esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Analysis of Aeroelastic Response
The model is also a three-blade horizontal axis wind turbine whose rotor diameter is 93 m and the height of tower is 78 m. The results of 6 MW simulation show that blade 1 vibrates more intensely than other blades, and the vibration of the tower is more severe in the \(y\)-direction. Therefore, Table 3 gives the maximum tip displacement of blade 1 and the distribution of ultimate loads of the tower in the \(y\)-direction under different inflow angles of the 2 MW model. The results show that there is a significant effect on the vibration of wind turbine when the inflow yaw angle changes. The blade displacement reaches a maximum at the inflow yaw angle of 30 degrees, and the vibration amplitude of the tower in the \(y\)-direction is higher than that in other cases when the yaw angle is 30 degrees and 120 degrees, which are similar to those of the 6 MW wind turbine. In order to further study the particularity of the typhoon and its influence on the structure of wind turbine, the modal decomposition of the fluctuating wind speed is carried out in the frequency domain, and the instantaneous frequency of the intrinsic mode is obtained by the Hilbert-Huang transformation (HHT). The HHT methodclearly shows the frequency of structural response variation with time [43]. In practice, the range about structural frequency which is greatly affected by the fluctuating wind is between 0.1 Hz and 1 Hz. Figure 11 shows the instantaneous frequency in the three kinds of the fluctuating winds simulated by the different power spectrums which are located among the above frequency range. The black curve is the second order instantaneous frequency and red is the third order instantaneous frequency. The second order instantaneous frequencies in Figure 11a,b fluctuate sharply between 0.1 to 0.5 Hz, while most of the second order peak frequencies in Figure 10c are concentrated between 0.2 to 0.3 Hz. Table 1 shows that the first natural frequencies of the wind turbine blades and the towers are observed in this range, so the energy of typhoons can be more easily resonant with the structure. According to the third-order instantaneous frequency, it is found that the peak of Figure 11a changes very slightly, but there are obvious abrupt changes in Figure 11b,c. These differences show the non-stationary characteristics of the typhoon. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Inflow Yaw Angle (Deg)** & **0** & **30** & **60** & **90** & **120** & **150** & **180** \\ \hline Deflection Amplitude of Blade 1 (m) & 4.16 & 5.99 & 5.31 & 5.31 & 4.95 & 4.5 & 2.61 \\ Fy Maximum of Tower (\(\times 105\)N) & 2.81 & 7.38 & 2.31 & 2.27 & 7.09 & 4.54 & 2.74 \\ Fy Minimum of Tower (\(\times 105\)N) & \(-3.48\) & \(-5.05\) & \(-5.33\) & \(-4.24\) & \(-4.61\) & \(-3.75\) & \(-2.38\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Aeroelastic responses of 2 MW wind turbine at different yaw inflow angles of the typhoon.
## 3 Materials
### Feature Extraction
#### 3.2.2 Framing  
Framing divides a continuous audio signal into smaller segments (frames). Each frame typically consists of a fixed number of audio samples or time points, and the frames are usually overlapping to capture temporal information in signals. By breaking continuous signals into frames, we can extract useful information from each segment, and analyze it separately.
# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 5 Results
### Time Cost  
As shown above, the accuracy of GK-R is much higher than GMM-R and kNN-R. Meanwhile, for the 1620 test samples collected from 12 hand movements of 27 subjects, the decisions on 52.7% of samples are made directly by the first layer GMM-R classifier, and the rest are then sent to the second layer kNN-R classifier. The percentage of decisions made by GMM-R determines the computational efficiency of the proposed classifier. It is said in [46] that a delay no larger than 300 ms will not be perceived by the user and can be used for prosthetic control. However, it is stated in [47] that delays greater than 200 ms are not acceptable, but the values were changed to 100-300 ms in the later paper [48]. A similar study in [49] showed that the maximum allowable time delays are 300-400 ms, so the user cannot notice the delay. Tests on different levels of controller delay ranging from 0 ms to 300 ms were conducted in [50] and it was found that the optimal controller delay lies between 100 ms and 175 ms. Though the conclusion on optimal controller delay varied in different studies, the less the delay, the better.  
The comparison of average time cost for recognizing one movement by kNN-R, GMM-R, and GK-R is shown in Figure 7. In Figure 7, the values that deviate from the mean of the data by more than three standard deviations are considered outliers and are removed. The width of the bin in the histogram of Figure 7 is set to 75 ms. From Figure 7a, it can be found that the average time cost for recognizing one movement by GMM-R is much less than kNN-R, which is actually around five milliseconds. For kNN-R, about 12.5% of samples need less than 150 ms; 57.8% of samples need 150-300 ms; and the rest need more than 300 milliseconds to be recognized. GMM-R is much more time efficient compared with kNN-R. From Figure 7b, it can be found that the samples recognized within 150 ms are 57.5% by GK-R; it is only 12.5% by kNN-R, meaning more than half of the samples need less than 150 ms to be recognized by the GK-R classifier. Meanwhile, the percentage of samples recognized with more than 300 ms by kNN-R is 14.4% and is reduced to 8.7% by GK-R. So, the proposed GK-R classifier is much more time efficient than kNN-R.  
Figure 6: Movement recognition by proposed algorithms. (**a**) Comparison of GMM classifier and GMM-R classifier. (**b**) Result of GK-R classifier.
# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 2 Contributions  
In this study, the authors recommend an Optical Camera Communication (OCC) system for IoT applications using MIMO technology with an LED matrix. This approach is well-suited with most commercial cameras existing on the market. The key advantages of the proposed scheme are outlined below:  
* Compatibility with Various Camera Types: the system supports most commercial cameras existing on the market by appropriately adjusting the exposure time.
* Rotation Support: The scheme ensures full 360-degree rotation support by applying a matrix transpose. By anchor positioning the four corners of the LED matrix, cameras can accurately distinguish and process rotation.
* Frame Rate Variation Handling and Data Merging Algorithm: Frame rate variation is a significant challenge in OCC systems, often leading to packet loss during data decoding at the receiver. While many people assume that a camera's labeled frame rate (e.g., 30 fps or 60 fps) remains non-changeable, fluctuations can cause synchronization issues between the transmitter and receiver. The system embeds a sequence number (SN) in each sub-packet, which indicates the sequence within the data stream. By adjusting the SN length according to the data packet size, the OCC system can be optimized.
* Missing Packet Detection: To efficiently reconstruct images from consecutive packet transmissions, the system employs an SN within each packet. By comparing SNs across successive images, the receiver can easily identify and compensate for most missing packets.  
* Multiple Link Process: By applying deep learning, we can process multiple users at the same time with high speed and over a long distance. With deep learning for object detection and data decoding, we can achieve very good performances (data rate: 15.360 kbps, distance up to 25 m, and 20 links considering a mobile environment).
## 3 Results
### GeoSlam Zeb-Revo
#### 3.3.2 Dataset Evaluation  
As a general consideration, in the common areas the Zeb-Revo and Pegasus are the most comparable systems, since both allow the ceilings of the internal test field to be surveyed, while the Stencil generally does not, as explained in Section 3.1.2. Although the Zeb-Revo resolution on higher surfaces is lower than with the Pegasus, the distances to the reference are usually smaller. In fact, the standard deviation of the distances is now respectively 4.0 cm for both path 1 and path 2, lower than that computed for the Pegasus data.  
In Figure 15a, the points of path 1 are mostly green-coloured, and some of the red and blue ones relate to the tree canopy seen earlier. In Figure 15b, the colour of the points acquired in path 2 is \"not green\" (namely with an error of more than 8 cm) mainly in the passages between different rooms. This aspect requires a more attentive analysis. Nevertheless, the magnitude of error is less than ten centimetres. Moreover, considering a vertical cross section (Figure 16) similar to those in Figures 9 and 13 but now along the orthogonal direction 2, there is no evidence of any remarkable misalignment or noised data in those areas. Some local point to point distance checks report very small values of 2-4 cm.  
Figure 14: GeoSlam Zeb-Revo datasets: (**a**) path 1; (**b**) path 2.  
Figure 15: Cont.  
To conclude Section 3.3, the results reported in Sections 3.1.2, 3.2.2 and 3.3.2 give an idea of the various potentials and limits of the systems examined in our test field. The most important quantitative values are the obtained discrepancies, reported in Table 2 in the form of the root mean square of the signed distances. They show how on average for an IMMS survey there is a difference of less than 8 cm with respect to a TLS static survey, and this difference is reduced up to 4 cm for the Zeb-Revo cloud. For the same IMMS, the obtained signed distances for cloud from closed and open paths present similar mean values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Path 1** & **RMS (m)** & **Path 2** & **RMS (m)** \\ \hline Kaarta Stencil & 0.070 & Kaarta Stencil & 0.050 \\ Leica Pegasus & 0.080 & Leica Pegasus & 0.070 \\ GeoSlam Zeb-Revo & 0.040 & GeoSlam Zeb-Revo & 0.040 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The RMS of signed distances of IMMS datasets with respect to the ground truth.  
Figure 16: GeoSlam Zeb-Revo, path 1. Cross Section 2 (reference data in white, compared dataset in colour).  
Figure 15: Signed distances of the GeoSlam Zeb-Revo dataset compared to the ground truth: (**a**) path 1; (**b**) path 2.
# Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms
## 4 Results
### Discussion  
In this section, the proposed method is compared and evaluated with the approaches presented in [3]. The same dataset was used in this research as in [3], and a comparison is made on the accuracy of the models. The accuracy parameter is for calculating the efficiency of the classification algorithms, which shows the percentage of the entire set of test records that is correctly classified by the model. The proposed hybrid optimization algorithm BGWOPSO obtained the highest accuracy with the ANN classifier in detecting fake accounts on Instagram, with an accuracy of 99.1% as compared to the accuracy of 91.76% achieved by [3]. Figure 15 shows a graphical representation of the accuracy of fake account detection in the Instagram dataset in terms of bar charts. The chart shows values for four different training-testing ratios (60:40, 70:30, 80:20, and 90:10) of four different classifiers KNN, SVM, ANN, and LR, where the X-axis is represented with four different classifiers and the \(y\)-axis is the accuracy in percentage.  
Table 4 gives a comparison with the state of the art in detecting fake accounts on Instagram datasets, as given in the literature review in Table 1.  
As can be observed from Table 4, the proposed method of hybrid Binary Grey Wolf Optimization with Particle Swarm Optimization when applied to the classifiers gives improved performance. The datasets used in the state of the art are obtained differently and a complete comparison cannot be made. However, the dataset used by [PERSON], [PERSON], and [PERSON] [3] is the same as used in this research and it can be observed that there is considerable improvement in performance by applying optimization techniques, especially BGWOPSO. Optimization methods have been applied and tested by a number of researchers on Online Social Network (OSN) data and a higher accuracy has been seen in such methods, as shown in the literature review in Table 1.  
Figure 15: Accuracy of the classifiers with the optimization methods.
## 4 Evaluation of Industrial Applicability  
To support Contribution 3, the potential industrial applicability of the generalized workflow was evaluated in an industrial case study. Here, the proposed workflow was used to recognize previously unknown AM parts in the live production of an AM service provider.
# Mechanical Properties of ASTM A572 Grades 50 and 60 Steels at High Temperatures
## 3 Results and Discussion
### Mechanical Properties
#### 3.1.4 Tensile Strength  
The tensile strength reduction factors from the literature (Table 2) and ASTM A572 Gr. 50 and 60 were compared, as shown in Figure 10. In the Eurocode and AISC standards, the tensile strength at the target temperature was divided by the yield strength at room temperature and expressed as a yield ratio. However, in this study, the tensile strength reduction factor was obtained by dividing the tensile strength at the target temperature by the tensile strength at room temperature (\(f_{u}/f_{u,25}\)). To compare the tensile strength reduction factor based on an equal normalization, the literature values (Table 2) or codes presenting yield ratios \(\left(f_{u}/f_{y}\right)\) were changed to the tensile strength ratio \(\left(f_{u}/f_{u,25}\right)\).  
Tables 6 and 8 list the tensile strength reduction factors \(\left(f_{u}/f_{u,25}\right)\) of ASTM A572 Gr. 50 and 60 steels. The tensile strength of the steel was the highest value at all strain levels. The tensile strength of Gr. 60 steel (red line) increased by -10% at 300 \({}^{\circ}\)C due to temperature strain hardening. However, the tensile strength reduction factor of Gr. 50  
Figure 10: A comparison of the reduction factors for the tensile strength with the literature values and standards.  
Figure 9: A comparison of the reduction factors for the elastic modulus with the literature values and standards.  
(black line) was observed to be 0.28 (28%), which is different from the AISC graph. The two ASTM A572 Gr. 50 and 60 test lines showed significant differences in the target temperature range from 300 to 400 \({}^{\circ}\)C, as well as a similar strength reduction above 500 \({}^{\circ}\)C. Above a target temperature of 400 \({}^{\circ}\)C, the Gr. 50 line was located below the distribution of the other data. In addition, the distribution of the data was below the intensity reduction graph for the AISC at all target temperatures. There was no significant decrease in the tensile strength from room temperature to 300 \({}^{\circ}\)C, but the decrease in the tensile strength increased significantly above 400 \({}^{\circ}\)C. Therefore, a new tensile strength reduction factor regression equation was proposed based on the target temperature of 300 \({}^{\circ}\)C.
## 3 Results and Discussion
### Particle Size  
The average diameter and size distribution range of spray-dried CMC.BTA microspheres, obtained from different SEM micrographs, are listed in Table 2, and the respective histograms are available in the supplementary information (Figure S1).  
It is possible to observe that the mean average size of the microparticles produced with different inlet temperatures was not affected by the different experimental conditions. Although different size microparticles were observed, the associated standard deviations are high and the mean average size values fall within experimental error of each other. High standard deviations are most likely a consequence of the broad size distribution observed, a common and known limitation of the spray drying technique [14]. Notwithstanding, a closer analysis of the size distributions (see Figure S1) for each of the spray flow rates tested, suggests that for 440 L/h (conditions 1, 3 and 5), sizes range from 0.5 to 14.8 \(\upmu\)m, whereas for 600 L/h (conditions 2, 4 and 6), sizes range between 0.2 and 12 \(\upmu\)m; this indicates that for a higher spray flow rate (drying air), a decrease in size occurs. Reports in the literature support this observation, since it is known that high temperatures during spray drying may cause a decrease in particle size, due to a high evaporation (volatilization) rate of the solvent from the atomized droplets [15; 34; 35].  
In order to confirm whether these values are significantly different, an ANOVA analysis was performed on the size experimental data. The statistical analysis revealed that the mean average size values are not statistically different (F < F\({}_{\mathrm{crit}}\) with \(p\)-value > 0.05).  
When considering active anticorrosive coating development, the size of the microcontainer must be lower than the thickness of the coating layer where the microcontainers will be dispersed. Since the thickness of coatings, used in different applications, can range from a few to several hundred micrometers [4], and given the small size of the CMC.BTA-MS synthesized (Table 2), they can be readily applied in organic coatings [3; 4; 36].
# Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems
## 1 Introduction
In Section 4, the simulation results are presented. Finally, the conclusions of the study are presented in Section 5. _Notations_: A boldface capital \(\mathbf{X}\) is used to denote a matrix, and a boldface lowercase \(\mathbf{x}\) represents a column vector. The entry in the \(i\)th row and \(j\)th column of \(\mathbf{X}\) is denoted by \(x_{i,j}\), whereas the \(i\)th entry of vector \(\mathbf{x}\) is denoted by \(x_{i}\). The \(j\)th column vector of \(\mathbf{X}\) is denoted by \(\mathbf{x}_{j}\). The transpose operation is denoted by \((\cdot)^{T}\), and the norm of a vector is denoted by \(\|\cdot\|\). Finally, \(\Re(\cdot)\) and \(\Im(\cdot)\) indicate the real and imaginary parts of a matrix or vector, respectively.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.9 In Vitro GAP Activity Assays Identified Rheb as a TSC2/TSC1 GAP Substrate  
Given that the TSC2 C-terminal region displays a sequence homology to RapGAP, [PERSON] et al. first purified GST-TSC2 C-terminal fusion proteins from _E. coli_ and examined its GAP activity towards Ras subfamily GTPases, including Ras, Rap, TC21, and Rheb, and Rho family GTPases, including Rac and Cdc42 [43]. However, no obvious GAP activity was observed. Instead, [PERSON] et al. expressed TSC2 with TSC1 in HEK293 cells and immunoprecipitated TSC2/TSC1 complexes to perform in vitro GAP activity assays using the same set of small GTPases as substrates. As a result, TSC2/TSC1 was observed to stimulate GTP hydrolysis of Rheb in vitro [43].
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 4 System Model  
There are \(n\) OpenFlow enabled switches that are connected to each other based on the matrix \(\mathbf{B}_{(n\times n)}\), where \(\mathbf{B}_{ij}\) determines the bandwidth of the link from switch \(i\) to switch \(j\). The number of the flows in the network is \(p\). The routing matrix \(\mathbf{A}_{(n\times n\times p)}\) specifies the path selected for each flow, e.g., if \(\mathbf{A}_{ij}^{f}\in\{0,1\}\) is equal to \(1\), then the flow \(f\) crosses the link \(i\to j\). Suppose that there are multiple classes of traffic in the network. The flow requirement matrix \(\mathbf{C}_{(p\times 2)}\) indicates flows' quarantined requirements based on the corresponding class. The \(i\)-th row of flow requirement matrix contains \(Delay,\ Rate\), where \(Delay\) clarifies the maximum tolerable propagation delay of flow \(i\) and \(Rate\) defines the guaranteed bandwidth for the flows. Current flow matrix is represented by \(FM_{p}\), where \(FM_{f}\) (or \(FM[f]\)) is recent bandwidth consumed by flow \(f\). Similarly, the parameter \(PF_{p}\) is the predicted flow matrix that states the forecasted bandwidth for each flow. Table 1 reports the main notations of the paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \multicolumn{2}{c}{**Mathematical Parameters**} \\ \hline \(n\) & Number of switches \\ \(p\) & Number of flows \\ \(FM_{1\times p}\) & Current flow matrix \\ \(PF_{1\times p}\) & Predicted flow matrix \\ \(MS_{1\times p}\) & Maximum size of flow \\ \(\mathbf{B}_{n\times n}\) & Link bandwidth matrix \\ \(\mathbf{C}_{p\times 2}\) & Flow requirement matrix \\ \(\mathbf{D}_{n\times n}\) & Link delay matrix \\ \(s_{1\times p}\) & Source switch of flows \\ \(d_{1\times p}\) & Destination switch of flows \\ \(\mathbf{B}_{n\times n}\) & Link bandwidth matrix \\ \(\mu\) & Maximum link utilization \\ \hline \multicolumn{2}{c}{**Decision Variable**} \\ \hline \(\mathbf{A}_{n\times n\times n\times n}\) & Routing matrix \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Notation.  
Figure 2: Outline of the proposed schemes. The proposed scheme can work with any of existing routing algorithm. In this architecture, a typical routing algorithm routes one flow in each step while the proposed rerouting algorithm reroutes several flows to reduce the packet loss and provides a better QoS (quality of service) to the users.
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 3 Experiment
### Performance Evaluation for AGVs
From Figure 7, we can observe that the proposed approach effectively captures comprehensive depth information of the scene with a relatively high degree of accuracy and accurately portrays specific targets' spatial characteristics through color differentiation. The proposed method benefits from the optimized structure of the depth estimation network and the incorporation of ECANet, a channel attention mechanism. By comparing the regions in the blue bounding boxes in Figure 7, we can find that our method achieves more accurate retention and extraction of detailed features, leading to clearer and more precise contours and boundaries in the depth map. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & _Abs\_Rel_ & _Sq\_Rel_ & _RMSE_ & _RMSE\_Log_ & _Thr Acc (thr = 1.25)_ & _Thr Acc (thr = 1.25\({}^{2}\))_ & _Thr Acc (thr = 1.25\({}^{3}\))_ \\ \hline _Proposed_ & **0.146** & **0.883** & **4.857** & **0.197** & **0.792** & **0.954** & **0.988** \\ \hline _HR-Depth_ & 0.157 & 1.039 & 5.698 & 0.217 & 0.751 & 0.941 & 0.985 \\ \hline _Monodepth2_ & 0.163 & 1.108 & 5.699 & 0.222 & 0.750 & 0.931 & 0.982 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of quantitative comparison based on the _KITTI_ dataset (The best result in each column is shown in bold). Figure 6: Comparison of the four evaluation metrics in ablation experiments on the _KITTI_ dataset.