esa-sceva/satcom-chunk-collection huggingface dataset:
# The Uses of a Dual-Band Corrugated Circularly Polarized Horn Antenna for 5G Systems
## 2 Mode Converter and Circularly Polarized Converter Design  
### Mode Converter  
The input port of the mode converter is a standard WR-28 waveguide, with a TE\({}_{10}\) transmission mode and its E-field type is shown in Figure 2. The output port is a circular waveguide with a radius of 4.2 mm with a TE\({}_{11}\) transmission mode. The E-field pattern of the circular waveguide is shown in Figure 2 as well. Both modes are the fundamental mode of these types of waveguides. In order to make the mode conversion and transmission efficiency of the microwave signal between the two waveguides high, the length of the mode converter must be designed with a low insertion loss. The insertion loss of the proposed mode converter is less than 0.05 dB. The electric field pattern of this structure at 28 GHz and 39 GHz is illustrated in Figure 2a,b, respectively. The TE\({}_{10}\) mode transmitted in the WR-28 waveguide can be smoothly converted into the TE\({}_{11}\) mode of the circular waveguide with low insertion loss.
# Place Recognition: An Overview of Vision Perspective
## 2 Traditional Image Descriptors
### Local Image Descriptors
\begin{table}
\begin{tabular}{c c} \hline
**Abbreviation** & **The Extended Representation** \\ \hline CNN & Convolutional Neural Network \\ SIFT & Scale-Invariant Feature Transformation \\ SURF & Speed-Up Robust Features \\ VLAD & Vector of Locally Aggregated Descriptors \\ BoW & Bag-of-Words \\ FAST & Features from Accelerated Segment Test \\ BRIEF & Binary Robust Independent Elementary Features \\ \hline \end{tabular}
\end{table} 
Table 1: Abbreviations used in this paper and their extended representations. where the gradient of the log likelihood describes the direction in which parameters should be modified to best intuitively fit the observed data. It transforms a variable-length sample **X** into a fixed-length vector whose size is only dependent on the number of parameters in the model. Reference [22] applied the Fisher kernel in the context of image classification with a Gaussian mixture model to model the visual words. In comparison with the bag-of-visual-words representation, they obtained a \((\mathbf{2d+1})*\mathbf{k-1}\) dimensional image representation of a local invariant feature set, while **k** dimensional image representation using the bag-of-visual-words. Thus, the Fisher kernel can provide richer information under the condition that their codebook size is equal, or fewer visual words are required by this more sophisticated representation. Reference [19] proposed a new local-aggregation method, VLAD, which is a state-of-the-art technique compared to the bag-of-visual-words and Fisher vector. The final representation is computed as follows:  
\[\mathbf{v}_{i,j}=\sum_{\mathbf{NN}(\mathbf{x})=\mathbf{c_{i}}}\mathbf{x}_{j}- \mathbf{c}_{i,j} \tag{2}\]  
The VLAD vector is represented by \(\mathbf{v}_{i,j}\), where the indices \(i=1,...,k\) and \(j=1,...,d\) respectively index the ith visual word and the jth component of the local invariant feature. The vector is subsequently \(\mathbf{L}_{2}\)-normalized. We can see that the final representation stores the sum of all residuals between local invariant features and its nearest visual word. One excellent property of the VLAD vector is that it is relatively sparse and very structured [19], showing that a principal component analysis is likely to capture this structure for dimensionality reduction without much degradation of representation. They obtain comparable search quality to bag-of-visual-words and the Fisher kernel with at least an order of magnitude less memory.
## 3. SOH Prediction Method Based on the PSO-GWO-LSSVM Model
### SOH Estimation Based on PSO-GWO-LSSVM and HFs Extraction  
As shown in Figure 8, this section outlines an SOH prediction framework using the PSO-GWO-LSSVM model. Step 1: Extract eight features from the charging voltage profiles. Step 2: Normalize processing for eight HFs. Step 3: Analyze the HFs correlations and handle outliers with Gaussian filtering and LOF. Step 4: Train the model using the training set and optimize LSSVM hyperparameters with PSO-GWO. Step 5: Predict SOH and evaluate performance for four battery estimation results.
## 2 Material and Methods
### Qualitative Analysis of Aroma Compounds  
#### 2.4.1 SPE-GC-QTOF-MS  
The volatiles in wine were extracted by solid phase extraction (SPE) with LiChrolut EN cartridge (500 mg, Merck, Darmstadt, Germany). The blueberry wine sample (100 mL) was diluted with 100 mL of milli-Q water, and filtrated with filter paper (medium flow, Aoke, Hangzhou, China). An aliquot of 40 mL filtered wine was passed through the LiChrolut EN cartridge, followed by 10 mL of milli-Q water. The cartridge was dried by passing through the air for 5 min, and the volatiles were eluted with 5 mL of dichloromethane. The residual water in the eluate was carefully removed using a glass dropper. The vial was then capped and stored at \(-20\)\({}^{\circ}\)C until analysis. The extraction was performed in duplicate for each of the eight biological replicates (four replicates \(\times\) two varieties).  
A 7200 accurate-mass GC-QTOF-MS instrument (Agilent Technologies, Santa Clara, CA, USA) operated in electron impact ionization (EI) mode at 70 eV. MassHunter Acquisition B.06 was used for the determination of volatile compounds. The GC separation was performed using a fused silica HP-5 MS (5% Phenyl Methyl Siloxane, 30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m) column. The GC oven temperature was programmed starting at 40 \({}^{\circ}\)C for 5 min, and increased to 180 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and held for 1 min, then increased to 300 \({}^{\circ}\)C at 30 \({}^{\circ}\)C/min and held for 2 min. The samples were injected by an ALS autosampler (Agilent Technologies, Santa Clara, CA, USA). Splitless injections of 1 \(\upmu\)L of sample (eluate from SPE) were carried out at 250 \({}^{\circ}\)C and ultra-pure grade helium was used as the carrier gas at flow rate of 1.2 mL/min. The interface and ion source temperatures were set to 300 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. A solvent delay of 4 min was used to prevent damage in the ion source filament. QTOF-MS was operated at mass range of \(m/z\) 35 to 350. Mass calibration was performed daily. The in-batch order of all samples analyzed in this study was randomized with one blank sample injection for every 5 samples.
# Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes
## 4 Experiments with Convolutional Neural Networks
### Experiments with Rectified Simple Grapheme Images
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Strokes**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-9}  & **Accuracy** & **Time (s)** & **Loss** & **Accuracy** & **Loss** & **Accuracy** & **Time (s)** \\ \hline “C” & 100\% & \(3.3154\times 10^{4}\) & 0.0126 & 89\% & 0.6058 & 90\% & 2.6648 \\ “\(\sim\)” & 100\% & \(2.6981\times 10^{4}\) & 0.0016 & 90\% & 0.3463 & 87\% & 2.5534 \\ “\(\cap\)” & 100\% & \(2.6975\times 10^{4}\) & 0.0008 & 90\% & 0.4733 & 90\% & 2.5969 \\ “S” & 100\% & \(2.6196\times 10^{4}\) & 0.0058 & 91\% & 0.4791 & 81\% & 2.4709 \\ “U” & 100\% & \(2.7947\times 10^{4}\) & 0.0026 & 89\% & 0.4293 & 90\% & 2.5750 \\ \hline “Grouped” & 100\% & \(1.9607\times 10^{5}\) & 0.0004 & 90\% & 0.4573 & 90\% & 11.3092 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Rectified Simple Grapheme - VGG-16 (epoch = 80). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Strokes**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-9}  & **Accuracy** & **Time (s)** & **Loss** & **Accuracy** & **Loss** & **Accuracy** & **Time (s)** \\ \hline “C” & 91\% & \(1.0918\times 10^{4}\) & 0.7492 & 73\% & 1.2662 & 77\% & 0.8327 \\ “\(\sim\)” & 92\% & \(8.9056\times 10^{3}\) & 1.1301 & 61\% & 1.7404 & 67\% & 0.9581 \\ “\(\cap\)” & 97\% & \(8.9344\times 10^{3}\) & 0.9568 & 69\% & 1.5359 & 77\% & 0.9423 \\ “S” & 92\% & \(8.6529\times 10^{3}\) & 1.0892 & 62\% & 1.7339 & 65\% & 0.8589 \\ “U” & 91\% & \(9.2122\times 10^{3}\) & 1.0614 & 68\% & 1.5047 & 74\% & 0.8495 \\ \hline “Grouped” & 98\% & \(5.1184\times 10^{4}\) & 0.2912 & 69\% & 0.9947 & 70\% & 3.3401 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Rectified Simple Grapheme - ResNet-18 (epoch = 100).
# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 2 Materials and Methods  
### Emotional Speech Corpus  
The GEES (Corpus of Verbal Expressions of Emotions and Attitudes--in Serbian: _Korpus Govorne Ekspresije Emocija i Stavova_) is the first corpus of acted emotional speech recorded in Serbian [16]. Six actors (3 female, 3 male) were recorded while verbally expressing semantically neutral textual material into five basic emotions: anger, joy, fear, sadness, and neutral. The underlying textual material included 32 isolated words, 30 short sentences, 30 long sentences, and one passage of 79 words. The corpus was evaluated by human listeners and reported recognition accuracy was 94.7% [16]. In this study, a part of corpus containing short and long sentences was taken into consideration because it better reflects a real conversation scenario. The isolated words and the passage were omitted from the research. Aiming to have each speaker equally represented, 58 recorded utterances from every speaker in each emotion class were used for the feature extraction. The total number of utterances used in experiments was 1740. It has been pointed out that acted emotions are more exaggerated than real ones [17] and discussed that acted emotions have limited application in real-life situations. Still, by studying the acoustic features of emotional speech on the acted emotion corpus, one can analyze acoustic variations and get insight into acoustic correlates of emotional speech. Those acoustic correlates are, to a greater extent, present in emotions occurring in real life situations or in elicited emotional speech. In that sense, the relationships between the acoustic features and the acted emotions, as well as between the acoustic features and the real life emotions, do not contradict [18]. Using acted emotions in emotional speech recognition is a way to obtain and study generic (maybe universal) expressions of emotions [19]. Additionally, our research setting was to recognize more intensive emotional states which are reflecting more urgent callers. These intensive vocal emotional expressions are more frequent in acted emotional speech corpora than in natural speech corpora.
## 4 Numerical Examples  
### A Three-Point Bending Beam  
This model was first studied by [PERSON] (1981) as an experimental study of the mode I fracture propagation problem [31]. The geometry, boundary conditions, and material parameters of the beam are shown in Figure 8. The tensile strength \(f_{t}\) is 3.33 MPa and the mode I fracture energy \(G_{fl}\) is 137 N/m. The present example predicts the crack propagation path based on the LEFM maximum circumferential tensile stress criterion. Analyzing the single linear softening curve (Figure 5b) and based on the mode I fracture energy \(G_{fl}\), the limit value of the linear softening curve \(w_{c}\) is 0.0823 mm. The results of three crack propagation steps \(a=10\) mm, 20 mm, and 30 mm, for a 20 \(\times\) 200 grid density, are calculated and compared with the results based on the linear elasticity method [24].  
Figure 9a depicts the relationship of the load for three different crack propagation steps and load point displacements (Load-LPD) based on LEFM work. The results are compared with [PERSON]'s work based on the linear elasticity method [12] and experimental results published by [PERSON] [27], as shown in Figure 9. We can see that the elicited results based on LEFM are very different from the experimental data. This is particularly evident for the peak load, which is much higher than the experimental peak. This is because the LEFM-based method is incapable of simulating the energy dissipation of the fracture zone. Based on X-SBFEM, this study has used different methodologies to  
Figure 8: Three-point bending beam for crack propagation (unit mm).  
Figure 7: Key steps of the solution of cohesion.  
solve the cohesive tractions based on the iterative method of linear superposition by simulating the energy dissipation of FPZ. Figure 9b shows the Load-LPD curves for three different crack propagation steps considering FPZ nonlinearities. It can be seen from Figure 9 that the calculated results are in good agreement with the experimental results of [PERSON], which shows that the method used in this study can simulate the energy dissipation of FPZ. Moreover, it can be seen from Figure 9 that the results of the three crack propagation steps are in good agreement with the experimental curve, which shows that different steps have minor effects on the calculated results.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 3 Neural Network-Assisted Polar Code Decoding Scheme
### System Model  
Figure 7 shows the block diagram of the proposed decoding scheme system, which is composed of training and decoding parts. The \"Training Part\" is executed first, followed by the \"Decoding Part\". The Training Part is composed of \"Polar Encoder1\" and \"Neural Network Training\"; the Decoding Part comprises \"Polar Encoder2\", \"Mapper\", \"Noise\", and \"NNA Decoding Scheme\". Note that the Mapper Layer, Noise Layer, and Decoder Layer are trained in the training part, but only the model of the Decoder Layer is saved and called in NNA Decoding. Although the decoder layer decodes \(\hat{y}_{1}^{N_{S}}\) to obtain \(\hat{\hat{u}}_{1}^{N_{S}}\), the purpose of the training part is to obtain the trained Decoder Layer model and not the \(\hat{\hat{u}}_{1}^{N_{S}}.N_{S}\) and \(K_{S}\) denote the code length and information length of the codewords to be trained, respectively, while \(N\) and \(K\) denote the code length and information length of the codewords to be decoded, respectively. \(u_{1}^{N}\) denotes a row vector \((u_{1},\cdots,u_{N})\). The two parts have different inputs, \(\hat{u}_{1}^{N_{S}}\) and \(u_{1}^{N}\), and different diagram structures, but \(\hat{u}_{1}^{N_{S}}\mapsto\hat{y}_{1}^{N_{S}}\) and \(u_{1}^{N}\mapsto y_{1}^{N}\) are mapped in the same way. Taking \(u_{1}^{N}\mapsto y_{1}^{N}\) as an example, consider single-carrier signaling in which \(N\) binary streams \(u_{1}^{N}\) are encoded with a channel encoder  
Figure 5: Schematic diagram of subcode classification.  
Figure 6: Schematic diagram of the decoding process based on traditional subcodes.  
that generates a codeword of length \(N\). The codeword \(x_{1}^{N}\) is then mapped to binary phase-shift keying (BPSK) symbols \(s_{1}^{N}\) by the mapper. Then, \(s_{1}^{N}\) is transmitted over noisy channels by the noise block, and finally, the signal \(y_{1}^{N}\) is received. When the channel noise is additive gaussian white noise (AWGN), \(y_{1}^{N}\) is given by \(y_{1}^{N}=s_{1}^{N}+n_{w}\), \(n_{w}\sim N(0,\sigma_{w}^{2})\). Similarly, \(y_{1}^{N_{S}}\) can be obtained from \(\hat{u}_{1}^{N_{S}}\). Note that \(\hat{u}_{1}^{N_{S}}\) is determined by the Node Recognizer. We can obtain the LLR values through \(2y_{1}^{N}/\sigma_{w}^{2}\), where \(\sigma_{w}^{2}\) denotes Gaussian noise power. In this paper, the channel-side information is assumed to be known at the receiver. The details of the neural network (NN) model and decoding scheme are introduced in the following section.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 7 Numerical Results  
This section provides a numerical analysis of the results obtained with our optimal placement method for two different VoWiFi communication services.  
* Individual VoIP channels. Each ground user has an independent bidirectional VoIP channel.
* Broadcast channel. Every ground user receives a single shared unidirectional VoIP flow.  
For each communication service, we solve our optimal 3D placement problem for an increasing number of users confined in a small area of 100 m\({}^{2}\) as well as in a large area of 10,000 m\({}^{2}\). The results provided are the average of 30 simulation runs and exhibit a 95% confidence interval. On each run, users' location was randomly generated.
## 4 Routing Layer
### Energy Efficiency
Another approach is the one followed by the energy-efficient routing protocol based on physical distance and residual energy (ERP2R) [118] which tries to minimize consumption by improving the delays while taking into account residual energy and distance in order to minimize unnecessary retransmissions. A simpler protocol is the so-called SEANAR [119]. For path calculation, SEANAR adopts a more efficient technique than VBF and greedy forwarding, assigning weights to sensors that have greater connectivity with the base station. Similarly, neighbor information routing (NIR) [120] selects the next hop by applying a greedy approach which provides a greater transmission rate to nodes with a high number of neighbors. With the objective to obtain reduced consumption, this algorithm follows the simple greedy forwarding technique. The main disadvantage is that NIR is only intended for 2D underwater scenarios; however, this approach achieves high efficiency, which is its main design goal. The reliable and energy balanced routing algorithm (REBAR) [121] utilizes a limited broadcast by implementing an adaptive algorithm in order to adjust the propagation range, taking into account energy efficiency. This protocol broadcasts in a specific domain, applying geographic information, instead of in an entire network broadcast, in order to minimize battery usage of the nodes around the sink. Results show that REBAR performance is better than VBF-based protocols in terms of USN lifetime and message delivery. Nevertheless, without mobility, the nodes around the base station deplete the batteries rapidly, impeding the correct functioning of the network.
## Materials and Methods
### UL Rehabilitation Platform
#### 2.1.2 Myo Gesture Arm Band  
The Myo Gesture armband (see Figure 1, left) is a wearable gesture and motion control device working with EMG sensors to measure electrical pulses in the arm [49]. While muscles expand and contract, the armband sends signals wirelessly to other devices. It uses a Bluetooth 4.0 low energy connection, it has a proprietary muscle activity EMG sensor, a 9-axis inertial measurement unit (IMU), a rechargeable lithium-ion battery with one full day use out of single charge and haptic feedback which provides short, medium or long vibrations.  
Deeping into the sensors, the device has 8 EMG sensors and an IMU, containing a three-axis gyroscope, a three-axis accelerometer and a three-axis magnetometer. Thanks to this 9-axis IMU, Myo Gesture armband senses the motion, orientation and rotation of the forearm, used to recognize hand and wrist movements.  
In this paper, the device was used for obtaining a correct EMG monitoring during the performance of the rehabilitation games. By using this armband, the muscle contraction and the arm kinematics were stored for a posterior data analysis. Because of the Myo Gesture armband design, the 8 EMG sensors are located equally distributed in the perimeter of the forearm. The EMG data was streamed at 200 Hz by Bluetooth communication to the PC.  
Moreover, the rehabilitation platform implemented provided the opportunity to visualize in real time the data obtained from the EMG sensors. This functionality allowed to measure the muscle strength that the patient performs during each game.
## 3 Results
### Model Reliability
#### 3.2.4 Statistical Analysis  
Statistical analysis was performed using the Statistical Package of Social Science software (SPSS(r) version 25). To measure the reliability of the proposed system, it was necessary to compare the EMG output collected data via Delsys with that sampled by the proposed system at the same time to evaluate the correlation between the two collected EMG measurements. An in-house program using Matlab(r) was used to analyse and compare the experimental output data from both systems, which were stored in datasheet files to identify the time shifting and matching between both signals in each trial, and to perform other calculations of measurements such as the mean, mean difference, SD, root mean square difference, \(p\) value, and Pearson Correlation Coefficient for all paired  
Figure 13: The main interface of the Getting EMG software (v.1).  
data from both systems. After that, SPSS was used for analysing the data, and descriptive statistics identified any statistically significant differences that existed between the means and standard deviations of both systems. An intraclass correlation coefficient test (ICC) was also used to identify the level of correlation between both signals.  
ICC estimates and their 95% confident intervals (CI) were calculated in SPSS using a single-measure, absolute-agreement, two-way mixed-effects model, and it was found that an excellent interrater reliability existed between the proposed device and the Delsys(r) system. As shown below in Figures 14-19, three different examples of 200 ms of EMG data were recorded by the Delsys(r) system and sampled by the proposed system at the same time. This demonstrates that both systems had the same pattern of EMG signals that would reflect each other.  
The single measure of ICC was 0.978 with a 95% confidence interval from 0.970 to 0.984 (\(F_{(200,200)}=96.254\), \(p<0.001\)).  
The single measure of ICC was 0.958 with a 95% confidence interval from 0.081 to 0.990 (\(F_{(200,200)}=277.225\), \(p<0.001\)).  
The single measure of ICC was 0.975 with a 95% confidence interval from 0.471 to 0.993 (\(F_{(200,200)}=257.374\), \(p<0.001\)).  
Figure 16: Correlation between the two EMG signals from both systems for gastrocnemius medialis.  
Figure 17: Scatter plot (correlation between the two measures for gastrocnemius medialis).