esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Surface Characterization Results  
#### 3.1.1 Pore Distribution and Size  
This analysis aims to quantitatively and visually characterize sulfonated PEEK specimens' pore size distribution and surface coverage, using data from image analysis and statistical comparisons among replicates. Images of the sulfonated surfaces in Figure 2 show total pore surface coverage. Pore size data collected from ImageJ software revealed that various specimens had outliers in large pore sizes; however, the distribution showed that most pores ranged from 15 \(\upmu\)m to 400 \(\upmu\)m. This distribution is represented in the histogram in Figure 3, which shows similar distribution patterns across sulfonated specimens in the 15 \(\upmu\)m to 400 \(\upmu\)m size range; each bin increases by a size of 40 \(\upmu\)m. The distribution shows an increase in pores ranging from 15 \(\upmu\)m to 100 \(\upmu\)m, then tapers off as the size approaches 400 \(\upmu\)m.  
Figure 2: Digital images of independent replicates sulfonated on three separate days.  
Figure 4a shows that all of the independent replicate specimens have significant differences among the pores that are in the size range of 15-100 \(\upmu\)m (\(p<\) 0.05); however, there is only a significant difference in average pore size from >100-400 \(\upmu\)m amongst the replicates on day one compared to the other two groups as shown in Figure 4. Table 1 shows how the pore counts change in the 15-100 \(\upmu\)m and >100-400 \(\upmu\)m range and total surface pore coverage. Specim pore count is between 3421 and 4259 up to 100 \(\upmu\)m and 1220-1866 between >100-400 \(\upmu\)m. The total coverage of pores on the specimen surfaces ranges from 10.5% to 17.5%. Average pore sizes in Figure 4c for specimens S1, S2, and S3 are 95.4 \(\pm\) 86.1 \(\upmu\)m, 99.6 \(\pm\) 111.2 \(\upmu\)m, and 101.8 \(\pm\) 101.3 \(\upmu\)m, respectively. How the specimens vary in pore size significance can also be seen in Figure 4c (\(p<\) 0.05). The pore depth was found to be 18.02 \(\pm\) 3.23 \(\upmu\)m for sfPEEK-HT samples and 16.39 \(\pm\) 5.48 \(\upmu\)m for the sfPEEK samples. No significant differences were found between the two groups.  
Figure 4: Average sizes of the pores in the range of (**a**) 15–100 \(\upmu\)m, (**b**) >100–400 \(\upmu\)m, and (**c**) all pores on the independent replicates. Specimens with the same letter represent significant differences in pore sizes on each specimen type. Significance noted as \(p<\) 0.05.  
Figure 3: Histogram showing the trend of the number of pores in the range of 15 \(\upmu\)m to 400 \(\upmu\)m for the independent replicates.
## 5 Conclusions  
In this study, a conventional CSA was converted from linear polarization to circular polarization by introducing a shifted feedline and two equal rectangular slots to the circular-slotted patch. This design yielded a 3 dB ARBW narrower than its impedance bandwidth. The proposed method to enhance the 3 dB ARBW involved inserting two asymmetrical rectangular slots and a parasitic patch. Both asymmetrical rectangular slots and the parasitic patch successfully improved the measured 3 dB ARBW of 787.5 MHz by 35.79% from 2.16 to 2.95 GHz. We confirmed that the two asymmetrical rectangular slots and the parasitic patch improved the gain. The measured average gain generated a peak gain of up to 5 dBic at the frequency of 2.2 GHz. The proposed antenna achieved a measured impedance bandwidth of 1.04 GHz, or equal to 47.27%, from 1.77 to 2.81 GHz. Thus, this design can be implemented for applications that require a wideband circularly polarized antenna.  
Conceptualization, P.P.S. and J.T.S.S.; methodology, P.P.S. and F.K.; software, P.P.S. and F.K.; validation, P.P.S. and F.K.; formal analysis, P.P.S.; investigation, P.P.S.; writing and the original draft preparation, P.P.S.; writing, review, and editing, P.P.S., F.K., J.T.S.S. and M.N.; supervision, J.T.S.S.; project administration, J.T.S.S.; funding acquisition, J.T.S.S.  
This research is partially supported by the Chiba University Strategic Priority Research Promotion Program FY2016-FY2018.  
This work was supported by the Indonesian National Institute of Aeronautics and Space (LAPAN); Ministry of Research, Technology, and Higher Education (RISET-PRO).  
The authors declare no conflict of interest.
## 1 Introduction
### Smart Cities and Smart Mobility  
A set of characteristics has been identified as relevant in the context of smart cities [20; 21]: smart governance, smart economy, smart environment, smart people, smart living, and smart mobility.  
Smart mobility includes local, national, and international accessibility, and the availability of communication infrastructure or sustainable and safe transport systems and is aligned with the United Nations (UN) Sustainable Development Goals [22; 23]. Smart mobility is often seen as related to the use of IT to adequately orchestrate services designed to improve urban mobility [24]. In this respect, a wide range of information services can be foreseen, such as intelligent transportation systems [25; 26; 27; 28] or algorithms to infer mobility patterns [29; 30]. These information services might contribute to the reduction of air and noise pollution, traffic congestion, and travel costs, while increasing individuals' safety [24; 31; 32].  
Moreover, smart mobility might facilitate older adults' activities and participation, in line with the goals of active ageing and age-friendly cities and communities' approaches [13]. In its baseline report for the decade of healthy ageing, the WHO states that \"engagement of older people and municipalities can steer the use of digital technology to support enabling environments--and reduce the digital divide between older and younger people\" [1] p. 67, pointing out Chicago as a good example of a city in which the labels of Age-Friendly and Smart City have been brought together.
## 2 VLC System Model for Underground Mining
### Optical Transmitter  
To optimize the effect of communication in underground mines, the light intensity in the space should be distributed as uniformly as possible. According to the work in [9], multiple optical transmitters (LED lamps) should be installed of the ceiling of underground mines. In this manuscript, we consider a specific area portion of the mining scenarios presented, with \(T_{x}\)S located in different positions depending on the mining scenario analyzed. When comparing the distance between the LED's array, the size of the LED lamps can be ignored. Therefore, the LED's array can be considered as a single entity and the luminous power of each LED array is equal to the sum of the LED lamps' luminous power. In our work, and based on [9], each LED array consists of 10\(\times\)10 identical LED lamps. In addition, to simplify the calculation, LED lamps in the same LED array transmit the same signal. Thus, we generalize the concept that each LED's array is considered as an optical transmitter \(T_{x}\). Assuming that each LED lamp of the \(T_{x}\) has the same generalized Lambertian radiation pattern, and as this model is widely used for the light emission distribution of the LEDs [11], the angular distribution \(S(\phi)\) of the intensity radiation pattern is given as [11]  
\[S(\phi)=\begin{cases}\frac{m+1}{2\pi}\cos^{m}(\phi)&\text{if }\phi\in[-\frac{ \pi}{2},\frac{\pi}{2}]\\ 0&otherwise\end{cases}, \tag{1}\]  
where \(\phi\) is the incidence angle and \(m\) is the Lambertian mode number, which is related to the half angle \(\phi_{1/2}\) of the half power emitted by the \(T_{x}\); \(m\) acquire the form of [11]  
\[m=\frac{-ln(2)}{ln\left[cos(\phi_{1/2})\right]}. \tag{2}\]  
The maximum intensity corresponds to \(\phi_{1/2}\)=0. When \(m\) is increased, the \(T_{x}\) beam may be more directional.
# Digital Photoelastic Analysis of TAD-Supported Maximly Arch Distalization
## 1 Introduction  
Several intraoral devices have been reported for the distalization of maxillary molars to achieve non-extraction treatment [1; 2; 3; 4; 5]. However, the mesial movement of anterior teeth was inevitable because of the reaction force generated by the molar distal movement [1; 2; 3; 4; 5]. Temporary skeletal anchorage devices (TAD) have made it possible to distalize molars without mesial movement of the anterior teeth. [PERSON] et al. [6] reported that maxillary molar distalization using the Skeletal Anchorage System (SAS), a plate-type anchor, did not mesialize the anterior teeth. Other reports [7; 8; 9; 10; 11; 12; 13; 14] have demonstrated distalization appliances used with TAD.  
However, this method is not without similar untoward effects, and unexpected molar movements, such as tilting, translation, and other vertical movements, can occur. In addition, this system is difficult to study both in vivo and in vitro. [PERSON] et al. [15] evaluated stress on the tooth root when using a TAD for mandibular molar retraction using a photoelastic model. [PERSON] et al. [16] studied the biomechanics of the sliding mechanism after tooth extraction using a 3D finite element method (FEM). However, no studies have explicitly looked at the retraction of the maxillary dentition with photoelastic analysis.  
Two commonly applied methods for studying biomechanical stress are the photoelastic and finite element methods. Each has advantages and disadvantages. [17; 18] The photoelastic method has the advantage of more closely replicating clinical settings (it is a physical model). However, it is a type of qualitative analysis in contrast to FEM (virtual/theoretical model), which provides quantitative data. However, recent advancements in technology have led to the creation of a digital photoelastic method; with this new method, quantitative analysis is now possible with measurements of compressional andtensional stress as in FEM. In the analysis of the biomechanical forces used in clinical orthodontics, this model, therefore, has great potential.  
The objective of this study was to determine whether the distribution of compressional and tensional stress around tooth roots is influenced by the position of the TAD and length of the retraction hook during the retraction of maxillary dentition using the digital photoelastic strain measurement system.
## 4 Results
### Step Response  
Figures 7 and 8 depict the tracking performance under the first scenario of step trajectory. At \(t=60\) s the fan was activated to inject wind toward the quadcopter. As illustrated in Figure 7, the \(x\) and \(y\) states converged to their setpoint references within approximately 10 s with no overshoot. The z-axis motion demonstrates that the designed technique could track the desired altitude, achieving a time response of 5 s. The wind applied at \(t=60\) s was rejected by the FCS within 7 s with few oscillations of the drone, especially on the \(x\) and \(y\) axes. Euler angles in Figure 8 were maintained around the desired motion, and no oscillation phenomenon was detected. Figure 9 illustrates the real-time 3D trajectory for a step response.  
Figure 8: Experimental Euler angles phi, theta, and psi in the case of a step trajectory.  
Figure 7: Experimental linear positions x, y, and z in the case of a step trajectory.
# Automatic Digital Modulation Classification Based on Curriculum Learning
###### Abstract  
Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and StudentNet, to construct an automatic modulation classifier, which possesses great performance on the test set with \(-18\)-\(20\) dB signal-to-noise ratio (SNR). The MentorNet supervises the training of StudentNet according to curriculum learning, and deals with the overfitting problem in StudentNet. The proposed classifier is verified in several test sets containing additive white Gaussian noise (AWGN), Rayleigh fading, carrier frequency offset and phase offset. Experimental results reveal that the overall accuracy of this classifier for common eleven modulation types was up to 99.3% while the inter-class accuracy could be up to 100%, which was much higher than many other classifiers. Besides, in the presence of interferences, the overall accuracy of this novel classifier still could reach 90% at 10 dB SNR indicting its excellent robustness, which makes it suitable for applications like military electronic warfare.  
deep neural network; modulation classification; curriculum learning; robustness; Rayleigh fading channel +
Footnote †: journal: Applied sciences
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 2 Three-Phased Induction Motor
### Speed Variation and Variable Frequency Drives
#### 2.6.1 Scalar Control (or \(U\!f\) Control)  
For constant load applications, scalar control is suitable because the magnitude is only \(\sqrt{2}.U\) and supply voltage frequency \(f\) are controlled. Phase control is impossible because the rotor position feedback is not possible. Nevertheless, this kind of control is very popular because it is less expensive than the vector control.  
In sinusoidal mode, with the conservation of the \(U/f\) ratio, the magnetic circuit remains in the same magnetic state, whatever the supply frequency. In other words, the shape of the hysteresis cycle of the magnetic materials, both in the stator and rotor, remains identical whatever \(f\). Thus, when \(f\) decreases, the corresponding RMS fundamental voltage \(U\) decreases, thereby avoiding any risk of magnetic material saturation. The other consequence is that a control that keeps \(U_{1}/f\) constant, where \(U_{1}\) represents the fundamental voltage RMS value, and it also maintains the same torque curve as a function of slip for any supply frequency \(f\) by a simple translation on the \(f\) (or \(\Omega\)) axis. All other harmonics give \"zero-average\" pulsating torques.  
When the \(U/f\) ratio is maintained as a constant, the linear part of the torque-speed characteristic can be written as follows: \(T_{em}=K\cdot(n_{S}-n)\).  
Scalar control is limited both at the start and beyond the rated rotational speed. First, at a high torque (high current) starting (low electromotive force \(emf\)), the voltage drop due to the stator resistance is greater than the \(emf\). The nominal flux is then impossible to obtain in the machine due to the law \(U/f=K\). To compensate for this drawback, industrial drives offer different \(U(f)\) laws, depending on the intended application. Then, once the rated voltage is reached, the machine supply frequency can be increased, but without increasing its voltage. This is called flux weakening, which leads, of course, to a drop in the maximum torque the machine can deliver.
## 4 Snake Robot Ethical Issues in SAR Response Phase
### Human-Robot Interaction  
The ability of robots to interact (both directly and indirectly) with victims during SAR is crucial. By taking the interaction feedback from the robot the rescue team can evaluate the situation and take appropriate action. In some circumstances, the robot needs to interact physically with victims to comprehend the situation and save them. When compared to human interactions, robotic interactions are less natural. Additionally, the interactions have to be according to ethical standards (both directly and indirectly). During the interaction, the robot must understand the victim's situation and take proper decisions and avoid irresponsible movements that could hurt victims. From this perspective, the following aspects are considered:  
* Accountability: The degree of robot interaction required with the victim must be evaluated; robot-victim interaction can be fully autonomous or remotely operated. For instance, during the interaction victim may get hurt because of hardware failure even after taking the right interaction decision by the operator or robot. Who is responsible for such an incident? Is it the hardware developer, the operator or the robot (for not looking at the hardware capabilities/failures, who is responsible? It is challenging to hold any participant liable in this scenario for the robot's irresponsible actions.
* Reliability and trust: Consider a scenario in which a robot is ready to assist a victim who is physically injured. A robot tries to help the victim by holding or moving out of the situation. During this situation, will the victim have enough belief that the robot interaction will be safe and will not harm? People might not have clue about the robot's actions and its interactions, which may or may not ease the victim. Will a robot make the right choices consistently under different circumstances and act accordingly? This raises questions on reliability and trust in robot interactions.
* Care and safety consideration: SAR operations carried out by human rescuers typically include victim care and safety. Humans are capable of comprehending both the victim's physical and emotional experiences. Will SAR robots perform with similar care and safety, and comprehend the victim's situation as human rescuers do?
## 3 Results
### Comparison of Basic Parameters: Density, Hardness, and Electrical Conductivity  
Basic parameters for all composites were measured and compared in Figure 9. Changes in the most important properties: softening temperature, relative density, electrical conductivity, and hardness of copper and Cu-Tuff composites are shown depending on the volume share of tuff particles.  
When comparing the sinter of pure copper with the 5% tuff composite, one can see a very clear improvement in properties such as softening temperature, relative density, and hardness. However, as the content of the material increases, the conductivity of the composites decreases. This is a result of a decrease in the active cross-section of the composite due to the presence of non-conducting tuff particles. No diffusion of tuff components to copper or its contamination was found in the study conducted. The purity, and thus the conductivity of the matrix, remains unchanged. The electrical conductivity values of copper and tuff composites correspond to copper alloys used for resistance welding electrodes. The highest values of the relative density of composites were obtained for a tuff content of 15%. Compared to pure copper sinter, it is about a 10% increase in relative density.  
Figure 8: Electrical conductivity of compounds with tuff.  
The softening temperature of the composites studied increases with the increase in tuff amount. Already, 5% of the addition of tuff causes an increase in the softening temperature from 80 \({}^{\circ}\)C to more than 600 \({}^{\circ}\)C. Composites containing 30% tuff per volume have the highest softening temperature. They can work even at temperatures of about 900 \({}^{\circ}\)C. With the increase in the tuff volume share, the hardness of the composites increases after the re-pressing at 1200 MPa. Moreover, for a tuff content of 15%, a decrease in hardness is observed. This effect is caused by a smaller degree of composite deformation, which is connected with obtaining the highest value of relative density for this composition.
# Impact of Sentence Representation Matching in Neural Machine Translation
## 3 Sentence Representation Matching
### Methods
#### 3.2.1 Vector Representation of Sentences  
The proposed method matches two vector representations to evaluate the semantics of source and target sentences. The sentence representation of the source sentence is generated from the output vectors of an encoder model to induce a guiding effect to the model. The representation of the target sentence uses fixed-word vector sequences. The common assumption in building the representations is to use fixed word vectors of an imported external dictionary or using one-hot representation.  
The method used in this paper is defined according to the following equations, where the representations \(\mathbf{r}_{S}\) of a source sentence \(S\) and \(\mathbf{r}_{T}\) of a target sentence \(T\) are defined as  
\[\mathbf{r}_{S} = \sum_{t=1}^{|S|}\mathbf{h}_{t} \tag{1}\] \[\mathbf{r}_{T} = \sum_{t=1}^{|T|}\mathbf{w}_{t} \tag{2}\]  
where \(\mathbf{h}_{t}\) is the hidden vector generated from the top LSTM stacks at time step \(t\) in a NMT encoder and \(\mathbf{w}_{t}\) is the word vector at time \(t\) in a decoder. This representation extraction is not neccessarily the addition of word or output vectors over times. It can be also easily extended to general encoder-decoder models. For example, in bidirectional models, \(\mathbf{h}_{t}\) is replaced by \(\mathbf{h}_{t}^{f}\|\mathbf{h}_{t}^{b}\). In bidirectional attention models, interface vectors are transformed vectors of \(\mathbf{h}_{t}\) with the alignment model and target word, but we can still use \(\mathbf{h}_{t}^{f}\|\mathbf{h}_{t}^{b}\).
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 3 Mathematical Statement and Solution Methods of the Optimization Problem
### Multiobjective Optimization  
In the preceding formulations, Equations (1)-(7), the optimization has one objective only. In reality, there may be two or more desirable goals simultaneously and often competing with each other. For example, it is desirable to have a system of low cost, high efficiency, and low emissions. However, a system of minimum cost may not have maximum efficiency or minimum emissions. In such cases, the system obtained by optimization with a single objective most probably will not satisfy other objectives. In order to address problems of this type, multiobjective (or multicriteria) optimization methods have been developed [33; 34; 35; 36]. In general terms, the problem is stated mathematically with the objective function  
\[\min\left[f_{1}(\mathbf{x}),\;f_{2}(\mathbf{x}),\;\ldots,\;f_{i}(\mathbf{x}), \;\ldots,\;f_{l}(\mathbf{x})\right]^{T} \tag{8}\]  
where \(f_{i}\) is the \(i\)-th objective function, accompanied by constraints, Equations (2) and (3).  
The simplest way of formulating and solving a multiobjective optimization problem is to define the objective function \(F\) as a weighted summation of the individual objectives \(f_{i}\)  
\[F=\sum_{i}w_{i}f_{i} \tag{9}\]  
where \(w_{i}\) are the weighting factors, often subjective. Then the problem is reduced to one of a single objective and can be solved with methods available for problems of Equations (1)-(7).  
More elaborate methods exist, as, e.g., methods that select as optimal solution a point on the Pareto front, such as the point with minimum distance from the _ideal_ one or the point of maximum distance from the _nonideal_ one.  
No matter what is the method, the system obtained usually does not satisfy each objective in separate, but it is the result of a compromise, more or less subjective. Applications on ship energy systems will be presented in Section 4.4.