esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The basic characteristic of the subjects is shown in Table 1.  
The result related to iPR is shown in Figure 3. TSME on the scale \(k\) = 19 CDF(E(\(k\) = 19)) had the lowest \(p\)-value calculated using the Welch \(t\)-test between two groups (\(p\) < 0.001).  
The Spearman correlation r between CDF(E(\(k\) = 19)) and body temperature is shown in Figure 4. The r values are \(-\)0.757, 0.287, and \(-\)0.830 in all subjects, the non-fever group and the fever group, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Non-Fever Group** & **Fever Group** & **Total** \\ \hline \# of subjects & 18 & 15 & 33 \\ \# of boy & 11 & 10 & 21 \\ age & 10.78 \(\pm\) 3.15 & 10.4 \(\pm\) 2.38 & 10.60 \(\pm\) 2.79 \\ Body temperature (\({}^{\circ}\)C) & 36.28 \(\pm\) 0.30 \({}^{\star}\) & 38.4 \(\pm\) 0.47 & 37.25 \(\pm\) 1.14 \\ \hline \end{tabular} \({}^{\star}\)\(p\) < 0.05 compared to the Fever group.  
\end{table}
Table 1: Basic information of subjects.  
Figure 3: Distribution of CDF(E(\(k\) = 19)) of iPR of 2 groups (0 for the non-Fever group; 1 for Fever group) and the value of \(p\).  
Figure 2: Example of different time scale E(\(k\) = 1–20) of iPR.  
The AUC, calculated from the ROC, of each entropy variables are shown in Table 2. The AUC of CDF(E(\(k=19\))) had the highest value of 0.915, the sensitivity is 0.933, and the specificity is 0.833. Figure 5 showed the AUC distribution of E(\(k\)) and CDF(E(\(k\))) calculated by using iPR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **AUC** & **Sensitivity** & **Specificity** \\ \hline Mean(E(\(k=1\)-\(20\))) & 0.659 & 0.667 & 0.722 \\ std(E(\(k=1\)-\(20\))) & 0.681 & 0.733 & 0.611 \\ Max(E(\(k=1\)-\(20\))) & 0.567 & 0.467 & 0.778 \\ Min(E(\(k=1\)-\(20\))) & 0.657 & 0.667 & 0.778 \\ Area(E(\(k=1\)-\(20\))) & 0.663 & 0.667 & 0.722 \\ E(\(k=4\)) & 0.685 & 0.733 & 0.778 \\ CDF(E(\(k=19\))) & 0.915 & 0.933 & 0.833 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The AUC of variables related to the entropy value of iPR.  
Figure 4: Linear fitting and Spearman r value between body temperature and CDF(E(\(k=19\))) of iPR in (**a**) total subjects; (**b**) non-fever group; (**c**) Fever group.  
For comparison with conventional PRV time series, Table 3 showed the AUC of the above variables calculated by using the PPi series. The AUC of all variables related to the entropy value of the PPi series was lower than the CDF(E(\(k\) = 19)) of the iPR.
## 4 Implementation
### Edge Side
#### 4.2.4 Video Transmission  
A CCTV edge device generally produces video in the H.264 standard format to deliver it to the server side. Most video or image encoding algorithms can achieve high compression performance when a lot of continuous and repetitive color pixels exist within an image or between adjacent images in the video stream. Thus, to replace the background in the original CCTV video with a single color allows us to increase the compression ratio dramatically.  
For the experiment, we prepared H.264 video with the rate of 30 frames per second from 240 images included in the dataset. The size of the original video is 60,746 KB. The video file size varies as the DSSIM threshold value is set differently: 59,703 KB for the threshold 0.1, 43,549 KB for 0.2, 32,969 KB for 0.3, and 25,824 KB for 0.4, respectively.  
Figure 7 shows the change in bitrate per second when those videos were actually streamed over the network.  
Figure 6: Filtering results with different DSSIM threshold values.  
In Figure 7, higher DSSIM threshold values show lower bitrates, while lower DSSIM threshold values result in higher bitrates. Notably, when the DSSIM threshold value is 0.2, we can observe a momentary increase in DSSIM values around the 4-s mark of the video. This occurred because camera shake in the video caused elevated DSSIM values across the entire frame.
## 3 Parametric Analysis
### Mass Ratio  
The parameter radius ratio is defined as \(r_{r}=r/l\). The parametric analysis curves of the mass ratio and the radius ratio are shown in Figure 5. It can be observed that when the mass ratio is constant, the control effect becomes better as the radius ratio increases. There is a coupling effect between the mass ratio and the radius ratio on the control effect. The variation of the fixed quantitative results is not monotonous. For the radius ratio of 40%, 60% and 80%, when the mass ratio is less than 0.6, the control effect increases with the mass ratio. When the mass ratio is greater than 0.6, the control effect begins to decrease as the mass ratio increases. However, when the mass ratio is less than 0.8, the control effect increases as the mass ratio increases for the radius ratio of 20%. When it exceeds 0.8, it decreases as the mass ratio increases. For different radius ratios, when the mass ratio is greater than 0.9, as the mass ratio continues to increase, the control effect decays faster. Therefore, the reasonable interval of the mass ratio parameter is between 0.4 and 0.9.
## 8 Numerical Results
### Sensitivity Analysis  
The sensitivity of the displacements \(x,y\) and \(\theta\) with respect to the design parameters was analyzed by evaluating the gradients of those displacements with respect to the design parameters. As all the links of the manipulator are designed to have the same material, the inertial and elasticity properties of the links are dependent on the mass density \(\rho\) and the modulus of elasticity \(E\) of the links, respectively. Other design parameters affecting both the mass and the elasticity of the links are the cross section geometry. As the links in this case have a rectangular cross section, these parameters are the width \(b\) and height \(h\) of the rectangular cross section. Thus, the design parameters are P = [\(E\), \(\rho\), \(b\), \(h\)]. The gradients of the displacements with respect to the aforementioned design parameters were obtained by using finite difference approach. The difference between the displacements corresponding to perturbed design parameters and the displacements corresponding to unperturbed design parameters is divided by the amount of perturbation. Using SI units for all the design parameters, a perturbation of \(1\times 10^{-5}\) was used for all the design parameters.  
Figure 11: The elastic displacements and the orientation of the end-effector obtained by using the two methods in the case of triangular pulse loads.  
Figures 12-14 show respectively the sensitivity of the displacements \(x\), \(y\) and \(\theta\) with respect to the design parameters. All the figures show that the displacements in all the DOFs are more sensitive to the dimension of the cross section than to both the modulus of elasticity and the mass density of the links material. Furthermore, the displacements in all the DOFs are more sensitive to the height \(h\) of the cross section than to the width \(b\) of the cross section. This is expected as the manipulator is working on the XY plane which is the plane where the height \(h\) is defined. In this case, the area moments of inertia of the links, which affect the elasticity of the links, have a cubic relation with the height \(h\) and have a linear relation with the width \(b\) of the cross section. Hence, a change in the height \(h\) will affect more the elasticity of the links than a change of the width \(b\). Consequently, the rigidity of the links can be significantly increased by increasing the height \(h\) of the links' cross section.  
Figure 14: The sensitivity of the solution \(\theta\) with respect to the design parameters \(E\), \(\rho\), \(b\) and \(h\).  
Figure 12: The sensitivity of the solution \(x\) with respect to the design parameters \(E\), \(\rho\), \(b\) and \(h\).  
Figure 13: The sensitivity of the solution \(y\) with respect to the design parameters \(E\), \(\rho\), \(b\) and \(h\).
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 4 Results  
SatSINS was used for ice navigation in November 2011-2013 in support of the RV _Xuelong_ as she approached the ice edge by the second half of November during each year (Figure 4). Over 2014-2016, on the other hand, SIC in the region were low, and the SatSINS procedures were similar to those taken in 2012. The SIC showed inter-annual variability which can be reflected very well by RV _Xuelong_ ice navigation speed (Figure 4). Large scale MSLP and 10-m wind field on selected days during CHINARE ice navigation periods are presented in Figure 5. The large-scale atmosphere circulation features are useful since they will impact the ice drift pattern and eventually affect preorder of SAR data in the SatSINS.  
Figure 4: Sea ice concentration from the Special Sensor Microwave Imager/Sounder (SSMIS)/Advanced Microwave Scanning Radiometer 2 (AMSR2) for Prydz Bay on 23 November for each expedition: 2011, 2012 and 2013. The vessel track is overlaid as sequence of points coded with colors to indicate the speed.
# Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
## 3 Components for the Beam-Forming Network
### Magic Tee
This solution was simpler to build compared to the double-layer one, but had some drawbacks. The slotline was not enclosed, so the device suffered from radiation loss. In this view, we could expect more losses when the field in the slotline was excited, i.e., when the difference port was fed. Moreover, the band was narrower when compared to the double layer implementation. On the other hand, the isolation was very good, especially between the sum and difference ports, because it was enforced by field symmetry. The final size of the device was 70 \(\times\) 70 mm\({}^{2}\), whereas the core without the transitions to SMA connectors was 37 \(\times\) 37 mm\({}^{2}\). Figure 15 shows the experimental reflection and transmission coefficients of the prototype. Figure 16 shows the isolation among ports.
# Transverse and Longitudinal Energy Flows in a Sharp Focus of Vortex and Cylindrical Vector Beams
## 3 Spin and Energy Flow in the Focal Plane for an Optical Vortex with Cylindrical Polarization
It turns out that the transverse component of the energy flow in the focal plane remains at each point, and, on average over the plane, is equal to zero. This can happen when, in some areas of the focal plane, the beam is still converging, and, in some places, it is already diverging. Or, in other words, optical vortices of orders \((n-1)\), \(-(n-1)\), \((n-2)\) and \(-(n-2)\) propagate along the focal plane and partially along the optical axis but in the opposite direction. Thus, at \(m=0\), when there is only a cylindrical vector beam of order \(n\) at the input, four different energy flows are formed in the focal plane: forward \(W_{0}\), reverse \(W_{2}\) and transverse energy flows directed to the optical axis \(W_{2}+W_{1}\) and from the optical axis \(W_{2}+W_{1}\). The total energy flow in the initial plane is preserved in the focal plane:  
\[\overline{P}_{z,0}=W=\overline{P}_{z+}+\overline{P}_{z-}+\overline{P}_{x,y+}- \overline{P}_{x,y-}=W_{0}-W_{2}+2(W_{2}+W_{1}). \tag{29}\]  
The polarization in the focal plane for field (21) at \(m=0\) (no vortex) is distributed in an unusual way, different from the polarization distribution in the focus of the field (1). Since the longitudinal spin density in the focal plane (23) at \(m=0\) is equal to zero, the SAM vector has only a transverse projection onto the focal plane, and the polarization ellipses centered at each point of the focal plane are perpendicular to this plane. That is, polarization ellipses resemble optical wheels in which the polarization vector rotates in time like wheel spokes. Moreover, at some points, the polarization vector rotates clockwise, and, at other points of the focal plane, rotates counterclockwise. Shown in Figure 2 is a diagram of the wave vector distribution (thick arrows that are perpendicular to the beam cross section) and linear polarization vectors (thin arrows lying in the beam cross section) in the initial plane and in the focal plane for field (21) at \(m=0\). The initial energy flux, equal to the total beam energy, is seen from Figure 2 to be directed along the optical axis \(z\). This energy flow is divided into four energy flows in the focal plane: a flow along the optical axis \(z\), an energy flow in the opposite direction and two energy flows moving towards each other towards the optical axis and from the optical axis. Figure 2: Scheme of the location of wave vectors \(\mathbf{k}\) and linear polarization vectors \(\mathbf{F}\) in the initial plane (21) at \(m=0\), as well as the direction of energy flows \(\overline{P}_{z+}\), \(\overline{P}_{z-}\), \(\overline{P}_{x,y+}\), \(\overline{P}_{x,y-}\) and the location of polarization ellipses in the focal plane. Polarization ellipses are perpendicular to the focal plane. Between the initial and the focal planes, there is a spherical lens, not shown in the figure.
# Respiration Symptoms Monitoring in Body Area Networks
## 1 Introduction  
Hyperhyreosis is a condition that is characterized by the excess production of thyroid hormone [1]. This disorder is primarily caused by the auto-immune system, produces a butterfly-shaped organ in the neck and controls the rate of metabolism. The term hyperhyrodisim refers to the production of the huge amount of thyroid hormone that affects the metabolism rate, as a result increase in shortness of breath (abnormal breathing), mood swings, anxiety, muscle weakness, hair loss and tremors in limbs can be experienced [2]. Sleep apnea is a sleep disorder that causes abnormal breathing which can ultimately cause apnea [3], while sudden infant death syndrome condition is experienced by a newly born baby or around one year old where the baby experience abnormal respiration and then suddenly stops breathing while asleep. The normal breathing rate for healthy person while is resting is 12 to 20 breaths per minute and the abnormal respiratory rate is over 25 breaths per minute. Normal breathing presents a periodic waveform and abnormal breathing waveform is non-period.  
In healthcare environment, the introduction of wireless communication systems has promoted the improvement in the efficiency of patient care and health management [4; 5]. This paper presents S-Band sensing technique (operating at 2 GHz to 4 GHz) that leverage wireless channel information (WCI) obtained using wireless devices such as antenna, card, omni-directional antenna, etc. to monitor and detect the respiratory condition experienced by patient suffering from diseases such as hyperhyreosis, sleep apnea and sudden infant death syndrome (SIDS). The aim is to assist the doctors or caregivers for documenting objective assessments.
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
###### Abstract  
With the fast-paced realization of the Industry 4.0 paradigm, completely centralized networking solutions will no longer be sufficient to meet the stringent requirements of the related industrial applications. Besides requiring fast response time and increased reliability, they will necessitate computational resources at the edge of the network, which demands advanced communication and data management techniques. In this paper, we provide an overview of the network communications and data management aspects for the Industry 4.0. Our global perspective is to understand the key communication and data management challenges and peculiarities for the effective realization of the fourth industrial revolution. To address these challenges, this paper proposes hybrid communications management and decentralized data distribution solutions supported by a hierarchical and multi-tier network architecture. The proposed solutions combine local and decentralized management with centralized decisions to efficiently use the available network resources and meet the requirements of Industry 4.0 applications. To this end, the distributed management entities interact in order to coordinate their decisions and ensure the correct operation of the whole network. Finally, the use of Radio Access Network (RAN) slicing is proposed to achieve the required flexibility to efficiently meet the stringent and varying communication and data management requirements of industrial applications.  
commuications; data; Industry 4.0; Internet of Things; 5G; industrial wireless networks +
Footnote †: journal: Electronics Electronics
## 5 Evaluation Results: Influence of Each Layer to the Overall Performance
### MAC Layer
As a consequence, the network is flooded with RREQs, adding a significant amount of  
Figure 7: The average energy efficiency for different values of _macMaxFrameRetries_ in different traffic scenarios. traffic to the network that may decrease the performance of ongoing transmissions. A high amount of allowed retries reduces the overall amount of route repairs that are initiated during the tests (see Figure 9), decreasing the control message overhead and reducing the network congestion. When using values higher than those allowed by the specification (9, 11 and 13 retries), at all traffic scales, further improvements are observable for nine and 11 retries. However, with a maximum of 13 retries, the PDR and energy efficiency drop and the delay increases. With 13 allowed retries, the congestion created by repeatedly trying to transmit frames at medium and high traffic rates becomes significant, leading to a degradation of the overall performance, as one-hop delays become larger and buffer overflows occur more frequently due to increased buffer occupation times. This also reflects in the energy efficiency, which increases for nine and 11 retries and decreases for 13 retries. Judging from the results obtained in the experiments, it is highly recommended to increase the default value of _macMaxFrameRetries_ for ZigBee HA profile systems to the maximum the specification allows, as it benefits the PDR, delay and the energy efficiency for end-to-end transmissions. Moreover, the  
Figure 8: Link delivery ratio (LDR) for different values of _macMaxFrameRetries_ in different traffic scenarios. Figure 9: Relative amount of route repairs for different values of _macMaxFrameRetries_ taking the default value of three as the base. allowed value range of _macMaxFrameRetries_ should be increased, as further performance gains can be achieved by using values from the extended value range.
## 2 LNA Design Methodology
### 22 nm FD-SOI CMOS Technology  
This work has been designed using GlobalFoundries' 22 FDX technology, which is an advanced 22 nm FD-SOI CMOS process [10; 11]. We created a single-ended, two-stage cascode-cascode mm-Wave broadband LNA. Devices within this technology exhibit reduced off-state leakage current thanks to the presence of a thin buried oxide layer (BOX) and a fully depleted channel [9; 10]. Our design specifically utilizes SLVTNFET (i.e., super low threshold voltage N-MOSFET), which is capable of achieving an impressive peak \(f_{T}\) of approximately 350 GHz and a peak \(f_{MAX}\) of around 370 GHz for the smallest device [11; 12; 13]. Additionally, this technology enables back-gate biasing of the MOSFETs, offering the ability to adjust the threshold voltage (V\({}_{\rm T}\)) through back-gate controls, although we did not employ this feature in our work [13].
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 2 Materials and Methods
### Repellency Assay
#### 2.5.3 Filter Paper Assay Using _Haenaphysalis longicornis_ Nymphs  
The repellency tests of veratraldehyde and DEET at concentrations of 0.09, 0.05, 0.02 and 0.01 mg/cm\({}^{2}\) against _H. longicornis_ nymphs followed KFDA (Korea Food & Drug Agency) guidelines [27]. Two circles were constructed on filter paper (15 cm diameter) by a compass, and the paper was fixed onto a wood board (22 \(\times\) 30 \(\times\) 0.6 cm) covered with aluminum foil. The diameters of the smaller and larger circles were 5 and 13 cm, respectively, and each tested material was evenly applied between them (treated zone, 113.5 cm\({}^{2}\)) one to five times. Ten nymphs collected from the wild environment were placed on the center circle (untreated zone), and the number of ticks crossing or staying in the treated zone was recorded for 5 min. All assays were repeated every 1 h and were performed 10 times. The repellency index was calculated according to the following formula: % repellency = [(C \(-\) T)/C] \(\times\) 100, where C is the number of tick nymphs crossing or staying in the negatively treated zone and T is the number of tick nymphs crossing or staying in the treated zone.
## 3 Experimental Setup and Results
### Datasets  
Table 1 presents the four publicly available datasets from Twitter used in this work. For training the stacking model, three sets, namely train, development, and test set, are needed. The training set is used to train the Base-Level classifiers, and the development set is used to train the Meta-Level classifier. The HatEval dataset was already available in three sets. The datasets were first randomly split into training and testing sets. Furthermore, we divided the training set to obtain 10% for the development set, similar to [PERSON] et al. [29]. This splitting aims to verify the performance of the approach, as it is tested on unknown data. We applied different splits on the dataset to perform a comparison with the state-of-the-art as they used different split percentages on each one.
## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{10}{c}{**The Number of Repeats**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline
**PCA-CNN** & **24** & 37 & 46 & 61 & 71 & **75** & **84** & **86** & **90** & **90** & **92** & **94** & **95** & **97** & **98** \\
**BN3** & 22 & **39** & **58** & **67** & **73** & **75** & 79 & 81 & 82 & 86 & 89 & 92 & 94 & 96 & **98** \\
**CNN-1** & 16 & 33 & 47 & 52 & 61 & 65 & 77 & 78 & 86 & **90** & 91 & 91 & 91 & 93 & 97 \\
**SVM** & 16 & 32 & 52 & 60 & 72 & 71 & 82 & 81 & 82 & 83 & 87 & 88 & 94 & 95 & 97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy rate of subject A in dataset I.  
Figure 4: The information translate rate graph for four algorithms in the dataset I calculated by the average information translate rates of subject A and subject B.  
Figure 3: The accuracy rates of all ten subjects in dataset II. The vertical axis corresponds to the accuracy rate, and the horizontal axis corresponds to the number of repeats. The color curves represent respectively the different classification accuracy rates of each subject and the average accuracy rate (AVG) of all 10 subjects (S1, S2,..., S10) tested by four methods.
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 4 Application  
Incorporating expert judgements is an essential investigation. The treatment is carried out with the use of Matlab Toolbox through the use of Fuzzy logic Designer and Time Series Neural Networks with the result to generate forecast demand in the automotive industry.