esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Test Rig  
The test rig consisted of several components, as shown in Figure 1. The electric motor (electrospindle) was connected to the shaft through a flexible coupling. The maximum rotational speed of the motor was 24,000 rpm. The shaft was supported by two foil bearings, and one of them (bearing No. 2) was modified so that the temperature and deformation of the top foil could be measured. The bearing with thermocouples (bearing No. 2) had its own special support to accommodate the necessary conductors. The diameter of the bearing journals was 30 mm. Vibration acceleration in three directions was measured on the bearing supports (Figure 1a). Eddy current sensors (positioned at a 45-degree angle to the shaft axis) and laser sensors, placed on additional supports, were used to measure displacement both in the Y direction (direction of the gravitational force) and X direction (direction opposite to the direction of the gravitational force). Two types of sensors were used to reduce the measurement error (Figure 1b). The axial displacement of the shaft was also recorded.  
In a traditional foil bearing, it is difficult to measure the temperature and deformation of the top foil. In the method used [16], which is based on contact between the thermocouple tip and the foil, there was uncertainty with regard to the correct connection between the temperature sensors and the surface of the foil. A decision was made to change the way the thermocouples were mounted and integrated into the foil (Figure 2a,b). This involved permanently connecting the temperature sensors on the outside of the top foil. The thermocouples were arranged on the foil, forming a 3 \(\times\) 6 grid whose dimensions are shown in Figure 3a. Strain gauges were also welded to the foil. A detailed description of the entire installation procedure can be found in articles [17; 18].  
The foil bearing tested consisted of three bump foils and a single top foil made of Inconel 625. In addition, the top foil had been coated with a Teflon-based anti-friction material. The bearing had a nominal diameter of 30 mm and a width of 40 mm. The bump foil had notches, placed across its width, which allowed the passage of conductors towards the top foil (Figure 3b). The sleeve also had similar notches.  
Figure 1: (**a**) Front bearing support (front view); (**b**) Rear bearing support with measuring equipment (rear view).  
Figure 2: (**a**) Tested bearing (second bearing); (**b**) Angular thermocouple distribution on the top foil.
## 4 Literature Review on Supervised Learning Based Weather Nowcasting
The proposed model predicted whether radar echo values will be higher than 35 dBZ in 30 min, thus modelling the problem as a classification problem. The authors modeled the input radar data as multi-channel 3D images and proposed a CNN model that performs cross-channel 3-D convolution. In their model, the output is also an 3D-image, where each point of the image is a 0 if radar echo is predicted to be \(\leq\)35 dBZ in 30 min and 1 otherwise. A Critical Success Index (CSI) score of 0.44 was obtained. [PERSON] et al. [42] proposed a model for precipitation nowcasting employing a convolutional architecture using multihead attention and residual connections (MAR-CNN). The data fed into the network consists of radar reflectivity images on three elevation levels and other numerical features, such as cloud movement speed. In order to deal with the unbalanced classes of precipitations, extreme meteorological events have been oversampled. The proposed model outperformed several deep learning baselines obtained by using only several of the MAR-CNN components--a dual channel convolutional attention module, a dual channel convolutional model, a single channel convolutional model, as well as a GBDT and an SVM.
# Application of Tucker Decomposition in Temperature Distribution Reconstruction
###### Abstract
In this study, a method based on Tucker decomposition algorithm is proposed for the rapid and reliable reconstruction of the 3D temperature field using sparse measurements, and it was verified by simulation data and experimental data, which provides a new solution for temperature distribution measurement.
# OSS Project Stability Assessment Support Tool Considering EVM Based on Wiener Process Models
## 1 Introduction  
Traditionally, the method of Earned Value Management (EVM) [1] is used in the progress assessment of actual software projects under various IT companies. Also, many Open Source Software (OSS) are managed in OSS project by using fault big data recorded on the bug tracking systems. Recently, OSS are used under the various situations, because OSS are useful for many users to make cost reduction, standardization, and quick delivery. Moreover, the methods for reliability assessment of OSS have been proposed by several researchers [2; 3; 4]. Also, our research group has proposed and discussed the method of reliability assessment for the proprietary software and OSS [5; 6; 7; 8]. However, the researches focused on the software effort expenditures of OSS have been not proposed.  
Moreover, the effective methods assisting dynamic testing management for OSS development paradigm as typified by the open source project are few in number [9; 10; 11]. Also, many OSS are used in various area such as cloud computing, mobile cloud, and server software. For example, several research papers [12; 13; 14; 15; 16] have been proposed in the area of cloud computing. However, these papers focus on the security, service optimization, secure control, resource allocation technique, etc. The research papers in terms of project management for clouds by using OSS are few in number.  
In particular, it is important to appropriately manage the quality according to the progress status of OSS project, because the software failure is caused by the poor handling of effort control as shown in Figure 1. Figure 1 shows the difference in the data sets between the proposed method and the existing reliability assessment one. In particular, our method can totally assess the development phase by using the effort data. Also, the appropriate control of management effort for OSS will indirectly link to the quality, reliability, and cost. Moreover, it is useful for OSS project managers to decide the maintenance time.  
In this paper, we discuss the OSS effort estimation model by using the stochastic differential equation model. Also, this paper discusses a useful method of OSS project management based on the earned value analysis considering the irregular fluctuation of performance resulting from the characteristics of OSS development and management. Moreover, we formulate the optimal maintenance problem based on the proposed effort estimation model and extend it to the optimization problem with requirements specification based on the proposed model by stochastic differential equation. Then, we find the optimum maintenance time by minimizing the total expected software maintenance effort by using the cost performance index. In particular, we develop the OSS project stability support tool considering EVM. Furthermore, several performance illustrations of the OSS project stability support tool are shown by using the effort data in actual OSS project.
# Generating Robotic Speech Prosody for Human Robot Interaction: A Preliminary Study
## 4 Results  
### Experiment A  
The rate of correct answers in the sessions corresponding to the neutral, positive, and negative voices are given in Figure 4. The participants showed a better understanding when talking to [PERSON] with either a positive (314 Hz) or negative voice (251 Hz) than with a neutral voice (292 Hz). The percentage of correct answer was simply calculated that the total number of questions divided the number of correct answers. The result was 60.48% when the participants listened to the neutral voice of robot. 88.57% was indicated for interacting with the positive voice. During listening to the robot with negative voice, the percentage of correct answer was 93.81%. The results showed a statistically significant difference between groups as determined by one-way ANOVA (F(2,60) = 14.681, \(p<0.001\)). A [PERSON] post-hoc test revealed that the listening score with the neutral voice was significantly lower than that with the positive voice (\(p=0.001\)) or negative voice (\(p<0.001\)).  
The participants answered the questionnaires, and the populations are indicated in Figure 5. More than 70% of the participants in all sessions responded that the NAO speech was easy to understand. Less than 40% of the populations felt the emotional feeling during their conversions.  
Figure 4: The average percentage of correct answers to the questionnaire. The rate in session interacting with neutral voice of robot showed 60.48%; the one with positive voice was 88.75%; the one with negative voice was 93.81%, \"\" means \(p<0.001\).  
Figure 5: The population of positive answers to understanding and emotion in each robotic speech. The green bar shows whether it is understandable, and the black one indicated the feeling of emotion into the robot.
# Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
## 2 Mathematical Model of PMSM  
The PMSM is a nonlinear system, strong coupling between its mechanical and electrical variables. The differential equations that define the dynamics of the motor in a frame of reference \(dq\) can be defined as:  
\[\frac{di_{d}}{dt}=-\frac{R_{s}}{L_{d}}i_{d}+\frac{PL_{q}}{L_{d}}i_{q}\omega+\frac {1}{L_{d}}u_{d} \tag{1}\]  
\[\frac{di_{q}}{dt}=-\frac{R_{s}}{L_{q}}i_{q}-\frac{PL_{d}}{L_{q}}i_{d}\omega- \frac{P\lambda_{m}}{L_{q}}\omega+\frac{1}{L_{q}}u_{q} \tag{2}\]  
\[\frac{d\omega}{dt}=\frac{1.5P\lambda_{m}}{J}i_{q}-\frac{b}{J}\omega-\frac{1}{ J}T_{l} \tag{3}\]  
where \(i_{d}\), \(i_{q}\), \(u_{d}\) and \(u_{q}\), are \(dq\) components of the current and the voltage in the stator respectively, \(R_{s}\) is the stator resistance, \(P\) is the number of pole pairs, \(\lambda_{m}\) is the flux generated by the permanent magnet of the rotor, \(L_{d}\) and \(L_{q}\) are the \(dq\) components of the stator winding inductance, \(b\) is a viscous damping coefficient, \(J\) is the inertia moment and \(T_{l}\) is the load torque. In case the PMSM is surface mounted permanent magnet type, \(L_{q}\) is equal \(L_{d}\), consequently, the electromagnetic torque \(T_{e}\) can be defined as follows:  
\[T_{e}=1.5\frac{n_{p}}{2}\lambda_{m}i_{q} \tag{4}\]  
where \(n_{p}\) is the number of poles. The dynamic equations of the mechanical variables of the PMSM are expressed as  
\[\frac{d\omega_{r}}{dt}=\frac{1}{J}(T_{e}-T_{l}) \tag{5}\]  
\[\frac{d\theta_{r}}{dt}=\omega_{r} \tag{6}\]  
where \(\omega_{r}\) is the mechanical speed of the rotor and, \(\theta_{r}\) is the position of the rotor.
## 1 Introduction  
Recently, small unmanned aerial vehicles (UAVs) have frequently been used for military and civilian applications [1; 2; 3; 4]. The risks involving small UAVs are problematic in both areas because the application can monitor private activities, collect data, and even directly attack [5; 6].  
In order to neutralize the risk from small UAVs, RF jamming is frequently considered [7; 8; 9]. The jammer-to-signal power ratio (JSR) can be used as an indicator to show the effectiveness of the jamming [10]. In a UAV jamming link, the JSR can be defined as the ratio of the received power by the UAV receiver of jamming signal to the signal for mission performance. Both signals can be estimated by the Friis transmission equation by providing such parameters as the gain, polarization loss factor (PLF), and positions of the transmitter antenna and the receiver antenna, transmission power, and frequencies [11].  
Unlike many other larger aircraft, small UAVs suffer greatly from unpredictable attitude changes induced by the wind, which then cause changes in the gain and polarization of the antenna mounted on the UAV as the directions to other antennas are varied. When considering the effectiveness of jamming, it is frequently assumed that a fixed value of the polarization loss exists for all configurations [12], or polarization loss is not taken into account in the jamming link [13; 14; 15]. However, polarization loss can have a significant impact on the jamming link.  
In this paper, we propose a method to analyze the variation of the jamming power received by the UAV receiver in the UAV jamming link due to UAV attitude changes. The antenna is assumed to be fixed to the UAV body, and thus, the antenna radiation characteristics can be described in reference to the local coordinates of the UAV body. The attitude change is modeled by three successive rotations using roll, pitch, and yaw which are defined as UAV rotations.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 5 Experiments and Results
### Results
#### 5.4.3 Results on the ICDAR2017 Dataset  
Table 7 shows the results on the ICDAR2017 dataset, which contains text lines that are relatively easier to segment. The most significant challenge is consequently touching components among several text lines in the CB55 book. Hence, the UTLS method [22] performs worse on CB55 than the others. The intense touching components among the text lines confuses the statistical supervision of the network. The proposed blob line detection is successful regardless of how the touching components fill the interline space because the global pattern of the document is monotonic. Our pixel-extraction method deals with touching components by labeling each pixel based on the blob line closest to it (Figure 14). The documents contain an almost regular global pattern, which is an advantage of the proposed method. As we discuss in Section 6, the clustering phase depends on the existence of consistent interline spaces and text line heights. The proposed method outperforms [11] because it merges the broken blob lines. System-9+4.1 outperforms the proposed method on both CB55 and CSG863, and performing on par the proposed method on CSG18. While the proposed method is not adapted for a specific script or dataset, System-9+4.1 was developed for the ICDAR2017 dataset [24]. The FCN+EM method [15] surpasses all other methods, but is supervised by human-annotated blob lines.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CB55**} & \multicolumn{2}{c}{**CSG18**} & \multicolumn{2}{c}{**CSG863**} \\ \hline  & **LIU** & **PIU** & **LIU** & **PIU** & **LIU** & **PIU** \\ \hline
**Unsupervised** & & & & & & \\ UTLS [22] & 80.35 & 77.30 & 94.30 & 95.50 & 90.58 & 89.40 \\
[11] & 93.45 & 90.90 & 97.25 & 96.90 & 92.61 & 91.50 \\ System-9+4.1 [24] & 98.04 & 96.67 & 96.91 & 96.93 & 98.62 & **97.54** \\ Proposed method & 96.01 & 92.40 & 97.25 & 96.90 & 93.75 & 91.99 \\ \hline
**Supervised** & & & & & & \\ FCN+EM [15] & 100.0 & 97.64 & 97.65 & 97.79 & 100.0 & 97.18 \\ \hline \hline \end{tabular}
\end{table}
Table 7: LIU and PIU values on the ICDAR2017 dataset.  
Figure 14: CB55 contains touching components among several text lines. The left image shows the segmented text lines by pixel labels. The right image shows only the touching components that have been successfully split.
# The Development and Analysis of a Multistage Spraying Method for Liquids in an Ultrasonic Field
## 3 Ultrasonic Emitter
A vacuum aspirator was used to collect the aerosol droplets. The selected droplets corresponded to the aerosol after the first stage of spraying. 2. At a distance of 50 mm from the lower end of the tubular emitter, to determine the dispersion characteristics of the aerosol generated as a result of the multistage spraying of liquids. Since three ultrasonic vibration anti-nodes are formed along the axis of the developed emitter, it will provide three stages of droplet breakdown. Visual observation and analysis of the dynamics of deformation and destruction of the liquid droplets in the sound field were performed using a \"VideoSprint\" high-speed video camera. The frame rate was 5000 frames/s. Water, aqueous solutions of glycerol (of different viscosity), and alcohol (of different surface tension) were used as sprayed liquids.
# Offline Payment of Central Bank Digital Currency Based on a Trusted Platform Module
## 4 Results and Discussion
### Verifiability and Anonymity  
As elucidated in this section, each token possesses a digital signature and is accompanied by an ID certificate that facilitates the verification of the token. Furthermore, all tokens can be authenticated concerning their association with the input tokens at the time of their creation, which can be corroborated by the total amount and the transaction number. With respect to anonymity, the names of the token owner and endorser, referred to as \"cbOwn\" and are designated as the subject names of the ID certificate. These names are independent of the subscriber and are assigned by the CBDC institution overseeing the OffCW.  
Table 4 provides a summary of the countermeasures associated with the proposed model and its security analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Offline Digital Currency Threats** & **Proposed Model** \\ \hline Token forgery and non-repudiation & - Token is backed by guarantor’s signature (also prevents fraud by owners) - TPM is accessible only to authorized OffCW and the digital signature key is not exposed to the outside of TPM \\ \hline Digital wallet breach & - Critical information (e.g., encryption key SRKx) is inside the TPM and most of the information is stored encrypted - Some security countermeasures are more necessary, such as TEE, secure coding, limiting the amount of token held, and limiting the number of token exchanges \\ \hline Double spending & - The TPM monotonic counter that changes with each transaction to prevent double spending \\ \hline Verifiability & - Token has the signature with ID certificate - To create a new token, existing valid tokens are required \\ \hline Anonymity & - The ID certificate (OffCW ID) is applied in this model. The ID is an administrative code by CBDC institution, similar to the serial number on a banknote \\ \hline \hline \end{tabular}
\end{table}
Table 4: Security analysis of proposed model.
## 4 Numerical Analysis Results and Data Analysis
### Analysis of Variance (ANOVA)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **SS** & **\(\boldsymbol{\phi}\)** & **\(\boldsymbol{V}\)** & **\(\boldsymbol{F}\)** & **\(\boldsymbol{p}\)-Value** \\ \hline Joint orientation & 4.835 & 3 & 1.612 & 329.249 & \(4.99\times 10^{-19}\) \\ Aspect ratio & 1.033 & 2 & 0.516 & 105.503 & \(2.59\times 10^{-12}\) \\ Cover depth & 1.185 & 1 & 1.185 & 242.08 & \(1.06\times 10^{-13}\) \\ Vehicle speed & 5.638 & 1 & 5.638 & 1151.843 & \(3.76\times 10^{-21}\) \\ Joint orientation \(\times\) Aspect ratio & 0.115 & 6 & 0.019 & 3.905 & \(7.79\times 10^{-3}\) \\ Joint orientation \(\times\) Cover depth & 0.003 & 3 & 0.001 & 0.234 & \(8.72\times 10^{-1}\) \\ Joint orientation \(\times\) Vehicle speed & 0.183 & 3 & 0.061 & 12.463 & \(4.80\times 10^{-5}\) \\ Aspect ratio \(\times\) Cover depth & 0.345 & 2 & 0.173 & 35.264 & \(9.80\times 10^{-8}\) \\ Aspect ratio \(\times\) Vehicle speed & 0.002 & 2 & 0.001 & 0.215 & \(8.08\times 10^{-1}\) \\ Cover depth \(\times\) Vehicle speed & \(7.69\times 10^{-5}\) & 1 & \(7.69\times 10^{-5}\) & 0.018 & \(9.01\times 10^{-1}\) \\ e & 0.113 & 23 & 0.005 & & \\ T & 13.451 & 47 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: ANOVA table after three-factor interaction pooling. As a result of ANOVA, joint orientation (\(F_{(3,\ 29)}\) = 395.416, \(p\) < 0.05), aspect ratio (\(F_{(2,\ 29)}\) = 126.706, \(p\) < 0.05), cover depth (\(F_{(1,\ 29)}\) = 290.73, \(p\) < 0.05) and vehicle speed (\(F_{(1,\ 29)}\) = 1383.32, \(p\) < 0.05) were all significant. However, there was no significant interaction effect for joint orientation\(\times\) cover depth, aspect ratio \(\times\) vehicle speed, and cover depth \(\times\) vehicle speed. Among the interactions of the internal feature factors, the joint orientation \(\times\) aspect ratio and the aspect ratio \(\times\) cover depth showed statistically significant results, while the interaction between the joint orientation and cover depth showed insignificant results. The aspect ratio showed statistically significant results with both the other two internal characteristic factors, joint orientation, and cover depth. Therefore, the shape of the lava tube is an important factor in the interaction of the geometrical characteristics of the lava tube under the vehicle vibration load.
## 3 Results and Discussion
### Model Results
#### 3.2.2 Electron Density  
After applying the electric field, ionization of species was started inside the AC-PPP reactor, producing electrons, which are responsible for most of the mechanisms of the CO\({}_{2}\) splitting. These electrons start moving to the electrodes, where they concentrate, under the effect of the electric field. During their movement, they can collide with other species present in the reactor, producing new reactions, such as ionization, dissociation, etc. The determination of the spatial distribution of electrons is essential to find the kinetic energy of the discharge.  
The electron density inside the plasma reactor for times 2, 6, 10, 14, and 18 ms are shown in Figure 11. During this time, when a positive voltage was applied, the electrons were concentrated in the powered electrode. They moved inside the plasma reactor to the  
Figure 10: Spatial distribution of the electric potential for times (**a**) 2 ms, (**b**) 6 ms, (**c**) 10 ms, (**d**) 14 ms and (**e**) 18 ms for the reactor at 22 kV AC voltage and 1 cm distance between electrodes.  
surface electrode, where they were lost by recombination with its positive charge. When the voltage applied on the electrodes changed, these electrons moved to the ground electrode.  
In the model results, high values of electron density inside the inlet/outlet pipes were observed, which means that these pipes contained high levels of chemical activity even though the electric field was low.  
The experimental results were time-averaged values over the measuring period. Then, to compare with the experimental results, the time averages of the simulated electron density over a period (T = 20 ms) were performed for the radial position r = 15 mm, where the lens was focused. The results of these calculations are plotted in Figure 8b. A close agreement between the experimental results and this averaged result was found, which supports the soundness of the model.