV group is set as \(\mathbf{U}=[U_{1},U_{2},\cdots,U_{N}]^{T}\), where \(N\) is the number of UUVs. \([x_{i},y_{i}]\), \(v_{i}\), and \(r_{i}\) represent the location coordinates, velocity, and search range of the UUV \(i\), respectively. \(\mathbf{T}=[T_{1},T_{2},\cdots,T_{M}]^{T}\) denotes irregular task areas, where \(M\) is the number of tasks. UUVs are expected to search all irregular task areas with minimum traveling distances and search times. Accordingly, the objective function \(f\) is defined as follows:  
\[\text{min}f=\sum_{i=i}^{N}\sum_{m=0}^{M}\sum_{n=0}^{M}d(T_{m},T_{n})\delta_{i,m,n}+\frac{(t(U_{i},T_{m})+t(U_{i},T_{n}))}{2}\delta_{i,m,n} \tag{1}\]  
where \(d(T_{m},T_{n})\) is the Euclidean distance between the locations of task \(m\) and task \(n\), and \(\delta_{i,m,n}\) is the binary decision variable. We define \(\delta_{i,m,n}\) as 1 if task \(m\) and task \(n\) are allocated to the UUV \(i\), and otherwise, we define it as 0. \(t(U_{i},T_{m})\) denotes the search time of the UUV \(i\) in task \(m\):  
\[t(U_{i},T_{m})=\frac{L_{i,m}}{v_{i}} \tag{2}\]  
where \(L_{i,m}\) and \(v_{i}\) are the coverage search path of the UUV \(i\) on task \(m\), and the search velocity of the UUV \(i\), respectively. As shown in Figure 2a,b \(L_{i,m}\) needs to be specially designed according to the UUV's search range and the task area's shape and size [4]. For the convenience of calculating the coverage search path, a maximum bounding rectangle is employed to approximate the irregular task areas.  
In Figure 2a, the irregular task area is approximately represented by its bounding rectangle. Figure 2b shows an illustration of the UUV spiral search strategy, and the coverage search path \(L_{i,m}\) can be calculated as follows:  
\[L_{i,m}=\frac{l_{m}\cdot w_{m}}{r_{i}}-r_{i}+\frac{\sqrt{l_{m}^{2}+w_{m}^{2}} }{2} \tag{3}\]  
where \(l_{m}\) and \(w_{m}\) are the length and the width of the bounding rectangle, respectively.  
To decrease the search time of the allocation of schemes, some UUVs with faster search velocities and wider search ranges tend to be assigned more tasks, leading to uneven allocation of schemes to UUVs. To avoid this situation, the maximum task load of the UUVs is described as follows:  
\[load=ceil\ (\frac{M}{N-1}) \tag{4}\]  
where \(ceil\ (\cdot)\) is the top integral function, which guarantees that UUVs are not overloaded.  
Figure 2: An approximate model of irregular areas. If there are multiple panels, they should be listed as (**a**) the maximum bounding rectangle and (**b**) the spiral search strategy.
# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 4 Background
### Generative Adversarial Network
Overtime, \(G\) becomes better in generating more realistic samples that would confuse \(D\), and \(D\) becomes better at detecting fake samples. They both try to optimize their objective functions, in other words, \(G\) tries to minimize its cost value and \(D\) tries to maximize its cost value. \[\min_{G}\max_{D}V(D,G)=\mathbb{E}_{x\sim p_{data}(x)}[logD(x)]+\mathbb{E}_{z \sim p_{z}(z)}[log(1-D(G(z)))] \tag{1}\]  
Equation (1) was designed by [PERSON] et al. [53] to compute the cost value of GAN where \(x\) is the real sample from the training dataset, \(G\)(z) is the generated sample, and \(D(x)\) and \(D(G(z))\) are the discriminator's verdict that \(x\) is real and the fake sample \(G\)(z) is real. There are numerous variations of the original GAN. Among the most prominent ones are CGAN, WGAN, and Self-Attention GAN (SAGAN) [54]. CGAN extends the original GAN by taking an additional input which is usually a class label. The label conditions the generated data to be of a specific class. Therefore, the loss function in (1) becomes as follows:  
\[\min_{G}\max_{D}V(D,G)=\mathbb{E}_{x\sim p_{data}(x)}[logD(x\mid c)]+\mathbb{E }_{z\sim p_{z}(z)}[log(1-D(G(z\mid c)))] \tag{2}\]  
where \(c\) is the conditional class label. In order to prevent the vanishing gradient and mode collapse problems (discussed below), WGAN applies an objective function that implements the Earth-Mover (EM) [55] distance for comparing the generated and real data distributions. EM helps in stabilizing GAN's training and the equilibrium between the generator and the discriminator. If the gradient of the loss function becomes too large, WGAN will employ weight clipping, WGAN Gradient Penalty (WGAN-GP) [56] extends WGAN by introducing a penalty term instead of the weight clipping to enhance the training stability, convergence power, and output quality of the network. Moreover, SAGAN applies an attention mechanism to extract features from a broader feature space and capture global dependencies instead of the local neighborhoods. Thus, SAGAN can produce high-resolution details in data as it borrows cues from all feature locations in contrast to the original GAN that depends on only spatially local points. In theory, both \(G\) and \(D\) are expected to converge at the Nash equilibrium point. However, in practice this is not as simple as it sounds. Training GANs is challenging, because they are unstable and difficult to evaluate. GANs are notorious for several issues, which are already covered intensively in the literature; therefore, we will only discuss them briefly below. Figure 1: Architecture of the original GAN [53].
## 2 Materials and Methods
### HS-SPME/GC\(\times\)GC-TOFMS Detection of the Volatile Compounds  
Samples (100 mg) were mixed with 1 mL of 30% NaCl (saturated solution) and 10 \(\upmu\)L of 1 mg/L n-Hexyl-d13 (used as the internal standard). A solid-phase microextraction (SPME) holder installed with a 50/30 \(\upmu\)m fiber (DVB/CAR/PDMS, model 57329-U, Supelco, Bellefonte, PA, USA) was used for sampling. Before the adsorption, the fiber was conditioned at 270 \({}^{\circ}\)C for 10 min. The SPME extractor was transferred to the incubation chamber, and the samples were adsorbed at 60 \({}^{\circ}\)C for 40 min. After the adsorption, the SPME extractor was transferred to the GC inlet and desorbed at 250 \({}^{\circ}\)C for 5 min. GC\(\times\)GC detection was performed using a LECO Pegasus(r) 4D instrument (LECO, St. Joseph, MI, USA) consisting of an Agilent 8890A GC (Agilent Technologies, Palo Alto, CA, USA) system equipped with a split/splitless injector and dual stage cryogenic modulator (LECO) outfitted with a TOFMS detector (LECO). The separation system consisted of a one-dimensional column, Rxi-5 Sil MS (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m) (Restek, Bellefonte, PA, USA), and a two-dimensional column, Rxi-17 Sil MS (2 m \(\times\) 150 \(\upmu\)m \(\times\) 0.15 \(\upmu\)m) (Restek, Bellefonte, PA, USA). High-purity helium was used as a carrier gas with a constant flow rate of 1.0 mL/min. For the one-dimensional column Rxi-5 Sil MS (30 m \(\times\) 250 \(\upmu\)m\(\times\) 0.25 \(\upmu\)m), the initial temperature was 50 \({}^{\circ}\)C for 5 min, then increased to 170 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min for 1 min, then increased to 230 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min for 5 min. The temperature rise of the 2-D column Rxi-17 Sil MS (2 m \(\times\) 150 \(\upmu\)m \(\times\) 0.15 \(\upmu\)m) was 5 \({}^{\circ}\)C higher than that of the 1-D column, and the temperature of the modulator was always 15 \({}^{\circ}\)C higher than that of the 2-D column. The modulation period was 10 s. The inlet temperature was 250 \({}^{\circ}\)C [25]. The mass spectrometric detection of compounds was carried out on a LECO Pegasus BT 4D, with a mass spectrum transmission line temperature of 250 \({}^{\circ}\)C, ion source temperature of 250 \({}^{\circ}\)C, acquisition rate of 200 spectra/s, electron bombardment source of 70 eV, and detector voltage of 2042 V. The MS scan range was m/z 35-550 [26]. The relative quantity of these compounds was computed based on the concentration of the internal standard. Regarding qualitative analysis, the retention index (RI) was ascertained from the retention times of a series of n-alkanes (C7\(-\)C30) through linear interpolation. Volatile organic compounds in samples were identified relying on RIs and comparatively analyzed in conjunction with the recorded mass spectra of each compound by using a mass spectrum library search (NIST).
## 1 Introduction
### Step 3: LSI Construction  
Coupled with the _NIR_ component and \(H\) and \(I\) components obtained with various invariant color spaces in Step 3, we construct a logarithmic shadow index (_LSI_) in this step to further enhance the difference between shadow and the corresponding nonshadow based on the shadow properties mentioned previously.  
In particular, an initial shadow index (ISI) is first constructed with _NIR_, \(H\) and \(I\) components as follows:  
\[ISI=NIR\times\frac{I-H}{I+H} \tag{23}\]  
where _NIR_ indicates the near-infrared component, \(H\) implies the equivalent hue component and \(I\) refers to the equivalent intensity component.  
Figure 2: Dramatic decrease in terms of near-infrared (_NIR_) component, higher hue (\(H\)) component value and lower intensity (\(I\)) component value in shadow regions with samples for typical objects (taking the WorldView-3 as an example). (**a**) _NIR_. (**b**) \(H\). (**c**) \(I\).  
The developed ISI fully employs shadow properties of higher hue, lower intensity and dramatical decrease in terms of _NIR_ component when compared with the corresponding nearby nonshadow containing the same type features.  
Additionally, an obvious distinction appears between the linear function \(f(x)=x\) and the natural logarithm function \(f(x)=\ln(x+1)\) in compressing the data scale, as shown in Figure 3. Thus, the difference between the linear function and the natural logarithm function in compressing the data scale is further considered in _LSI_ construction.  
Subsequently, in order to further improve the distinction between shadow and the corresponding nonshadow, a certain natural logarithmic operation is particularly applied over ISI further compressing ISI to a narrower scale [24] at the pixel level as follows:  
\[LSI=\ln\Bigl{(}NIR\times\frac{I-H}{I+H}+1\Bigr{)} \tag{24}\]  
where \"+1\" is aimed at avoiding the calculation of \(\ln(0)\).  
Additionally, a significant importance appears in real-time and approximate real-time image processing (taking the shadow detection as an example) for HSR satellites [14; 28]. A great attention is attached to the timesaving shadow detection algorithm for shadow processing on HSR satellites. Accordingly, the proposed _LSI_ algorithm is promised to be a timesaving one, because the shadow index of the proposed _LSI_ algorithm is simply constructed with equivalent hue and intensity components as well as the _NIR_ component.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 4 Experiments
### Experimental Design and Setup
The used algorithms were: * Naive Bayes classifiers ([[http://scikit-learn.org/.../sklearn.naive_bayes.BernoulliNB.html](http://scikit-learn.org/.../sklearn.naive_bayes.BernoulliNB.html)]([http://scikit-learn.org/.../sklearn.naive_bayes.BernoulliNB.html](http://scikit-learn.org/.../sklearn.naive_bayes.BernoulliNB.html))); * decision trees ([[http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html](http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html)]([http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html](http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html))); * random forests ([[http://scikit-learn.org/.../sklearn.ensemble.RandomForestClassifier.html](http://scikit-learn.org/.../sklearn.ensemble.RandomForestClassifier.html)]([http://scikit-learn.org/.../sklearn.ensemble.RandomForestClassifier.html](http://scikit-learn.org/.../sklearn.ensemble.RandomForestClassifier.html))); * neural networks (multi-layer perceptron) ([[http://scikit-learn.org/.../sklearn.neural_network.MLPClassifier.html](http://scikit-learn.org/.../sklearn.neural_network.MLPClassifier.html)]([http://scikit-learn.org/.../sklearn.neural_network.MLPClassifier.html](http://scikit-learn.org/.../sklearn.neural_network.MLPClassifier.html))); * support vector machines (RBF kernel) ([[http://scikit-learn.org/.../sklearn.svm.SVC.html](http://scikit-learn.org/.../sklearn.svm.SVC.html)]([http://scikit-learn.org/.../sklearn.svm.SVC.html](http://scikit-learn.org/.../sklearn.svm.SVC.html))); * and l2-logistic regression ([[http://scikit-learn.org/.../sklearn.linear_model.LogisticRegression.html](http://scikit-learn.org/.../sklearn.linear_model.LogisticRegression.html)]([http://scikit-learn.org/.../sklearn.linear_model.LogisticRegression.html](http://scikit-learn.org/.../sklearn.linear_model.LogisticRegression.html))). Preliminary experiments with all feature vectors showed the best performances (in all cases) for random forests, followed by decision trees. Naive Bayes classifiers and logistic regression obtained the worst performances, which was expected for the case of the Bayes classifier due to the high correlation commonly shown by network traffic features [23]. Random forests and decision trees not only outperformed other learners, but they were also the most robust ones. Neural networks and support vector machines obtained lower performances than random forests as well as nondeterministic issues, meaning that they suffered noticeable performance drifts in different runs with the same parameters. As a general rule, all learners in their best performances show the same preferences among the studied vectors. Due to these reasons and for the sake of simplicity, in Section 5, we only show classification results obtained with random forests. We address the interested readers to examine the complete experiments available in Reference [18]. 8. **Evaluation** The evaluation considered three different perspectives: