esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this article, a miniature MIMO antenna array for 5G wideband metal-rim mobile phone applications is presented. The individual antenna element consists of a Wang-shaped radiator and three split-ring resonators (SRR) on the metal frame. The dimensions of an individual antenna element are \(6.8\times 7\times 1\) mm\({}^{3}\) (47.6 mm\({}^{3}\)). The measured total efficiency ranges from 40% to 80%, the isolation is better than 12 dB, the calculated ECC is less than 0.12, and the calculated CC ranges from 35 to 38 bps/Hz. The innovation points of the proposed handset antenna are wideband operation (which is suitable for 5G sub-6 GHz applications), high efficiency, and high communication capacity under minimized spacing. Other decoupling methods, such as the use of slots and parasitic elements, can be explored to further optimize isolation in future iterations of the antenna design. Therefore, the proposed MIMO antenna array is a good alternative for 5G sub-6 GHz miniature mobile handset applications.  
Conceptualization, J.H.; methodology, L.S.; investigation, S.X.; writing\(-\)original draft preparation, J.H.; writing\(-\)review and editing, X.S. and G.L.; supervision and funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \(\begin{array}{c}-10/-6\text{ dB}\\ \text{Bandwidth (GHz)}\end{array}\) & \multirow{2}{*}{**ECC**} & \multirow{2}{*}{**Efficiency (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **Element** \\ **Volume (mm\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{**CC**} \\ \cline{1-1} \cline{5-5} \cline{7-7}  
**Funding:** This work was supported in part by the Science and Technology Department of Zhejiang Province under Grant No. LGG19F010009, Wenzhou Municipal Science and Technology Program under Grant No. C20170005 and No. 2018 ZC019, and the National Natural Science Foundation of China under Grant No. 61671330.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
Therefore, this study aimed to classify a difference in gait and physical fitness characteristics between senior citizens with and without diabetes through automated machine learning (AutoML) by using the simple sensor and propose a machine-learning-based personal home training system for training abnormal gait motions by oneself. For this study, a dataset was constructed by 200 senior citizens over 65 years old who performed to walk a flat and straight pathway of 15 m under three different conditions of walking speed (slow (=20% slower than preferred walking speed)), preferred, and fast (=20% faster than preferred walking speed)) by using an inertial measurement unit (IMU) and physical fitness tests. Then, for training abnormal gait motions, the proposed home training system enabled us to correct the exercise posture and speed in real-time through machine-learning-based similarity evaluation between training experts and novices by using a single RGB camera. The contribution of this study was to make clear the association between diabetic walking abnormalities and measured feature vectors. Then, these results could be applied for early detection and therapeutic intercessions that rehabilitate the walking function in diabetic senior citizens by grouping them not only by clinical features but also based on their motor control strategies. Furthermore, the proposed home training system helps senior citizens with diabetes continue to exercise activities with the correct posture and speed. The structure of this study is: we describe how to construct the dataset for this study in Section 2, then explain how to classify through AutoML in Section 3, and show all of the results, including which gait characteristics are essential to classify, in Section 4. Finally, to train the detected abnormality, we introduce the developed machine-learning-based personal home training system in Section 5 and show all of the results to verify the usefulness in Section 6. Finally, we discuss the classification for healthcare through AutoML and the limitations of the developed home training system in Section 7, before our conclusions in Section 8.
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 2 Methodology
### Network Design  
In the addition of the existing components in the network, for network scaling, we add the EDFA, Mach-Zehnder modulator, AWG and power splitter (see Figure 4). The key in this study was to slice the broad-based signal into small spectrum before duplicate to many signals. The duplication will start at AWG, where it will then be broken down into multiple separator nodes. It is important to place components that can do the slicing and duplication early on in the network as it will increase the breakdown ratio and increase the final users. And as we carried out the simulation using Optisystem, we could later figure out the bit error rate (BER) and the Q factor.  
The main objective for this study was to increase the scalability of the FTTH networks without the need to install a new optical cable. So, aimed to maximize the separation ratio with the help of some components, to increase the number of users that can be reached. The simulation results for the network implemented using the spectrum slicing and duplication technique showed a good signal at the end and also a maximum separation ratio for the AWG, power splitter, WDM Demux and power splitter, of 6 \(\times\) 32 \(\times\) 32 \(\times\) 32, which reached approximately 196,608 users. Because of this addition of components (implementation of spectrum slicing and duplication technique), there is no need for installation of new optical cable; hence, we can lower the cost in increasing the scalability of networks. This creates an economical solution to solving high-density scenarios.  
Instead of scalability, many research studies focus on the extension of the fiber length to support users located at distances far from the central offices [17; 18; 19] and effective bandwidth to support more application to the users [20; 21].  
The flowchart below (see Figure 5) shows how the simulation or the network works after the spectrum slicing and duplication technique is applied. This is to analyze the network scaling. In this study, the simulation for the upscaling was implemented using Optisystem software. To achieve the objective of the study, we changed the network by adding the AWG, power splitter, WDM Demux and power splitter. Using the new arrangement of the network, we collected the raw data of the signal going through the network, such as the maximum number of users, Q factor and bit error rate (BER).
## 5 Performance Analysis
### Experiment 1  
The proposed channel estimator's performance was evaluated in the first experiment. For each OFDM symbol, there are \(N_{p}=8\) pilots available for channel estimation. The estimation performance was assessed in both the time and frequency domains using the proposed extended 2D-BEM model. The results shown in Figure 3 demonstrate that in vehicular scenarios with a high Doppler dispersion frequency (\(f_{D}=1\) kHz), the conventional  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter (Units)** & **Experiments 1, 2, and 3** & **Experiments 3 and 4** \\ \hline \(\{N,N_{d},CP,N_{p}\}\) (Samples) & \(\{64,48,16,8\}\) & \(\{64,48,16,8\}\) \\ Bandwidth (MHz) & 10 & 10 \\ Tx antennas \(N_{t}\) & 2 & 4 \\ Rx antennas \(N_{r}\) & 2 & 4 \\ Constellation size \(M\) & \(\{4,4,16\}\) & \(\{4,64\}\) \\ \(N_{C}\) (Samples) & \(\{2,2,4\}\) & \(\{2,2\}\) \\ \(N_{B}\) (Samples) & \(\{24,24,12\}\) & \(\{24,24\}\) \\ PDP \(\mathrm{E}\{h^{2}[n,l]\}\) & \(\lambda e^{-0.4l}\) & \(\lambda e^{-0.4l}\) \\ Number of multipaths & 6 & 8 \\ \(f_{D}\) (kHz) & 1 & 1.2 \\ \(\tau_{rms}\) (\(\mu\)s) & 0.4 & 0.4 \\ Channel estimation & 2D-BEM & - \\ \(\{M_{D},M_{\tau}\}\) & \(\{2,5\}\) & - \\ Spectral efficiency (bit/s/Hz) & 1.2 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: System and channel parameters.  
Figure 2: Block diagram of proposed MIMO-OFDM-IM receiver.  
OFDM-MIMO system fails to estimate the channel parameters accurately. The mean square error (MSE) exhibits a floor error at low SNR levels. However, in the case of the proposed system, specifically for \(N_{t}=2,N_{r}=2\), the MSE error behaves uniformly without any floor error in the reported SNR region. Additionally, for \(N_{t}=4,N_{r}=4\), the MSE error exhibits a floor error at high SNR levels. This can be explained by the fact that \(N_{p}=8\) remains constant, and a larger number of parameters need to be estimated with the same amount of information.
## 4 Experimental Evaluation
### Identification Evaluation  
Performance in the identification mode of our proposed network is evaluated according to the accuracy metric shown in Equation (5).  
\[accuracy=\frac{TP}{N}, \tag{5}\]  
where _TP_ are the _true positive_ cases correctly classified, i.e., the number of instances where the prediction and the correct label are equal, and \(N\) is the number of total instances [60]. Our identification task is done in closed-set modality so only true positive cases are counted to calculate the accuracy.
# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
## 4 Dataset
### Dataset Creation  
To generate the custom dataset SFPI, we took all floor plan layouts of SESYD [3] as the base and implanted the furniture objects to create different floor plan images. To overcome the shortcomings of SESYD [3], we take care of furniture object augmentations. The first augmentation we apply is rotation; we assign rotation randomly on furniture class objects. In this way, some objects will undergo rotation, and some will be the same as the original model. We use random angle choices between [0, 30, 45, 60, 75, 90, 120, 150, 180, 210, 250, 270, 300, 330] degrees and rotate the image from its center. Figure 7 depicts the example of this augmentation; Sofa and Tub furniture class objects have different orientations based on randomly selected angels. Another augmentation choice we employ is scaling; this is an important step to ensure that the model generalizes well to identify natural furniture objects. We enforce scaling randomly to have both scaled and non-scaled furniture class objects in the same image. For scaling, we use a random resize factor between [40, 60, 75, 85, 100, 130, 145, 160, 185, 200] and scale the objects using this. We use the inter-are option for interpolation while resizing the objects. While resizing, we make sure to keep the original aspect ratio of the objects intact. In Figure 7, we have different scaling for bed and sofa objects of respective furniture classes.
## 3 Results and Discussion
### Removal of Phenolic Compounds  
The concentration of total phenolic compounds in the initial AL effluent and in AL effluent after fungal treatment was analysed. The phenolic compounds cause severe pollution of surface and ground water, soils, and vegetation. Its presence has a negative effect on microorganisms due to its high antibacterial activity [35; 36].  
As is shown in Figure 4, the best efficiencies of phenolic compound removal were reached with the inoculation of _P. chrysosporium_ in E1, where around 30% of phenolic compounds were degraded after 10 days of treatment reaching values of 91 mg/L. This percentage of removal was slightly lower when glucose was added (E2), obtaining final removals of 25%. For the non-inoculated samples with fungus (C1 and C2), the amount of phenolic compounds removed was lower, with removal percentage of 12% in both cases. Results proved that the fungus inoculation increases the degradation of phenolic compounds with removal percentages almost three times greater than in the non-inoculated samples. However, higher efficiencies have been reported by other authors when the AL effluent was previously sterilised.  
[PERSON] et al. [23], who treated a diluted and sterilised olive pomace effluent by submerged fermentation with _Cerena unicolor_, reported a removal of phenolic content of around 80%. Additionally, this fungus showed a good capacity to release lacases, which are involved in the degradation of phenolic compounds. The low removals achieved in this study it could be because a non-sterilised AL effluent. Moreover, low lacase activity has been reported for _P. chrysosporium_[24]. [PERSON] et al. [35] reported a 92% total phenol degradation using _P. chrysosporium_ to treat a sterilised OMW supplied with a nitrogen source. Additionally, great phenolic removals were obtained when AL was dried, and the concentrate was treated. For example, [PERSON] et al. [37] reported removals around 85% using the fungus _Phlebia_ sp. immobilized in polyurethane sponge, while 43% was achieved when the effluent was treated by free mycelia.  
Figure 3: Changes in reducing sugars concentration during biological treatment. The dashed lines shown the non-inoculated tests C1 (\(\blacksquare\)) and C2 (\(\blacksquare\)), used as controls. The solid lines shown the inoculated tests E1 (\(\bullet\)) and E2 (\(\ast\)). The standard deviation (SD) of the experimental data were in all cases less than 11% of mean value.
## 4 Numerical Experiments and Results
### Example and Parameter Setting
#### 4.2.4 Comparison with Other Algorithms
Sensitivity Analysis of Different Traffic Congestion Scenarios  
The path planning under the no congestion and very congested scenario tends to be consistent. The total cost, vehicle management and use cost, transportation cost, mixed carbon emission cost, total fuel consumption and total carbon emission in the very congested scenario are 3519.505 CNY, 1257.196 CNY, 2227.959 CNY, 34.35 CNY, 282.0201 L and 654.2866 kg higher than those in the no congestion scenario, respectively. These results show that the cost, energy consumption and carbon emissions in the case of the very congested scenario increase compared with those in the case of the no congestion scenario. Furthermore, when the congestion degree coefficient changes consistently, the change rate of the mixed carbon emission cost is the largest. This result indicates that traffic congestion has the most serious negative impact on energy conservation and emission reduction. In addition, focus on the planning path in the output results while taking customer 20 as an example. In the no congestion scenario, \(20\to 35\) is the frequently selected section under multiple runs. However, \(20\to 29\) becomes the frequently selected section in the normal congestion scenario. The congestion degree coefficient of \(20\to 35\) is 2.3 and \(20\to 29\) is 1.67. This outcome shows that the two-stage VNS-TA algorithm has the ability to avoid road congestion (Figure 6).
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 2 Methodology  
The CAD system melanoma classification method includes a preprocessing stage, feature selection stage, and classification stage. The preprocessing improved the quality of the dermoscopic image, and the skin lesions were segmented and cropped. The extracted texture, color, and shape features (TCS feature set) were selected, and internal ranking contributions were carefully analyzed and selected (RFE-TCS feature set). Finally, SVM (\"polynomial(poly)\", \"radial basis kernel functions (rbf)\" and \"linear\", \"sigmoid\"), logistic regression (LR), and Gaussian naive Bayes (GaussianNB) were used to test and train the selected optimization group. The detailed steps are shown in Figure 1.  
Figure 1: Block diagram of the proposed melanoma classification method.
## 3 System Design
### Telemetry Decoding
#### 3.8.1 Sync Frame Detection  
The Galileo telemetry decoder initiates its process by identifying the I/NAV synchronization patterns within the received telemetry frames. To address the potential presence of bit errors, the receiver maintains a list of sample stamps corresponding to the most recently detected synchronization patterns. These sample stamps represent the receiver sample counter associated with those patterns. For each identified synchronization pattern, the receiver verifies if the timing relative to any of the most recently detected patterns is a multiple of the Galileo E1 I/NAV synchronization pattern period. If this condition is met, then the telemetry decoder assumes sync frame detection and proceeds to the second step, which involves sync frame confirmation.
## 3 Methodology
### The Dataset and Evaluation  
In our analysis, we have used RAVDESS [20]. This dataset includes 7356 recordings with acted-emotional content. The archives are distributed equally into three types of content (full AV, video-only, and audio-only) and two vocal channels (speech and song).  
Except for the neutral emotion, which includes only regular intensity, the rest of the expressions are produced at two levels of emotional arousal: regular and strong. Each file contains a single actor representing one of the eight following emotions: calm, neutral, happy, sad, angry, fearful, surprised, and disgusted.  
We only used the full AV material and the speech channel for our experiments because we were interested in audio-visual emotion recognition on speech rather than songs. This selection limited the number of files to 1440 videos with a maximum and minimum duration of 5.31 and 2.99 s, respectively. The corpus has 24 actors distributed in a gender-balanced way, who speak lexically-matched statements in a neutral North American accent. This setup is suitable to study the para-linguistics associated with emotions, isolating the lexical and reducing the bias in emotional expressions that culture may induce. Among its advantages, it also has a proportional number of files per emotion, which avoids problems derived from training algorithms with non-balanced data. Additionally, RAVDESS is a reference dataset in the research community, employed in several works [64; 65; 33].  
Despite its simplifications, this dataset poses significant hurdles for emotion identification, even for humans. The human accuracy rate achieved utilizing only speech stimuli was of 67%, whereas when using visual information, this rate just increased until the 75%.  
To evaluate and compare our results, we used a subject-wise 5-fold cross-validation strategy. The folds were randomly and stratified divided per classes and users, i.e., each fold had a similar number of samples per class randomly selected, but we always kept each actor in either the train or validation sets, never in both.  
Figure 1: Block diagram of the implemented systems. The figure represents the analyzed models from the existent family of sequential models, static models, and transformers.  
The distribution per actor for the validation folds was as follows:  
* Fold 0: (2, 5, 14, 15, 16);
* Fold 1: (3, 6, 7, 13, 18);
* Fold 2: (10, 11, 12, 19, 20);
* Fold 3: (8, 17, 21, 23, 24);
* Fold 4: (1, 4, 9, 22).  
We proposed this setup following the work of [PERSON] et al. [35], who applied a similar subject-wise cross-validation methodology using the eight classes of the dataset. This evaluation procedure allowed us to compare our contribution to this previous work and with our prior solutions in [13].  
Regarding the metrics, we compared our implementations with the average accuracy achieved by the cross-validation strategy at the video level. We also included a confidence interval to compare scenarios and evaluate the significance of our methods. Additionally, we calculated precision and recall per emotion for the best model.
## 2 Bearing Diagnostics
### Simulation of Fault Signal  
With this understanding, more complex signal closer to the reality is considered. Toward this end, a virtual signal is generated using the MATLAB code given in Appendix A. A fault signal is created as shown in Figure 3a by an exponential function, \(x_{1}(t)=A\cdot exp(-at^{\prime})\) to represent the transient impact signal with the frequency \(f_{f}=10\) Hz, where \(t^{\prime}=mod\big{(}t,1/f_{f}\big{)}\) and A and \(\alpha\) represent the magnitude of impulse signal and the damping parameter of the bearing, respectively. They are given by 5 and 75.4 in this example. The mod stands for the modulus after division, which is to make repeated signal over time [19]. The FFT of this signal becomes Figure 3d, where the fundamental frequency is 10 Hz with amplitude 1.0 with the subsequent ones being its harmonics. This is modulated by a carrier signal given by the sinusoidal function \(x_{2}(t)=sin(2\pi f_{f}t)\) with frequency \(f_{r}=600\) Hz which is much higher than \(f_{f}\), as shown in Figure 3b-c. This corresponds to the resonance of the bearing. Then, the modulated signal becomes \(x_{3}(t)=x_{1}(t)*x_{2}(t)\), and is shown in Figure 3c in which the carrier contains the fault in its amplitude [20]. Its FFT becomes Figure 3f, where the sidebands are present around 600 Hz.  
Added to this, the signal may include a certain discrete signals from other components such as gears and shafts as well as the background noise. In this example, they are represented by another low-frequency sinusoidal function \(x_{4}(t)=sin(2\pi f_{d}t)\) with amplitude 2 and frequency  
Figure 3: Modulation of simulation fault signal: (**a**) fault signal, (**b**) resonance signal, (**c**) AM signal, in the time domain; (**d**) fault signal, (**e**) resonance signal, (**f**) AM signal, in the frequency domain.  
\(f_{d}~{}=~{}13\) Hz and Gaussian white noise \(x_{5}(t)~{}\sim N\big{(}0,\sigma^{2}\big{)}\) with variance \(\sigma^{2}~{}=~{}0.5^{2}\), which are given by Figure 4a,b, and their FFTs (d) and (e), respectively. Putting all these together, the resulting raw signal \(x(t)~{}=~{}x_{3}(t)+x_{4}(t)+x_{5}(t)\) becomes Figure 4c with its FFT being (f). Note that the governing sinusoidal wave shown in Figure 4c is the discrete signal \(x_{4}\). The amplitudes at the high-frequency region in Figure 4f represent the carrier at 600 Hz (resonance) and surrounding sidebands (faults), which is a more complex version of the AM mentioned in Figure 2. The frequency amplitudes of the discrete and AM signal are 2 and 0.6, respectively, which means that the fault is buried by the carrier and another discrete signals. This agrees with the finding that the signals from the gears and shafts often show higher amplitudes than those of faulty bearing; typically, the amplitudes of the shaft rotation, gear meshing and the bearing fault signals are on the order of 0.1, 10 and 0.001 g [21; 22].