esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Characteristics of the Prosumer Installation
### EV User's Behaviour Scenario  
The impact of EVs on the supplying networks as well as the possibilities of their use for V2G or V2H ancillary services significantly depend on the EV user's behavior. Some typical behavior can be identified on the basis of the factors describing the way of EV exploitation. These are:  
* The distance that a user drives during a day;
* The place where a user charges their car;
* The time when a user charges their EV.  
The literature offers some research on the EV user's behavior regarding daily travelling distances. According to [12; 13; 15; 16], EVs are usually used for commuting and short trips, and the 99.9% of the covered urban daily distances is less than 50 km (the commercial use of EVs is not discussed in this paper).  
Figure 1: Generic structure of prosumer hybrid installation connected to the LV network.  
In the scenario of short-distance travelling and parking on private property most of the day, the EV can be connected to the home electric installation not only for charging but to provide V2H energy storage services, too. Such EV use might be particularly advantageous in the case of a family second vehicle. However, as the EV user's behavior is difficult to predict, some amount of energy should remain intact (energy reserve) to let the user drive at any moment for a short distance. If we assume that the energy needed to make a short trip (less than 100 km) is not higher than 16-20 kWh [26], the capacity of the EV battery eligible for V2H service must be larger to provide a comfortable vehicle range and some spare capacity for energy balancing in the prosumer installation. For example, assuming that the EV is equipped with a 100 kWh battery, 20% of its capacity can be reserved for short daily trips and the rest (up to 80%) can be used for V2H services.
## 4 Results and Discussion
### Multi-Criteria Decision Making Model Based on AHP
#### 4.2.1 Criteria, Alternatives and Hierarchical Structure  
Our previous studies [1; 2; 3] identified a list of 15 influencing factors, which were further validated in the outsourcing industry, that resulted in a list of 10 critical success factors regarding the offshore outsourcing decision of application maintenance. These factors were Employees Skills (ES), Poor Communication (PC), Cost (Cos), Legal Requirements (LR), Infrastructure (Inf), Maturity Level (ML), Frequent Requirements Changes (FRC), Language Barrier (LB), Domain Knowledge (DK) and Project Management (PM).  
Similarly, the alternatives, which were adopted to provide global services, are the Onshore Model (OM), Nearshore Model (NM) and Offshore Model (OfM). The hierarchical structure represents the goals, criteria and alternatives as shown in Figure 5. The highest level of the hierarchical structure represents the goal, which is the sourcing decision of application maintenance. The second level of hierarchy shows criteria that consists of an identified list of critical success factors, whereas the third level consists of alternatives showing the available models for providing global services [56; 80].
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 4 Study Case  
### Example of Calculations Based on Temporal Fractal Dimension  
As shown in Figure 3, the microseismic events in orebody I# were mainly distributed between 13:00 and 16:00 in the morning shift and 18:00 and 21:00 in the middle shift, intercepting these two periods for curve fitting.  
Figure 3: Daily hourly distribution of microseismic events.  
The \(\lgc(t)\) was calculated from the distribution of microseismic events over time. Using the microseismic event time range lgt t as the horizontal coordinate (here, \(t\) was chosen as 0 to 4, 8, 12, 16, 20, 24) and \(\lgc(t)\) as the vertical coordinate for linear fitting, the relationship between \(\lgt\sim\lgc(t)\) for the morning and mid-shift, as shown in Figure 4, had a self-similarity coefficient in the range of 0.955-0.985, indicating a good linear correlation. On this basis, as shown in Figure 5, the distribution of the microseismic time for the subsequent three days was counted, and a fitting analysis was performed.  
From the above analysis, it can be concluded that microseismic events exhibit fractal properties over time, as shown in Figure 6. To analyze this further, we took five time periods from each of the three days from September 16 to 18. The slope \(D_{t}\) of \(\lgt\sim\lgc(t)\), the fractal dimension of microseismic events in time, decreased in the process of rock burst gestation to 0.537 on September 18, indicating partial gestation of rock burst during a three-day monitoring period. The magnitude of the temporal fractal dimension was found to be unrelated to the September 16 increase, which corresponded to the accelerated accumulation of microseismic events over time. However, the decrease in the time fractal dimension on September 18 corresponded to a decreasing trend in the number of microseismic events over time. Hence, a larger fractal dimension during the rock burst process indicates a more intense rock burst and a higher risk of occurrence.  
Figure 4: Daily hourly fractal fit of microseismic events.  
Figure 5: Distribution of microseismic event accumulation and activity time.  
The resulting temporal fractal dimensional values can effectively express grade changes in the rock burst incubation process and more intuitively reflect the distribution characteristics of microseismic time points in a specific time period.
## 2 Related Work
### Calibration Methods
#### 2.4.4 Multi-Camera Calibration
Stereo camera and camera array calibration is dedicated not only to a case when cameras are located close to each other, but also they are aimed at the same direction and there is a selected reference camera which is a point of view of the entire set. The purpose of stereo camera calibration and rectification is to transform 2D images in order to reduce as closely as possible any differences from the ideal case in which camera are parallel to each other without any inaccuracy. Stereo cameras and camera arrays has a function of depth camera. Such devices are used when it is hard or even impossible to take images from around an object in order to retrieve its 3D structure. Stereo camera calibration converts 2D images so that they are sufficient for processing with the use of stereo matching algorithms. Bundle adjustment operates directly on 3D space. Disparity maps obtained from stereo matching algorithms can be converted to distances between a reference camera and objects visible in images [19]. The quality of a resulting depth map depends on the quality of obtained disparity maps.
## 2 Materials and Methods  
### MODTRAN  
Moderate Resolution Atmospheric Transmission radiative transfer code and algorithm (MODTRAN) [16], developed by the U.S. Air Force Research Lab, is widely used to calculate various atmospheric radiative transfer parameters under complex atmospheric conditions from 0 to 50,000 cm\({}^{-1}\), such as transmittance, et al. Models of atmosphere in MODTRAN can be defined by users according to the theoretical calculation or measured data, which makes the simulation and use of MODTRAN particularly flexible. In addition, MODTRAN includes representative models of atmosphere, aerosol, cloud, and rain, and various complex geometric conditions such as horizontal, vertical, inclined upward, and downward transmission, which makes MODTRAN a powerful tool in the calculation of atmospheric radiative transfer parameters.  
Although MODTRAN software provides default parameters for most settings, it would be best if measured data is input in order to approximate the real situation. For atmospheric parameters collection, cloud and aerosol micro lidar to determine the visibility and aerosol extinction profile, automatic sun tracking photometer to measure total atmospheric transmittance and total water vapor, and meterological station to collect the temperature, humidity, wind, pressure, et al., are often employed in the process of atmospheric parameters measurement. Figure 1 shows the workflow for the correction of atmospheric transmission via MODTRAN using measured atmospheric parameters.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Data Sources
#### 3.1.3 Ship-Related Data  
Since 2002, all commercial seagoing vessels are obliged to carry on board an AIS transponder [33], which transmits information about position, speed, heading (direction), and a unique identifier (MMSI) and the type of each ship. Thus, another source of data used in this study is data from Automatic Identification System (AIS) transponders. At the moment, there is no open-access AIS data available for the region and time period, as described in Section 3.1.1, as well as the quality required for this study. The data, however, can be accessed through several commercial providers. For the scope of this study, the AIS data, as well as information about the dimensions of the ships were provided by the Netherlands Human Environment and Transport Inspectorate (ILT). This is the Dutch national designated authority for shipping inspections, has access to commercial databases for the AIS dataset used in this study, and is participating in this research.  
With the aim of the reduction of the number of images where the ship plume cannot be visually detected, in our study, we only focused on ships with a speed that exceeds 14 kt. If two ships move in immediate proximity to each other, only the ship with the highest speed was taken into consideration. From the analysis were also excluded ships that are not involved in global trade, such as yachts, leisure vessels, or research vessels. In Figure 3, the information about the dates used for this study, as well as the number of ships per day studied is depicted. The differences in the number of ships per studied day can be caused by bad weather conditions on the measurement day.  
Figure 2: The NO\({}_{2}\) tropospheric column. Visualized day: 14 June 2019. Studied area: the Mediterranean Sea, restricted by the northern coasts of Libya and Egypt from the south and the south coast of Crete from the north. Black lines indicate ships’ tracks based on information from AIS data. The red area on the right-hand side of the figure corresponds to the land outflow from the variety of land base sources of NO\({}_{2}\). For the convenience of visualization, the presented data were not regridded—the native local size of the TROPOMI pixels are presented in the figure.
# Age and Gender Impact on Password Hygiene
## 4 Discussion
Our research also confirmed known user habits of password creation. The research of a small university staff and students showed that only 30% of users use special characters, while uppercase letters tend to be placed at the beginning, and numbers are placed at the end of a password (36), which is a pattern we called _ULS+_. In our research, only 2.58% of recovered passwords had a special symbol. It is possible that many more of the 8% of passwords that we failed to crack have these characters, but this will not change the statistics substantially. However, we only found 12.21% of our investigated passwords to use the _ULS+_ pattern. On the other hand, we noticed the propensity of Lithuanian users to limit their alphabet to English letters, disregarding the opportunity to strengthen their passwords with specific Lithuanian characters. Table 1 lists two similar dictionaries: Lithuanian words and the Latin version of the same dictionary where diacritic marks were dropped from all letters. The results show that the first dictionary with correctly spelled words has a smaller number of passwords in it. Only 12 passwords having the Lithuanian letters with diacritics (a, c, z, and similar) were recovered in total, and none of them were from these two dictionaries. The use of ASCII-only symbols is a historical artefact of how information technology was developed. Early keyboards and operating systems had only English letters, and the post hoc implementation of various other character sets led to many complications in standardisation. In turn, this taught users to fall back to the simplified alphabet to avoid encoding errors in sensitive areas such as password creation. The same is true for other nationalities; e.g., an exceptionally small number of Rockyou words have specific Spanish letters. Limitations and Strengths  
The analysed data sample represents an active and technologically advanced part of the Lithuanian population. Thus, the findings related to cyber hygiene may overestimate the general population's behaviour. There were twice as many males in this study as females, which could cause skewed results, although the statistical analysis takes the differences into account, and every age group of up to 55 years old has at least 100 women. In addition, we cracked only 92% of all leaked hashes. The remaining passwords are stronger and therefore could influence our findings. However, the main conclusions are unlikely to change, because a larger fraction of males (8.51%) have unrecovered passwords compared to females (5.17%), which is consistent with the main findings. Even though the effect sizes in the statistical analysis are considered small, the number of cases can lead to strong generalisations of the findings and might better reflect the differences between genders regarding password complexity.
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 4 Results and Discussion
### The Robustness of the MentorNet Classifier  
#### 4.2.1 The Impact of Rayleigh Fading  
As known, AWGN and Rayleigh fading are two common noise sources. The samples with AWGN have been tested above. Hence, this subsection will investigate the impact of Rayleigh fading on the accuracy of the MentorNet classifier. In the experiment, the modulation parameters and the number of  
Figure 10: Curves about inter-class classification accuracy versus SNR.  
Figure 9: Confusion matrix with different SNRs: (**a**) SNR = \(-20\) dB; (**b**) SNR = \(-10\) dB; (**c**) SNR = \(0\) dB and (**d**) SNR = \(10\) dB.  
samples in the test set were the same as above. Besides, we assumed that the received signal was a combination of two signals coming from two reflection paths. The gains of these two paths were 0 dB and \(-10\) dB, respectively, while the delay between them was \(10^{-7}\) s. In the meantime, the maximum Doppler frequency shift (fd), induced by the relative motion between the receiver and the transmitter in the propagation of two signals, was supposed as 0 Hz, 1 kHz, 5 kHz and 10 kHz.  
The experimental results are shown in Figure 11. It is worth mentioning that the black and red curves both represent the classification accuracy of test samples with a 0 Hz Doppler shift, but a multipath fading existed in the test samples of the red curves, leading to the relatively low classification accuracy. However, the red curve could also reach 20% at \(-20\) dB SNR and 99% at 10 dB SNR, which was close to the black curve. When the different Doppler shifts existed, the classification accuracy at very low SNR (such as \(-20\) dB) was very similar until the SNR was up to \(-5\) dB. With the further increase of SNR, the difference of classification accuracy increased and a larger Doppler shift corresponded to a lower classification accuracy classifier. When the SNR was 10 dB the classification accuracy of test samples containing Rayleigh fading ranged from 85% to 98%, which is enough for the application in military electronic warfare equipment. These results indicate the MentorNet classifier possesses a great robustness to endure the Rayleigh fading.
## 2 System Overview
### The Startup  
The power link relies on the data link to determine the transmitting power level, but the data link can function only if there is sufficient received power. During startup, the internal implant unit has no stored energy. To solve this problem, during the TcES system startup, the external control unit will issue a very high power to jump-start the internal implant unit. Once the data link is established, the transmitting power will decrease slowly. Eventually, the transmitting power will be controlled according to the power-regulation and the power-boosting mechanisms as previously discussed. The startup procedure will be repeated if the first trial fails. However, if it fails repeatedly due to weak coupling or misalignment between the coupling coils, the external control unit will be shut down for protection.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 4 Results and Discussion  
### Dataset  
An experiment was performed using a dataset obtained from the electrical power subsystem of an in-orbit spacecraft, which contained two-and-a-half-year data with more than 0.6 million samples regarding 8 telemetries, such as voltage, current and temperature of the solar panels. Of the dataset, the early 80% were used for training and the remaining 20% for testing. Of the data samples, 0.15% were labeled anomalous by an expert and the rest were normal. The training dataset was preprocessed to remove errors in the data [42]. The detailed dataset statistics are summarized in Table 1.
# Evaluating Soil Bacteria for the Development of New Biopreparations with Agricultural Applications
## 2 Materials and Methods
### Decomposition of Crop Residues in Horticultural Substrates  
On the basis of their enzymatic activity, five strains were tested for their ability to decompose crop residues. For this purpose, unsterilized dried maize harvest residue was cut into 3-cm lengths. Then, 17 g of the residue was weighed and placed in plastic cuvettes (32 \(\times\) 23 \(\times\) 7 cm) with a volume of 1 L. Suspensions of the tested strains (100 mL/cuvette) were applied to the crop residues.  
The substrates with crop residues in cuvettes were moistened in distilled water to 70-80% water content (measured using a Moisture Meter HH2, Cambridge, UK) and incubated in the dark in a BINDER KB 720 E.6 AS IS incubator, at a constant temperature of 20 \({}^{\circ}\)C (\(\pm\)1\({}^{\circ}\)C) and 80 \(\pm\) 5% relative air humidity (RH). The humidity of the substrates was kept constant by adding appropriate amounts of distilled water when necessary. The quality and decomposition dynamics of the harvest residues were assessed on the basis of weekly visual assessments on a five-point scale (5--undecomposed crop residues, 1--fully decomposed crop residues). The experiment was performed in triplicate for each variant.
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 1 Introduction
Therefore, differently from [21], the imaging is faced into a vertical domain, which is a portion of the plane defined by the along-track and the nadir directions. The imaging strategy is similar to the one proposed in [22; 23]. It formulates the radar imaging as a linear inverse scattering problem and uses the Truncated Singular Value Decomposition (TSVD) inversion scheme to get a regularized solution. Since the TSVD algorithm requires significant computing resources, especially when increasing the amount of data and the size of the investigated domain (in terms of the probing wavelength), the Shift and Zoom strategy [24] is exploited. The herein adopted imaging strategy benefits of a motion compensation (MoCo) procedure. MoCo exploits GPS or CDGPS positioning information to manage radar measurements in such a way to obtain datasets wherein the waveforms appear as collected at a constant altitude and evenly spaced along a straight line, which defines the along-track direction. It is worth pointing out that, like the approaches proposed in [14; 21; 23], the adopted imaging strategy can be implemented in such a way to consider the positioning information in the focusing step directly. On the other hand, the MoCo procedure allows a not trivial improvement of the computational effectiveness because it avoids the computation of the scattering operator, and of its SVD, for each portion of dataset to be processed. In order to evaluate the effect of the UAV positioning accuracy on the imaging capabilities, two experiments were conducted in different weather conditions, i.e., during summer and winter seasons, and by using targets having different geometrical and electromagnetic features. The analysis of the radar imaging performance is carried out in terms of target detection as well as of estimating the relative distance among the targets and their elevation from the ground. The paper is organized as follows. Section 2 describes the technological instrumentation used to realize the M-UAV based radar imaging prototype, the positioning estimation technologies and the signal processing strategy. Section 3 describes the two measurement campaigns conducted and reports also a qualitative analysis of the imaging results. Then, Section 4 discusses the achieved results. Conclusions end the paper.
# Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors
## Appendix A Derivation of Equation (16)  
Applying Equation (4) to smoothed signal samples \(\tilde{y}[i]\) for \(i\in[\tau_{n}-T/2,\tau_{n}+T/2-1]\), \(\text{slope}[n]\) of the samples is given as  
\[\text{slope}[n]=\frac{\sum_{i=\tau_{n}-T/2}^{\tau_{n}+T/2-1}ig[i]-T\tilde{i} \tilde{y}}{\sum_{i=\tau_{n}-T/2}^{\tau_{n}+T/2-1}(i-\tilde{i})^{2}} \tag{17}\]  
where \(\tilde{i}\) and \(\tilde{y}\) are the averages of \(i\) and \(\tilde{y}[i]\), respectively. Define \(j:=i-\tau_{n}+\frac{T}{2}+1\) (we leave \(g[i]\) for simplicity of exposition). Then, we rewrite \(\text{slope}[n]\) as  
\[\begin{split}&\text{slope}[n]\\ &=\frac{\sum_{j=1}^{T}(j+\tau_{n}-\frac{T}{2}-1)g[i]-T(\tilde{j}+ \tau_{n}-\frac{T}{2}-1)\tilde{y}}{\sum_{j=1}^{T}(j-\tilde{j})^{2}}\\ &=\frac{\sum_{j=1}^{T}jg[i]-T\tilde{y}}{\sum_{j=1}^{T}(j-\tilde{j })^{2}}.\end{split} \tag{18}\]where \(\bar{j}\) and \(\bar{y}\) are obtained as  
\[\begin{split}\bar{j}&=\frac{1}{T}\sum_{k=1}^{T}j=\frac {1}{T}\frac{T(T+1)}{2}=\frac{T+1}{2},\\ \bar{y}&=\frac{1}{T}\sum_{j=1}^{T}\bar{y}[i].\end{split} \tag{10}\]  
Therefore, the numerator of Equation (11) is reduced to  
\[\sum_{j=1}^{T}j\bar{y}[i]-T\frac{T+1}{2}\frac{1}{T}\sum_{j=1}^{T}\bar{y}[i]= \sum_{j=1}^{T}\left(j-\frac{T+1}{2}\right)\bar{y}[i] \tag{11}\]  
and the denominator is obtained as  
\[\begin{array}{l}\sum_{j=1}^{T}(\bar{j}^{2}-2j\bar{j}+\bar{j}^{2})\\ =\sum_{j=1}^{T}j^{2}-2j\sum_{j=1}^{T}j+T\bar{j}^{2}\\ =\frac{T(T+1)(2T+1)}{6}-2\frac{T+1}{2}\frac{T(T+1)}{2}+\frac{T(T+1)^{2}}{4}\\ =\frac{T(T+1)}{12}(2(2T+1)-6(T+1)+3(T+1))\\ =\frac{T(T+1)}{12}(T-1).\end{array} \tag{12}\]  
Finally, applying Equations (11) and (12) into Equation (11), and changing \(j\) back to \(i\), we have  
\[\begin{split}\text{slope}[n]&=\ \frac{12}{T(T-1)}\sum_{j=1}^{T} \left(j-\frac{T+1}{2}\right)\bar{y}[i]\\ &=\ \frac{12}{T(T^{2}-1)}\sum_{i=\tau_{n}-T/2}^{\tau_{n}+T/2-1} \left(i-\tau_{n}+\frac{1}{2}\right)\bar{y}[i].\end{split} \tag{13}\]