esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Since the official outbreak of the Russian-Ukrainian conflict on 24 February 2022, local armed conflicts and riots have had an irreversible impact on the economic development, infrastructure, and population movements in the war zone in the short term. There is a strong link between war and refugees, and wars between nations can cause internal displacement as refugees free combat zones [1]. On March 11, the United Nations refugee agency stated that 2.5 million people had fled Ukraine due to the ongoing conflict, which represents the fastest increase in refugees over a period of time since World War II. Meanwhile, the U.N. refugee agency predicts that as the war continues, the number of Ukrainian refugees may exceed 4 million.  
Given the reality of war, there are significant limitations concerning the acquisition of spatio-temporal information in conflict areas, such as the personal safety of journalists, traffic congestion, and information transparency. Due to the difficulty of conducting a comprehensive field survey in a combat zone, the acquisition of information is completely dependent on official announcements and witness reports, which can result in insufficient data richness and doubtful authenticity. In contrast to this, the acquisition of ground data through remote sensing satellites stands out for its low cost and high-efficiency characteristics, and remote sensing technology has become an indispensable and important part of many decision-making processes [2; 3].  
Night-time light data can reflect human activities very well, and in good weather conditions, the dazzling light emitted in human-inhabited areas can be observed from space [4]. It is well known that scale is an important factor in remote sensing image analysis [5]. Due to the differences in scale of geographic information, even from the same type of data, the research results obtained by using single-scale data in a specific area cannot be effectively generalized to other study areas. To address this issue, in this research, the 36-day VIIRS daily data from 24 February 2022, to 31 March 2022 is used as the data for the conflict period, and the monthly VIIRS data in December 2021 is used as the stable pre-war data. In terms of dimensions, three scales are combined, namely at the national level, state level, and city level, in order to analyze the time-series changes in night-time lights.
## 2 High Dynamic Infrared Image Enhancement Algorithm
### Discrete Smooth 2D Wavelet Transforms
Compared to the original image, the decomposed low-frequency image does not have the problem of blurring, and with the increase in the number of decomposition layers, the image gradually blurs due to the increase in the frequency of the interception, as shown in Figure 4b,c. Therefore, the first low-frequency information layer after wavelet decomposition is used for subsequent processing. Figure 4: Experimental results of low-frequency information layer 3. **(a)** First low-frequency information layer, **(b)** Second low-frequency information layer, **(c)** Third low-frequency information layer. Figure 3: SWT2 breakdown diagram. Figure 2: Block diagram of multi-layer wavelet decomposition. The results of the experiment involving the high-frequency information layer image are shown in Figure 5. The first layer image is shown in Figure 5a-c, and the original image is decomposed into horizontal, vertical and diagonal directions. The detailed information is not fully displayed during processing, and the subsequent enhancement effect is not obvious. The second layer image is shown in Figure 5d-f, where the image information is clearly presented. The impact of noise is small after the subsequent enhancement of the best results. The third layer of the image is shown in Figure 5g-i. The image also clearly shows detailed information; however, after subsequent processing and the amplification of the noise, as well as image synthesis, artifacts will appear, resulting in image blurring. Therefore, the algorithm in this study selected the second high-frequency information layer for subsequent processing. The experimental results of the image comparison are as follows: the experimental results of the low-frequency information layer are shown in Figure 4, and the experimental results of the high-frequency information layer are shown in Figure 5.
## 2 Experimental Setup and Mission Architecture
### Dynamics Payload  
The Inertial Measurement Unit (IMU) is a BerryIMU based on a LSM9 DS1 sensor [19] that contains a micro-electromechanical system (MEMS) accelerometer and a MEMS gyro in a single chip (Figure 5d). Each mission was equipped with one or two LSM9 DS1 sensors. This sensor provides information about the attitude and angular acceleration of the payload assembly. The sensor is controlled by a dedicated Raspberry Pi Zero, which stores the collected data in a micro-SD memory card.
# Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
## 5 Intelligent Dynamic Backlash Agent: IDBA
### Calculating Order Size
#### 5.2.3 Computing the Order Size  
The objective of forecasting whether a \(BR\) of a specific trade is to decide the size of the buy order of that particular trade. To this end, we use the concept of confusion matrix. A confusion matrix is a specific table layout that allows visualization of the performance of a classification algorithm such as C4.5. Table 4 illustrates a typical confusion matrix.  
In the context of this paper:  
* \(TP\) denote the number of correctly classified _True_ instances of \(BR\).
* \(FP\) denotes the number of _False_ instances of \(BR\) falsely predicted to be _True_.
* \(FN\) denote the number of _True_ instance of \(BR\) falsely predicted to be _False_.
* \(TN\) denote the number of correctly classified _False_ instances of \(BR\).  
Based on Table 4, we define two ratios, namely the Positive Predictive Value (\(PPV\)) and False Negative Rate (\(FNR\)), as in Equations (17) and (18), respectively.  
\[PPV=\frac{TP}{TP+FP} \tag{17}\]  
\[FNR=\frac{FN}{FN+TN} \tag{18}\]  
We call \(PPV\) and \(FNR\) the \"order size parameters\". Let \(FBR\) denote the forecasted value of \(BR\). The value of \(FBR\), which can be either _True_ or _False_, is determined by the C4.5 algorithm with the variables \(TBO\) and \(\sigma_{p}\) considered as the input attributes. Let \"_Order Size_\" denote the size of a buy order generated by IDBA. Let \(Capital\) denote the capital available at the current time. Whenever IDBA generates a buy order, the size of an order is calculated as follow:  
if \(FBR=True\), then  
\[Order\ Size=PPV\ \times Capital \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual**} \\ \cline{3-4}  & & _True_ & _False_ \\ \hline \multirow{2}{*}{**Predicted**} & _True_ & \(TP\) & \(FP\) \\ \cline{2-4}  & _False_ & \(FN\) & \(TN\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: An illustration of a confusion matrix.  
otherwise,  
\[Order\;Size=FNR\;\times Capital \tag{20}\]  
where \(PPV\) and \(FNR\) refer to the variables previously computed as in Equations (17) and (18), respectively.  
To conclude, in this section, we present our approach to control the size of a buy order triggered by IDBA. It consists of forecasting whether a particular buy order will generate profit. This objective was shortened to predict the Boolean dependent variable \(BR\). To this end, we used the two variables \(TBO\) and \(\sigma_{p}\). We employ the C4.5 machine learning algorithm to find the relation between these two independent variables and \(BR\). The size of the buy order is a function of the order size parameters \(PPV\) and \(FNR\) (with \(PPV\) and \(FNR\) being calculated based on the confusion matrix of the decision tree resulted from the C4.5 algorithm).
## 3 Results  
### Compounds Obtained by Hydrodistillation and SPDE  
The compounds obtained by hydrodistillation (in the essential oils) and HS-SPDE (in the infusions) have their identification data shown in Table 5. Most of the peaks could be tentatively identified by a combination of a MS library search and retention indices. All these tentatively identified compounds matched the reference standards (which were tested) when comparing their mass spectra and retention time, being considered correctly identified. Some compounds that were identified with low MS library search matches and/or an imprecise RI match (in some cases, no RI data were found in the literature) are indicated with a question mark '(?)'. Regarding the unidentified compounds, the following is mentioned, instead of their names: either the most abundant masses (\(m/z\)) found in their spectra; or 'unknown', for peaks that did not display clearly distinguishable predominant masses. These peaks might be composed of mixtures of compounds, as reported by [PERSON] et al. (2016). All the available data about each unidentified peak (\(m/z\), RT, and RI) that may eventually be useful for future investigations are included. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Retention Index**} & \multicolumn{3}{c}{**Retention Time (min)**} & \multirow{2}{*}{
\begin{tabular}{c} **CAS-** \\ **Number** \\ \end{tabular} } \\ \cline{2-2}
**Compound** & **Literature** & & & & & & \\ \cline{2-2}
**Hexanal** & 801 & - & 803 & - & 19.39 & 66-25-1 & x \\
**Oxime-metoxy-phenyl** & - & - & 891 & - & 25.31 & - & \\
**Pinene \textless{}a->** & 932 & - & 931 & - & 29.19 & 80-56-8 & x \\
**Camphene** & 946 & - & 948 & - & 30.93 & 79-92-5 & x \\
**Benzaldehyde** & 952 & 954 & 959 & 12.91 & 32.06 & 100-52-7 & x \\
**Pinene \textless{}b->** & 974 & - & 977 & - & 33.97 & 127-91-3 & x \\
**5**-Hepten-2-one** & 981 & 975 & 979 & 13.94 & 34.15 & 110-93-0 & x \\
**\textless{}6**-methyl-5** & 988 & - & 985 & - & 34.84 & 123-35-3 & x \\
**Myrence \textless{}b->** & 988 & - & 986 & - & 34.84 & 377-69-3 & \\
**Pentyl furan \textless{}2**-> & 988 & - & 986 & - & 34.84 & 377-69-3 & \\
**Heptdienel \textless{}(2E,4Z)**-> & 990 n & 992 & - & 14.63 & - & 4313-02-4 & \\
**Octanal** & 998 & - & 1001 & - & 36.52 & 124-13-0 & x \\
**Heptdienel \textless{}(2E,4E)**-> & 1005 & 1006 & 1008 & 15.47 & 37.64 & 4313-03-5 & \\
**Cymme \textless{}p->** & 1020 & 1020 & 1021 & 16.31 & 39.62 & 99-87-6 & x \\
**Limonene** & 1024 & 1024 & 1025 & 16.58 & 40.25 & 5989-27-5 & x \\
**Eucalypt** & 1026 & 1028 & 1029 & 16.81 & 40.76 & 470-82-6 & x \\
**Ocimene \textless{}(E)**-\textgreater{}** & 1044 & 1039 & 1039 & 17.47 & 42.35 & 3779-61-1 & x \\
**2**-ocatel \textless{}(E)**-> & 1049 & 1052 & - & 18.21 & - & 2548-87-0 & \\
**Terpinene \textless{}\textgreater{}**-> & 1054 & - & 1052 & - & 44.3 & 99-85-4 & x \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Identified compounds.
## 3 Results and Discussion
### Correlation between the Speciation of PTMs and Soil Properties  
The results of correlation among the speciation of Cd and Pb, the pH value and CEC are shown in Table 3. The pH value and CEC were significantly negatively correlated with the content of exchangeable fraction and carbonate bound fraction Cd (\(P<0.01\)), and positively correlated with the content of bound organic and residual fraction Cd (\(P<0.05\)); the pH value and CEC were significantly negatively correlated with the content of exchangeable fraction and carbonate bound fraction Pb, and significantly positively correlated with the content of bound organic and residual fraction Pb. This indicates that the exchangeable and carbonate bound Cd fraction and Pb were respectively gradually converted to the bound organic and residual fraction Cd and Pb after adding stabilizers. This process is mainly achieved through increasing the pH value and CEC. In the process of remediating soil contaminated by PTMs, soil pH value is a very important factor [55]. Soil pH value directly controls the solubility of metal hydroxide, carbonate, and phosphate, the hydrolysis of PTMs, the formation of ionic radius, the dissolution of organic matter, and the charge property of soil surface [56; 57; 58]. With the increasing pH value, the functional groups, such as the phenols, hydroxyl groups and carbonyl groups are separated from soil organic matter. In this way, their affinity for metal cation increases, and thus they can reduce the mobility of PTMs ions [13]. The exchange adsorption is also one of important reasons for the reduction of PTMs activities [59]. The bigger the number of cation exchange, the stronger the retention of PTMs [60; 61]. As the content of soil CEC increases,  
Figure 5: X-ray diffraction (XRD) patterns of the soil under different treatments. 1: Pb\({}_{5}\)O\({}_{4}\), 2: Cd(OH)NO\({}_{3}\), 3: Pb\({}_{3}\)(CO\({}_{3}\))\({}_{2}\)(OH)\({}_{2}\), 4: Cd(OH)\({}_{2}\), BC: biochar, CK: control sample.  
the adsorption and exchange ability on cationic in soil is enhanced, and the content of the available PTMs in soil can be reduced by exchangeable reaction [62].
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 3 Results and Discussion
### Additional Experiments  
Furthermore, we decided to evaluate the model on a smaller and more homogeneous dataset. In this case, the uniformity of the dataset meant choosing a subset of images that represented terrains with similar features. Thus, from the original dataset, a subset of 100 images was set. This new dataset consisted of images of forests and fields, with 10% of them described as abnormal. Examples from the newly constructed dataset are presented in Figures 7 and 8.  
The approach presented in Section 3.1 was applied to the new dataset. The model training process output is presented in Figure 9. The main goal of the experiment was to observe the model's behavior for less diverse datasets. The results of the experiment show that the model's accuracy and precision tend to be significantly better compared to the original experiment. The average accuracy obtained was 81% and the recall was 80%. All averaged performance indicators are summarized in Table 3.  
Figure 6: Sample results for running Grad-CAM method compared with original images from custom dataset.  
However, the obtained results for precision seem to be insufficient, and we assume that the recall is valued higher in the case of anomaly detection for aerial imagery. In this case, we expect the classifier to have lower precision. The results can also be seen from the confusion matrix (Table 4).  
Figure 8: Samples of normal class from homogenous dataset. (**a**) Normal example of forest area. (**b**) Normal example of forest area. (**c**) Normal example of forest area. (**d**) Normal example of crop area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
0.81 & 0.32 & 0.8 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance indicators for the evaluated system.  
Figure 7: Samples of anomalies identified in the second experiment. (**a**) Anomaly example of military trenches. (**b**) Anomaly example of military base starting lane.
## 3 Methodology  
Three-phase voltages have the same frequency and magnitude, but their phases are shifted by 120 degrees relative to each other. The phase difference means that they are 120 degrees apart in time, so in a three-phase voltage system with a frequency of 60 Hz, one cycle is 1/60 s (16.67 ms), so 120 degrees corresponds to a time difference of about 5.56 ms. The concept is as illustrated in Figure 1 below.  
There are three methods to obtain three-phase voltages. The static phase converter method uses a capacitor to create an imaginary phase, the rotary phase converter method utilizes a single-phase voltage supply to rotate an induction motor to generate three-phase voltages, and the third method employs power semiconductors.  
The first methodology uses a capacitor to create a phase difference in a single-phase power supply to create a second phase, and the remaining one phase is processed as an imaginary phase. This method operates stably under light loads; however, when the load increases, the output voltage balance and phase stability may be compromised, making it unsuitable for use in a PDS. The second methodology rotates an induction motor with a single-phase power supply to create three phases. The voltage and phase quality are superior to the first method, and it operates stably under heavier loads. However, since this method requires mechanical rotation, it generates noise and incurs maintenance costs. Additionally, its efficiency is relatively low. Furthermore, both the first and second methods convert three-phase 220 V into output voltage, not three-phase 380 V line-to-line voltage. Therefore, if 380 V is required, an additional transformer is also required, making these unsuitable for use with multiple loads connected to the PDS. The principles for the first and second phase conversion methodology are as illustrated in Figures 2 and 3 below.  
The last third method is the electronic phase converter method using power semiconductors selected in this paper. It converts a single-phase AC voltage to DC using the switching function of power semiconductors and then converts it to a three-phase AC voltage again.  
Figure 1: Phase and time of three-phase voltage.  
The conversion of single-phase to three-phase power using power semiconductors can be achieved in two ways: by converting the high-voltage single-phase 13,200 V to three-phase 22,900 V, or by converting the single-phase 13,200 V input to a low voltage through a transformer and converting it to three-phase 380 V. The common characteristic of the two methods is that they receive the high voltage of 13,200 V as the input voltage. However, this approach has the disadvantage of increased hardware size and costs due to the need for high insulation performance and a larger quantity of power converters. To address these challenges, this paper adopted a method of converting a low-voltage 220 V input to DC and then converting it to a three-phase line-to-line voltage of 380 V.  
The strength and weakness of each method are as shown in Table 3.
# Numerical 3D Modeling: Microwave Plasma Torch at Intermediate Pressure
## 2 Model Description  
### Microwave Plasma System  
Figure 1 shows a schematic of the microwave excitation device; 2.45 GHz electromagnetic waves enter the WR-430 waveguide from the right-hand side through a circulator, after which there are three stub tuners, and there is a short-circuit board on the left side. Figure 2 represents the cutaway view of the microwave plasma torch, and Figure 3 shows a 2D section along the central axis in the \(x\)-\(z\) plane, which is the cross-sectional dimension of the rectangular excitation waveguide with AB = 54.6 mm, length as AH = 218 mm, and width of the WR-430 rectangular waveguide in the \(x\)-\(y\) plane as 109.2 mm. To prevent microwave leakage, the quartz tube is surrounded by a cylindrical metal cage, which is modeled as a perfect electrical conductor. Ar was fed axially through the quartz tube at the flow rate of 1 L/min. The pressure in the quartz tube was 2000 Pa, initial gas temperatures was 293.15 K, the initial electron density was \(10^{16}\) m\({}^{-3}\), the initial average electron energy \(\varepsilon\) was 4 eV, and the initial potential was 0 V.  
In this study, the fluid approximation method was used to couple the Boltzmann equation to the Maxwell equation for calculating the density of charged particles, electron temperature, and the electromagnetic field. The Boltzmann equation embodies the change of ion velocity distribution equation \(f(r,v,t)\) with time, where \(v\) is the velocity, \(r\) is the position, and \(t\) is time. The calculation process was as follows: the energy distribution function of electrons was brought into the Boltzmann equation to solve the electron transport coefficient through the BOLSIG+ solver. The electron transport coefficient and the experimentally determined plasma chemical cross-section data were formed into the continuous equation, momentum conservation equation, and the energy conservation equation. Then, these three equations were coupled with the electromagnetic field to solve the electromagnetic field and plasma characteristic parameters. As the Boltzmann equation is highly nonlinear, it is difficult to use directly in the analytical solution. Thus, we used the finite element method and a numerical technique to solve the equations. Before solving the finite element method, the space and time of the solution target must be discretized. In this study, a tetrahedral mesh was used to divide the solution domain of the 3D graphics, and a boundary layer was used to encrypt and divide the boundary. The total number of the mesh elements was 183,448. The model was executed on a computer with an i9-9900U CPU and 64 GB RAM; the execution duration was approximately 101 min.  
Figure 1: Graphical representation of the microwave excitation device.  
Figure 3: Two-dimensional (2D) section of the model (\(y=0\)), with dimensions.  
Figure 2: Cutaway view of the microwave plasma torch.
## 3 Protocol Framework
### Sleep Frame  
Presently, many low-powered MAC protocols have employed the Duty Cycle mechanism to overcome the energy constraints so this paper also employed it. As described in Basic Framework subsection, every subframe is composed of an Active frame and Sleep frame. There is no transmission in Sleep frame except emergency data transmission, so all nodes maintain the sleep state during Overall Sleep frame. However, the PC has to perform the Duty Cycle mechanism by periodically switching both wake-up and sleep status because the proposed MAC protocol adapts emergency data transmission with conservation of energy efficiency at most periods.
# Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network
## 4 Simulation
### Piston Error Detection for the Telescope Composed of Two Segments
From Equation (16), we can conclude that the datasets used to train the network are only related to the numbers of telescope sub-pupils and input light sources. When the numbers of the sub-pupils and working wavelength were determined, the network could be trained to map the relation between the peak heights of MTF sidelobes and piston error, since learning mapping relationships is a data-driven process. Thus, even for different imaging systems, if they have the same numbers of sub-pupils and work under the same operating light, the trained network should have quite good generalization ability. In order to verify this, we designed another simulation based on the previous one. In this simulation, we changed the F\({}^{\#}\) of the imaging system and generated different intensity images from these systems as testing sets to examine the generalization ability of the trained network (original imaging system parameters are given in Section 3.2 with \(\Gamma^{\#}=8\)) for the new systems. The F\({}^{\#}\) of the imaging systems used for simulations were 10, 20, 30, 40, 50 respectively, while the piston error measurement results of all the new systems using the trained network had the same accuracy as those of the original imaging system with \(F^{\#}=8\). This demonstrates that our network has quite good generalization ability. Figure 9: The piston error detection accuracy under (**a**) different CCD image noise values and (**b**) different tip-tilt aberrations.