esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Method (FEM): Seismic Performance of Curtain Wall
### FE Modelling of the Full-Frame Curtain Walls  
The purpose of this section is to develop numerical models of various curtain wall elements for accurate experimental and finite element analyses. To ensure accuracy, the analyses focus on the global behavior of the curtain wall and the reaction to the external forces.  
A finite element model was constructed and implemented using the ABAQUS program to predict the seismic behavior of the stick curtain wall. The FE model was performed at the same 1:1 scale as the specimen used in the previous seismic test. The material used for the numerical analysis was the same as the seismic test specimen, and its properties are shown in Table 7. The properties of rubber were applied by inputting values calculated based on the test data, and for other materials, generally used properties were applied. Figure 15 shows the details of the solid-form FE model constructed in 3D.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density [kg/m\({}^{3}\)]** & **Modulus of Elasticity [GPa]** & **Poison’s Ratio** \\ \hline Aluminium & 2700 & 72 & 0.34 \\ PVC & 1400 & 0.1655 & 0.4 \\ Steel & 7800 & 210 & 0.3 \\ Concrete & 2300 & 26 & 0.167 \\ Glass & 2700 & 70 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Material properties used in the FE model.  
Figure 14: States of seismic curtain wall after the dynamic racking crescendo test: (**a**) no abnormalities detected in the front of the specimen; (**b**) no cracks or breakage of silicone; (**c**) no abnormalities detected in the upper fastener.  
All materials were assumed to be constrained to behave within their elastic range. Glass is a brittle material and failure occurs when the deformation state reaches its limit. The stress level of aluminum is significantly lower than the strength of the material, so ductile deformation does not occur. Therefore, it is reasonable to limit the use of the elasticity theory. Nonlinear static analysis was performed on the behavior of the curtain wall under displacement control. The curtain wall model was displaced to produce the specified movements. When displacement is applied, it should consist of three full cycles. (Herein, a cycle is defined as a full displacement in one direction back to the originating point, full displacement in the opposite direction, and return to the originating point.) The horizontal displacement was applied to the mullion connected to the rigid beam located in the middle of the main structure, and the inter-story drift was calculated.  
The boundary condition was set by fixing the vertical and horizontal displacements of the nodes at the connection between the aluminum frame and the fastener located at the top and bottom.
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 2 The OSP-MSK Modulation System Model
By multiplying Equation (6) by \(\sqrt{\frac{2}{T}}cos\big{(}\frac{\pi t}{2T}\big{)}\) and applying the integration of the result over \(2T\), the second term in Equation (6) will vanish. Thus, the first in-phase component \(S_{1I}\) can be easily extracted by using a low resolution one-bit ADC. The same procedure is applied to \(S_{2I}\), but Equation (6) is multiplied by \(\sqrt{\frac{2}{T}}cos\big{(}\frac{\pi(t-T)}{2T}\big{)}\) instead of \(\sqrt{\frac{2}{T}}cos\big{(}\frac{\pi t}{2T}\big{)}\). In the same way, the MSK quadrature-phase components \(S_{1Q}\) and \(S_{2Q}\) can be extracted by multiplying the OSP-MSK received signal \(S(t)\) by \(sin(2\pi f_{c}t)\). Then, a filtering process is applied to detect the summation of the two quadrature-phase components \(S_{Q}\) as follows:  
\[S_{Q}=\frac{1}{2}S_{1Q}sin\bigg{(}\frac{\pi t}{2T}\bigg{)}+\frac{1}{2}S_{2Q} sin\bigg{(}\frac{\pi(t-T)}{2T}\bigg{)},\text{ i.e.,} \tag{8}\]  
\[S_{1}(t)=\pm\sqrt{\frac{2E_{b}}{T}}cos\left(\frac{\pi t}{2T}\right)cos(2\pi f _{c}t)\pm\sqrt{\frac{2E_{b}}{T}}sin\left(\frac{\pi t}{2T}\right)sin(2\pi f_{c} t), \tag{2}\]0  
The quadrature-phase component of each dataset is then obtained by the same orthogonality principle used above. The extracted MSK in-phase and quadrature-phase components are then parallelly to serially converted. Then, a convolutional decoder and deinterleaver are applied in pursuit of estimating the input binary data. It should be noted here that the use of low-resolution one-bit ADCs to detect the in-phase and the quadrature-phase components at the RX side of the OSP-MSK removes most of the analog stages (e.g., automatic gain control circuit (AGC), analog filters, etc.). Moreover, the use of the coherent demodulation along with the one-bit ADCs at the RX side of the OSP-MSK eliminates the need for the complex maximum-likelihood sequence estimation (MLSE) equalizer mandated at the RX side of the MIMO-CEM system [21; 22; 23; 24; 25]. Hence, with the OSP-MSK modulation, a worthwhile reduction in both the power consumption and the hardware complexity is achieved over the MIMO-CEM system.
## 3 Research Methodology
### Feature Selection Using a Genetic Algorithm  
Here, the Genetic Algorithm [36] used is based on a feature selection method to select useful features. In the Genetic Algorithm, different combinations of features are called chromosomes, and every chromosome is be evaluated by the Fitness Function. According to the fitness value, only the highest-scored chromosome can survive to the next evolution round. The new chromosome replaces the old one in the total chromosome pool, which is called the initial population. When the evolutionary loop stops, relatively characteristic features are selected as an output of the Genetic Algorithm. Figure 2 illustrates the flowchart of the proposed feature selection process. The initial population consisted of feature chromosomes. Features in the dataset were coded into binary formations, such as 110110111... 00101101. The chromosomes were generated randomly.  
However, to capture as many attack categories as possible in both datasets, the number of the initial population is limited to 100 to 150. From previous research, the larger the initial population, the more complex the algorithm, and the more computation time required. On the other hand, if the initial population is too small, the optimal performance of the algorithm is reduced, and it can easily slip into a local optimum. Both original datasets are split into training and test datasets, using K-fold validation during the training process. The mutation rate and crossover rate are kept constant in the experiments. Based on the classification results from RF, the fitness function evaluates each chromosome at the end of  
Figure 2: Flowchart of the proposed feature selection process.  
the iteration, and the feature extraction algorithm terminates when certain conditions are met. This occurs, e.g., when the highest number of specified iterations is reached, or when the search is complete and the maximum fitness values have not changed for 10 consecutive generations. At the end of each evolutionary step, the chromosome with the highest score by the fitness function replaces the chromosome with the lowest score.  
A suitable fitness function should preserve chromosomes with high fitness values and speed up the iterative process of the genetic algorithm. Moreover, in IDS, not only the accuracy and true-positive rate, but also the false-positive rate should be included in the fitness functions. As a result, the number of features is reduced to 19, depending on the feature score. Figure 2 shows the features that will be used to build IDS frameworks based on tree architectures. The data are first prepared for processing using the proposed framework based on tree topologies. This work aims to reduce the computational complexity of building IDS frameworks based on tree architectures, and, thus, to improve their accuracy in terms of attack predictions, as the selected feature significantly affects the decision-making processes. The following section provides an overview of the evolution of tree-based IDS.
###### Abstract  
Accelerometers and gyroscopes are used to detect foot strike (FS), i.e., the moment when the foot first touches the ground. However, it is unclear whether different conditions (footwear hardness or foot strike pattern) influence the accuracy and precision of different FS detection methods when using such micro-electromechanical sensors (MEMS). This study compared the accuracy of four published MEMS-based FS detection methods with each other and the gold standard (force plate) to establish the most accurate method with regard to different foot strike patterns and footwear conditions. Twenty-three recreational runners (12 rearfoot and 11 forefoot stickers) ran on a 15-m indoor track at their individual running speed in three footwear conditions (low to high hardness). MEMS and a force plate were sampled at a rate of 3750 Hz. Individual accuracy and precision of FS detection methods were found which were dependent on running styles and footwear conditions. Most of the methods were characterized by a delay which generally increased from rearfoot to forefoot strike pattern and from high to low midsole hardness. It can be concluded that only one of the four methods can accurately determine FS in a variety of conditions.  
foot strike detection; accelerometer; gyroscope; rearfoot running; forefoot running; footwear +
Footnote †: journal: Applied Sciences
## 2 Field Test and Analysis  
### Research Zone and Construction Procedure  
The research zone is a typical section under geological conditions. The field test zone is shown in Figure 1. There were two test sections of K12 + 850 and K13 + 010 in the research zone. In this study, K12 + 850 was selected as test section.  
The thickness of soft ground in the test zone was 14.0-18.0 m. A plastic drainage band was used as a vertical drainage system. The plastic drainage band was arranged with an equilateral triangle structure with 1.2 m spacing, 4 mm thickness and 100 mm width. The treatment depth of the soft soil was 18 m, and the filling height of the monitoring section was 7.0-9.0 m.  
The drainage consolidation was selected to reinforce the soft foundation. The process of field construction is shown in Table 1 and Figure 2. The monitoring section and layout of the monitoring  
Figure 1: The field test zone.  
instruments in the test zone are shown in the Figure 3. There were two deep horizontal deformation instruments, which include the settlement plate and level gauge, and two vertical deformation instruments, which include the survey tube and survey instrument used. The CX1-1 was used to monitor the horizontal deformation of the new subgrade, and the CX1-2 was used to monitor the horizontal deformation of the old embankment affected by the new subgrade. The CJ1-1 was used to monitor the settlement of soft ground below the hydraulic fill sand, and the CJ1-2 was used to monitor the settlement of the old embankment.
# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 2 Related Work
### Traditional Machine Learning-Based Approaches
[31] presented time-series anomaly detection using the meta-feature-based OC-SVM. For the multivariate time-series dataset, singular value decomposition (SVD) or PCA techniques are used to reduce the dimension of the dataset to one-dimensional sequences. After that, six meta-features from the one-dimensional dataset are extracted. OC-SVM is then applied to six meta-features to detect anomalies. [PERSON] et al. [32] proposed a kNN-based method that removes noisy data from sensor signals. First, a difference between the data point and the median value of k-th nearest neighbors is calculated. After that, the difference is compared to an appropriate threshold value. However, knowledge about the signal is required to configure an appropriate threshold value.
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
## 3 Experimental Results and Discussion
In the proposed approach, the OD location in 126 of the 130 images was correctly identified. For the DIARETDB1 dataset, [PERSON] and [PERSON] [47] successfully performed the OD localization in all retinal images; the proposed method and the study by [PERSON] [41] followed the study of [PERSON] and [PERSON] [47] with a 98.88% success rate. In the proposed study, only one image was not detected correctly, and the remaining 88 images were successfully detected. The average absolute distance values in the proposed study were calculated as 10.07, 10.54, and 12.36 for the DRIVE, DIARETDB0, and DIARETDB1 datasets, respectively. The proposed method was observed to be superior to other studies discussed in the literature for the DIARETDB0 and DIARETDB1 datasets. For the DRIVE dataset, the proposed method was ranked second after the method proposed by [PERSON], according to the average absolute distance measure [41]. It has been observed that the proposed method can detect the OD from retinal images effectively and successfully in spite of all lesions and diseases. When the images that caused the failure of the method in OD localization were examined, it was observed that the OD regions were not significantly bright compared to other regions. The reason for this problem is that the proposed method is based on the hypothesis that the OD region is the brightest region of the retinal images.
## 3 Adaptive Transmit Diversity Strategy
### OFDM-NOMA Scheme  
The essence of PD-NOMA is to superimpose codes on different user signals according to different power allocations at the transmitting end. The receiver employs SIC detection to separate the user target signal for demodulation. Through the LED selection and combining process outlined in Algorithm 1, the number of NOMA users served by the LED cluster increases. Consequently, the number of users multiplexed under the same resources grows, leading to heightened interference among users. This increases the complexity of SIC detection at the receiving end.  
Therefore, we employ the OFDM-NOMA approach to mitigate intra-beam interference. Firstly, user grouping is determined using the D-NLUPA [25] method based on user channel gain relationships. Considering the difficulty of SIC detection at the receiving side, we pair users within each cluster in a pairwise manner. Users within the same pair share the same subcarrier and are subjected to power-domain superposition coding. Different user pairs are allocated distinct subcarriers, ensuring orthogonal transmission in the frequency domain.  
The adaptive transmit diversity strategy first merges LED beams with high inter-beam interference using an adaptive selection merge algorithm, and the corresponding users served by the LEDs are also combined into a cluster. Transmit diversity is used within the LED group to transform the inter-beam interference signals into the user signal gain, which reduces the impact of the inter-beam interference, while suppressing the effects of turbulence on optical signal attenuation. Within the beam constituted by the LED group, the OFDM-NOMA scheme is used to reduce intra-beam interference by reducing the number of multiplexed users, which reduces the difficulty of SIC detection at the receiving side.
## 3 Materials and Methods
### Feature Extraction
The differences in the levels between spectrum peaks and valleys can reveal a sound's timbre, whereas the spectrum's \"center of mass\" (also called the spectral centroid) can indicate a sound's brightness. Additionally, the spectral bandwidth indicates a sound's spectral shape by measuring the spectrum's spread around its centroid, with spectral roll-off being used to determine a sound's high-frequency content. The RMS is a feature that can be extracted from speech waves and used in SER tasks [35]. It is used to measure the energy of a signal and provide information regarding the overall loudness of the signal. The MFCC is used in SER applications to parameterize the speech signals generated by the Mel spectrogram. This scale matches the human auditory system more closely than linear frequency bands. To obtain the MFCC, the Mel spectrogram is transformed using a discrete cosine transform (DCT) [36], which captures speech signal characteristics and is resistant to timbre and instrumentation changes. This process entails determining the energy band of the speech wave, mapping the power spectrum onto the Mel scale using the corresponding deltoid windows to obtain the Mel spectrogram logarithm, and applying  
Figure 5: Zero-crossing rate (ZCR), chroma, and roll-off values for the anger sample. the DCT to this logarithm to obtain the MFCC. The formula for mapping the frequency (\(f\)) in Hz to the Mel frequency (\(m\)) is as follows:  
\[m=2595\text{log}_{10}\bigg{(}1+\frac{f}{700}\bigg{)}. \tag{2}\]  
The inverse formula for mapping the Mel frequency (\(m\)) to a frequency (\(f\)) in Hertz is  
\[f=700\bigg{(}10^{\frac{m}{295}}-1\bigg{)}. \tag{3}\]  
Emotion classification studies typically use 40 MFCCs for feature extraction; however, a more nuanced representation of speech data using more coefficients can improve the detection of emotional states. MFCCs, especially when combined with the RMS and ZCR, have performed well in the complex task of SER [28; 29].
## 3 Methodology
### Loss Function  
We used a two-stage training method to gradually recover the LB speech and the full-band speech. First, we trained LB-Net. LB-Net enhances speech in both magnitude and phase, so our loss function included the loss terms of magnitude and RI components, as shown in the following formulas:  
\[L_{Mag}^{lb}=E_{X_{m}^{lb},\hat{X}_{m}^{lb}}\bigg{[}\
orm{X_{m}^{lb}-\hat{X}_{m}^ {lb}}^{2}\bigg{]} \tag{30}\]  
\[L_{RI}^{lb}=E_{X_{l}^{lp},\hat{X}_{l}^{lp}}\bigg{[}\
orm{X_{r}^{lb}-\hat{X}_{r}^ {lb}}^{2}\bigg{]}+E_{X_{l}^{lb},\hat{X}_{i}^{lb}}\bigg{[}\
orm{X_{i}^{lb}-\hat{X}_ {i}^{lb}}^{2}\bigg{]} \tag{31}\]  
\[L_{TF}^{lb}=L_{Mag}^{lb}+(1-\mu)L_{RI}^{lb} \tag{32}\]  
where \(L_{TF}^{lb}\) consists of magnitude loss \(L_{Mag}^{lb}\) and phase-aware loss \(L_{RI}^{lb}\). \(X_{m}^{lb}\), \(X_{r}^{lb}\), and \(X_{i}^{lb}\) represent the magnitude, real component, and imaginary component of clean speech, respectively. \(X_{m}^{lb}\), \(X_{r}^{lb}\) and \(\hat{X}_{i}^{lb}\) represent the magnitude, real component, and imaginary component of the enhanced speech, respectively. \(\mu\) represents the chosen weight, and we took \(\mu=0.5\).  
In the second stage, we combined the pre-trained LB-Net network with MB-Net and HB-Net, and conducted joint training on them. The total loss can be given by the following formula:  
\[L^{full}=\alpha L_{TF}^{lb}+L_{Mag}^{mb}+L_{Mag}^{lb} \tag{33}\]  
where \(L_{Mag}^{mb}\) represents the loss function of MF-Net and \(L_{Mag}^{lb}\) represents the loss function of HF-Net in the magnitude domain, and we defined the total loss function of the second stage as \(L^{full}\). \(\alpha\) represents the chosen weight, and we took \(\alpha=0.1\).
# Flying Ad Hoc Networks: A New Domain for Network Communications
## 2 State of the Art
### Related Work  
Only a few works can be found in the related literature addressing the impact of UAVs on the propagation models. In Reference [63], a set of experiments were performed measuring the RSS in the U2G channel under the standard 802.11 a in the 5 GHz band. In addition, the best position of the antennas in terms of offering a higher level of received signal was inferred. The results showed that when the antennas were horizontal, the yaw differences could be handled, but if the antennas were placed perpendicular to the ground, the signal level varied greatly during the accelerations/decelerations of the UAV. [PERSON] et al. [12] carried out a series of experiments measuring the received power in a U2G communication link. Each communication node employed three transmit antennas in a triangular position in order to eliminate the nulls, thus preventing orthogonal polarization. In the experiments, the ground base station was fixed, and a UAV moved in various scenarios at different heights, orientations, and distances. The main drawback of this work is the lack of control over the environment parameters, such as interferences or reflections, that could alter the results and limits reproducibility. In Reference [64], the authors conducted a series of measurements of the U2G channel in a sub-urban mountainous environment at frequencies of 968 MHz and 5060 MHz. [PERSON] et al. [65] performed a set of measurements of the signal received from the U2G channel with various antenna positions, various base station heights, and different distances between nodes. They concluded that to obtain the highest throughput and thus the highest RSS, the antennas of both devices must be parallel to each other.  
In summary, none of these previous works obtained accurate results given the lack of control over the experimental scenario, limiting the reproducibility and the applicability of the obtained outcomes. Consequently, we decided to study the influence of a UAV on the radiation pattern of an embedded communication device in a fully-controlled environment. The complexity involved in modeling all the physical components of the UAV and the WiFi communication device with a high degree of detail (antennas, carbon fiber chassis, motors, metal parts, vibrations, etc.) in an electromagnetic simulator such as 4 NEC2, HFSS, CST, etc., is very high. In addition, results obtained in simulation should be later corroborated with real measurements. Consequently, we skipped this previous step and perform the measurements directly in a RF anechoic chamber under conditions of real use. That is, we carry out the experiments in a fully controlled environment so reproducibility is guaranteed.