esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Feature Selection Using Radial Chebyshev Moment Invariants
### Radial Chebyshev Moment Invariants  
The rotation, translation and scaling invariants of image moments have played a significant role in recognizing objects and patterns. Compared with translation and scaling invariants, derivation of invariants to rotation are generally far more complicated. However, due to the existence of circular-harmonic function, the rotation invariants of radial Chebyshev moments can be easily obtained by eliminating the phase component from moment expression.  
Invariants to translation are obtained by using the image centroid \((x_{0},y_{0})\) as origin in polar coordinates, where \(x_{0}=m_{10}/m_{00}\); \(y_{0}=m_{01}/m_{00}\), and \(m_{ij}\) is the geometric moment of order \(i+j\) defined as:  
\[m_{ij}=\sum_{x=0}^{N-1}\sum_{y=0}^{N-1}x^{i}y^{j}f(x,y) \tag{9}\]  
What's more, the pre-processed and edge images are expressed in Cartesian coordinate \((x,y)\). In order to obtain the intensity value, the parameters \(r\) and \(\theta\) should be mapped to the corresponding location of the image pixel. As a result, the mapping between \((r,\theta)\) and \((x,y)\) is denoted by:  
\[\begin{array}{l}x=\frac{rN}{2(m-1)}\cos\Bigl{(}\frac{2\pi l}{n}\Bigr{)}+x_{0 }\\ y=\frac{rN}{2(m-1)}\sin\Bigl{(}\frac{2\pi l}{n}\Bigr{)}+y_{0}\end{array} \tag{10}\]  
For deriving the rotation invariants, it is assumed that the original image is rotated by angle \(\alpha\), and the image intensity values remain constant. Then, the radial Chebyshev moments \(C_{pq}^{R}\) of the transformed image are derived from Equation (5):  
\[C_{pq}^{R}=\frac{1}{n\rho(p,m)}\sum_{r=0}^{m-1}\sum_{l=0}^{n-1}c_{p,m}(r)e^{-jq (\frac{2\pi l}{n}+\alpha)}f(r,\theta)=e^{-jq\alpha}C_{pq} \tag{11}\]  
It can be observed that the rotation angle \(\alpha\) affects only phase but not magnitude. Therefore, the rotation invariants are obtained as shown in the following expression:  
\[C_{pq}^{\prime}=\sqrt{\left(C_{pq}^{(c)}\right)^{2}+\left(C_{pq}^{(s)}\right)^ {2}} \tag{12}\]  
Scaling invariants can be achieved by proper normalization of each moment. In terms of radial Chebyshev moments, it can be seen that the maximum radius \(m\) and the size of object \(A\) are scaled when scaling an image. Consequently, the two parameters are used to normalize the \(C_{pq}^{\prime}\). According to the Equations (5) and (6), the zero-order of \(C_{pq}\) is calculated as:  
\[C_{00}=\frac{1}{nm}\sum_{r=0}^{m-1}\sum_{l=0}^{n-1}f(r,\theta)=\frac{A}{nm} \tag{13}\]  
Observe that, all of the radial Chebyshev moment invariants can be normalized by \(C_{00}\). On the whole, the proposed rotation, translation and scaling invariants of radial Chebyshev moments are derived as follows:  
\[\widetilde{C}_{pq}=\frac{C_{pq}^{\prime}}{C_{00}} \tag{14}\]
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 1 Introduction
### High-Speed Commercial Transportation  
The epitome of successful high-speed (i.e., supersonic) commercial transportation aircraft was the Concorde, a joint collaboration project between Britain and France which revolutionised commercial air travel by cutting down air journey time by half--bridging London and New York with only a 3.5-h flight time. At the same time efforts in the then Soviet Union resulted in the Tu-144 aircraft which was, however, less successful and short-lived. The Concorde was capable of Mach 2 cruise speeds, powered by four Rolls-Royce/Snecma Olympus 593 afterburning turbojets capable of producing a maximum installed thrust of 152,200 lbs [3].  
However, the significant fuel consumption coupled with escalating fuel prices and fears of another oil crisis caused its retirement as its popularity plummeted.  
Presently, there exists a large gap in the market, primarily business travellers, for the potential revival of supersonic, or better yet hypersonic travel. Focussing available resources on the design and development of airliners capable of achieving hypersonic cruise speeds can be achieved by extending the research on air breathers.
## 4 Results
### RQ1: Contributions of WSN in IR 4.0  
The use of WSN has attracted a lot of attention in industry. Because of their prevalence and use in industry, WSN have given rise to IWSN and IWSAN, respectively. These networks enable autonomous work without human intervention. The in-network transmission characteristics are fundamental properties of WSN. Sensor nodes do not transmit raw data, but integrate it to save communication costs. Due to their unique properties and wide range of applications, they are used in many systems, such as military, surveillance, home automation, smart cities, smart buildings, and healthcare monitoring [27]. WSN- and IoT-based devices are used to create reliable, realistic, efficient, flexible, and economical smart cities and buildings in heterogeneous environments [48].  
The categories discussed in this paper and the contribution of WSN in IR 4.0 are listed in the form of a taxonomy presented in Figure 8. WSN is also used in health care management systems to monitor medically ill patients, periodically check their various measurements such as blood glucose levels and pulse, and wirelessly transmit this information to a central repository for further diagnosis [49]. WSN is also used to assist elderly and disabled people. Disabled people are informed of relevant information about real-time activities using smart devices, such as wristwatches [28; 50]. In recent decades, WSN have been applied in many fields, including transportation, agriculture [51], automation, manufacturing process control, and supply chain management. In addition, WSN  
Figure 8: Taxonomy of existing studies.  
can be easily deployed, have low construction cost, no expenditure on wiring, and lower complexity [52; 53].  
WSN can be used in various manufacturing applications, such as industrial control, process automation, rescue, and defense. WSN is also used to control and automate industrial processes known as actuators. They can operate independently of a physical environment defined by predefined dimensions [54]. WSN is used to collect, track, and record data in smart factories. Data acquisition is usually done by product information in smart factories. After data collection, processing is done by intelligent machines and manufacturing systems. Nowadays, these factories are self-sufficient, cost-effective, and automated by integrating wireless communications with existing private networks and reducing labor [30].  
In software, WSN takes maximum advantage of wireless technologies used to build industrial network infrastructure [55]. On the other side, Industry 4.0 is integrating big data analytics and cloud services [56], 3D printing, computer security, autonomous robotics, the Internet of Things (IoT), 5G, Augmented Reality (AR), and modeling [57; 58].
## 3 Fluctuation Function and Decoding of the Proposed System
### Image Recovery  
The inputs of image recovery are recovered codewords \(\tilde{\mathbf{c}}\) and decrypted pixels \(p^{\prime}\). The recovered codeword bits \(\tilde{\mathbf{y}}\) are calculated by applying the process from (1) to (5) where recovered codewords \(\tilde{\mathbf{c}}_{v}\) are used instead of \(\mathbf{c}_{v}\). From the decrypted pixels \(p^{\prime}_{i,j}\) calculated in image decryption, the recovered pixels \(\tilde{d}^{\prime}_{i,j}\) are expressed as:  
\[\tilde{d}^{\prime}_{i,j}=\begin{cases}\overline{p^{\prime}_{i,j}}&\text{for }(i,j )\in S_{0}(a,b)\text{ and }\tilde{y}(a,b)=0\\ \overline{p^{\prime}_{i,j}}&\text{for }(i,j)\in S_{1}(a,b)\text{ and }\tilde{y}(a,b)=1\\ p^{\prime}_{i,j}&\text{otherwise},\end{cases}\]  
where \(\overline{p^{\prime}_{i,j}}=p^{\prime}_{i,j}\oplus h_{l}\) and \(\tilde{y}(a,b)\) is an element on the \(a\)-th row and the \(b\)-th column in \(\tilde{\mathbf{y}}\).  
The PSNR can be used for evaluating the image recovery performance and can be defined as:  
\[\text{PSNR}=20\cdot\log_{10}(\text{MAX})-10\cdot\log_{10}(\text{MSE}), \tag{24}\]  
where MAX denotes the maximum value of pixels in the image and MSE is the mean squared error defined as:  
\[\text{MSE}=\frac{1}{nqN_{\text{c}}}\cdot\sum_{i=0}^{s\lfloor\frac{k}{s} \rfloor-1}\sum_{j=0}^{\lfloor\frac{M}{s}\rfloor-1}(p_{i,j}-\tilde{d}^{\prime} _{i,j})^{2}.\]  
If the MSE is zero, the PSNR is infinite.
# How Do Humans Recognize the Motion Arousal of Non-Humanoid Robots?
## 3 Theory and Hypothesis
### Hypothesis
* The instantaneous velocity \(V_{s}\) of the robot's moving component at time \(t\) has the following relationship with the displacement \(S\) of the component throughout the movement: \[S(t)=\int_{0}^{t}v_{s}(t)dt\] (1) That is, when the end of the movement is at time \(t_{e}\), then: \[\int_{0}^{t_{e}}v_{s}(t)dt=S_{\text{max}}\] (2) In this study, different experimental groups all move at the same speed, so it is considered that the velocity \(V_{s}\) of the robot's moving component is a constant, therefore: \[V_{s}=\int_{0}^{t}v_{s}(t)dt\] (3) Since this study primarily focuses on the relationship between the amplitude of vertical displacement and emotional expression, the changes in instantaneous velocity do not constitute the dominant factor affecting emotional recognition. The above constitutes the input section of the model. Output Definition: Emotional Arousal is denoted as \(A\). This study refers to the values of the PAD (Pleasure-Arousal-Dominance) scale, selecting from four groups of descriptive phrases related to arousal using a 9-point rating scale, and finally taking the average of these ratings. Therefore, based on the results of the PAD scale, the range of emotional arousal values in this study is denoted as \(A\in[A_{l},A_{h}]\), with \(A_{l}=-2\), \(A_{h}=2\) in this example. \[A=\Omega_{1}(\partial,m,t) \tag{4}\]  
Among them, \(m\) represents the variable of motion amplitude, and \(t\) denotes the time. Since this study assumes that the component moves at a constant speed, the model is simplified as follows:  
\[A=\Omega_{2}(\partial,m) \tag{5}\]  
In this study, emotions are categorized into three types based on arousal level: high arousal, medium arousal, and low arousal. Therefore, the feasible domain of \(M\) is dividedinto three categories, with the boundary points of displacement proportion being \(\beta_{1},\beta_{2},\beta_{3}\), where \(\beta_{1},\beta_{2},\beta_{3}\in[0,\partial]\). Therefore, the expression models corresponding to the three types of emotions are as follows:  
\[ \begin{array}{l}E_{A1}=f(A_{1})=\Omega_{2}(\beta_{1},m)\\ E_{A2}=f(A_{2})=\Omega_{2}(\beta_{2},m)\\ E_{A3}=f(A_{3})=\Omega_{2}(\beta_{3},m)\\ \end{array}  \tag{6}\]  
In this study, we set the values as follows:  
\[ \begin{array}{l}\beta_{1}=1/3\partial\\ \beta_{2}=2/3\partial\\ \beta_{3}=\partial\\ \end{array}  \tag{7}\]  
The calculation method for \(f(A)\) is based on the PAD (Pleasure-Arousal-Dominance) scale, with specific details found in the experimental scale of the following experiment.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
###### Abstract  
Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.  
MFNN; handover; mobility prediction; ANSF 202110.3390/electronics10162016  
10.3390/electronics10162016
## 2 Experimental Procedures
### Test Procedure
#### 2.3.1 Flexural Strength (Static Bending)  
Static bending tests were conducted to determine the flexural properties, such as modulus of rupture (MOR) and apparent modulus of elasticity (MOE). Static bending tests were conducted on samples in accordance with ASTM D1037-06 Section 9 [20]. To obtain MOE and MOR for the particleboards, a three-point bending test was conducted (Figure 3). The flexural strength was measured by using rectangular specimens with dimension of \(530\times 76\times 20\) mm. Each flexural strength result was the average of strength for three specimens as replicates. According to ASTM D1037, each sample was loaded with a central loading nose, as well as two support noses with a radius of 25.4 mm each and a span of 24 times the nominal board thickness between them. The test specimens were loaded by lowering the central nose at a constant crosshead rate, which is determined by the geometry of the test specimen from (1).  
\[\mathrm{N}=\frac{\mathrm{zL}^{2}}{6\mathrm{d}} \tag{1}\]  
where:  
N: Rate of motion of moving head, in/min (mm/min),  
z: Unit rate of fiber strain, in/in. (mm/mm) of outer fiber length per minute (= 0.005),  
L: Span, in. (mm), and  
d: Depth (thickness) of specimen, in. (mm).  
Figure 2: Flow chart of particleboard manufacturing process.  
To calculate MOE and MOR for the samples according to (2) and (3), the maximum load reached during the test and the equivalent vertical deflection were recorded.  
\[\text{MOR}=\frac{3\text{P}_{\text{max}}\text{L}}{2\text{bd}^{2}} \tag{2}\]  
where:  
MOR: Modulus of rupture, MPa.  
P\({}_{\text{max}}\): Maximum load, N.  
b: Width of specimen, mm.  
\[\text{MOE}=\frac{\text{L}^{3}}{4\text{bd}^{3}}\frac{\Delta\text{p}}{\Delta \text{y}} \tag{3}\]  
where:  
MOE: Modulus of elasticity, MPa.  
\(\frac{\Delta\text{p}}{\Delta\text{y}}\) : Slope of the straight line portion of the load deflection curve, N/mm (slope between 10% of Pmax and 40% of Pmax as suggested in note 16 of ASTM D1037).
# EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training
## 4 Experiments
### Case Study  
As discussed in Section 3, EANT can filter noise and re-label some of them by negative training. Table 4 shows some examples of the model-refined DSRE dataset. The first column indicates the sentence in the dataset, the second column denotes the original label, and the third column shows the decision made by the model for that sentence. The first sentence is  
Figure 4: Comparison of EANT variant models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **P@100** & **P@200** & **P@300** & **P@Mean** \\ \hline Mintz [9] & 54.0 & 50.5 & 45.3 & 49.9 \\ MultiR [28] & 75.0 & 65.0 & 62.0 & 67.3 \\ MIMLRE [29] & 70.0 & 64.5 & 60.3 & 64.9 \\ PCNN [30] & 72.3 & 69.7 & 64.1 & 68.7 \\ PCNN+ATT [10] & 76.2 & 73.1 & 67.4 & 72.2 \\ PCNN+ATT+Soft [18] & 87.0 & **84.5** & 77.0 & 82.8 \\ BGWA [23] & 82.0 & 75.0 & 72.0 & 76.3 \\ RESIDE [31] & 84.0 & 78.5 & 75.6 & 79.4 \\ A2 DSRE [32] & 87.0 & 79.0 & 77.6 & 81.2 \\ \hline
**EANT(ours)** & **87.6** & 83.2 & **78.6** & **83.1** \\ \hline \hline \end{tabular}
\end{table}
Table 3: P@N of EANT and baselines.  
labeled as relation _\"/people/person/profession\"_ in the original dataset, but we can see that it does not express this relation. The model calculates the confidence value of the sentence and identifies that it is below the threshold \(Th_{d}\) of the relation _\"/people/person/profession\"_, so the sentence is refined as _\"NA\"_. Similarly, the fourth sentence's confidence value is higher than the threshold \(Th_{r}\) of the relation _\"/business/company/locations\"_, so EANT relabeled such false-negative instance with the highest confidence relation label. From these examples, we can see the effectiveness of the model in distinguishing noisy data.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.2 Half-Life Time  
Figure 4 depicts the half-life time of SEP-E, DEEC-E, EBCS, TEEN, and HFECS against different network sizes. It can be seen from Figure 4 that HFECS and EBCS enhance the sensor nodes' half-life time in comparison to SEP-E, DEEC-E, and TEEN. Since the entropy weight coefficients are updated in every communication round, we see an improvement in half-life time and correspondingly better network lifetime. Moreover, as the number of sensor nodes which can be supported (\(N_{support}\)) by a CH implicitly reflects the cluster load which the CH can handle, so we included \(N_{support}\) as one of our significant attributes in CH decision making process.  
The performance of EBCS in terms of half-life time is better than SEP-E, DEEC-E, and TEEN for all the network sizes. In addition, EBCS achieves better half-life time as compared to HFECS for network sizes up to \(200\times 200\) m\({}^{2}\) due to an increase in the cluster load and the extra computation required for calculating link connectivity between sensor nodes in the case of HFECS. For larger network sizes, HFECS performs better than EBCS due to the decrease in the cluster load, involvement of multiple attributes in decision making processes and sensor nodes with opportunistic connections, which finally results in a slight performance inversion between HFECS and EBCS. The percentage improvement of HFECS in terms of half-life time against different clustering schemes is given in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{5}{c}{**Network Size**} \\ \cline{2-7}  & \(\mathbf{100\times 100}\) & \(\mathbf{150\times 150}\) & \(\mathbf{200\times 200}\) & \(\mathbf{250\times 250}\) & \(\mathbf{300\times 300}\) & \(\mathbf{350\times 350}\) \\ \hline SEP-E & 25 & 23.81 & 27.03 & 41.5 & 53.39 & 74.16 \\ DEEC-E & 2.23 & 20.93 & 43.29 & 66.4 & 101.11 & 150 \\ EBCS & \(-\)5.17 & \(-\)3.7 & \(-\)2.08 & 1.96 & 5.23 & 5.44 \\ TEEN & 31.7 & 31.31 & 38.24 & 56.39 & 75.73 & 101.3 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Percentage Improvement of HFECS in terms of half-life time.  
Figure 4: Half-life in terms of rounds against variable network size.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{5}{c}{**Network Size**} \\ \cline{2-7}  & \(\mathbf{100\times 100}\) & \(\mathbf{150\times 150}\) & \(\mathbf{200\times 200}\) & \(\mathbf{250\times 250}\) & \(\mathbf{300\times 300}\) & \(\mathbf{350\times 350}\) \\ \hline SEP-E & 27.88 & 32.55 & 26.09 & 35 & 52.63 & 81.48 \\ DEEC-E & 62.31 & 98.82 & 110.91 & 161.29 & 314.29 & 512.5 \\ EBCS & \(-\)1.86 & \(-\)3.43 & \(-\)3.33 & 12.5 & 34.88 & 58.06 \\ TEEN & 45.52 & 53.64 & 54.67 & 62 & 87.1 & 122.73 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Percentage Improvement of hesitant fuzzy entropy-based opportunistic clustering and data fusion scheme (HFECS) in terms of stability period.
## 3 Experimental Campaigns at FLAME
### Auxiliary Beamline Target Area at FLAME  
The GEM area is a dedicated station located in the FLAME bunker in order to carry out laser-plasma interaction tests between low-intensity pulses and different types of plasma targets.  
The delivered LONG- or SHORT-HRR pulse is focused by a lens on the target, which is located in the center of a small interaction chamber (about \(1\times 1\times 1\) m\({}^{3}\)), in vacuum (\(10^{-6}\) mbar). The focusing lens can be adjusted to achieve the desired spot size, and it is placed on a motorized stage for focal plane adjustment. A camera is installed inside the interaction chamber to check both the focal plane and the transverse distribution. The maximum achievable intensity on target is \(10^{17}\) W/cm\({}^{2}\). Finally, the target can be moved in the X-Y-Z axis using a hexapod with six degrees of freedom (PI H-811.I2 6-Axis Miniature Hexapod, Physik Instrumente (PI), Karlsruhe, Germany). Different gases can be adopted for the plasma target and they can be injected from a dedicated gas bottle or a hydrogen generator.  
This experimental station allows us to carry out experimental campaigns mainly adopting a neutral gas or a pre-formed plasma. In the first case, the maximum achievable laser intensity makes it possible to carry out gas ionization tests inside the capillary through the laser pulse itself or to accelerate low-energy, low-charge electron beams. In the second case, if a pre-formed plasma is adopted (e.g., from a discharge in gas) it is possible to study the guiding of a laser pulse by means of plasma. This configuration is used to investigate the optimal matching conditions between the pulse and a plasma capillary discharge of a few centimeters in length and a few hundred microns in diameter.  
In this configuration, the setup, using capillaries with several dimensions (from 1 cm to 10 cm) into which gas can be injected at different pressures (from 1 bar to 10 s of bars), is equipped with a homemade designed discharge circuit [26] (Figure 2) able to supply a voltage from few kV up to 20 kV to generate a discharge to ionize the gas. This voltage is applied by means of a pair of electrodes placed at the ends of the channel. The achievable plasma density, as well as the ionization level of the gas, thus depends on the initial conditions of the gas channel and the applied voltage [27].  
\begin{table}
\begin{tabular}{l c c c} \hline
**Beamline** & **E (m)** & **\(\tau\) (fs)** & **RR (Hz)** \\ \hline SHORT-HRR & & & \\ Auxiliary and Probe beams & 1–40 & 40 & 1–10 \\ \hline LONG-HRR & & & \\ Auxiliary beam & 60 & \(10^{2}\)–\(10^{3}\) & 10 \\ \hline SHORT-LRR & & & \\ Main beam & \(4.5\times 10^{3}\) & 30 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Fundamental pulse parameters (energy, temporal duration, and repetition rate) of the three available beamlines at the FLAME facility.