esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Time Frequency Transforms
In addition, the WVD also presents six cross-terms, which occur between each pair of Gaussian signal components distributed in different time or frequency positions, and among them, two cross-terms overlap in the diagonal intersection point of the rectangle connected by the four vertices (_i.e._, four Gaussian components in the TF plane). These extra cross-terms have large oscillating amplitudes due to the interaction of the different signal components. The magnitudes of the oscillatory cross-terms can be twice as large as the auto-terms; note that in the diagonal intersection position, the intensity of the corresponding cross-term can be four-times as large as the auto-terms, since there exist two cross-terms that overlap in the diagonal intersection point. In Figure 4b, the serious cross-terms are apparently present in the regions of the TF plane where we expect no energy at all, which makes a proper interpretation impossible. The presence of the cross-terms in the TF plane is the main drawback of the WVD approach. \[STFT(t,\omega)=\frac{1}{2\uppi}e^{-j\omega t}\int_{-\infty}^{+\infty}Y_{a}( \omega^{\prime})H(\omega^{\prime}-\omega)e^{j\omega^{\prime}t}\text{d}\omega^ {\prime} \tag{13}\]5 WVD of four Gaussian components’ signals. (**a**) WVD; (**b**) Contour of WVD.
## 2 Objective Evaluation Test  
For the objective evaluation test, the following assumptions about stereo-to-multichannel upmix algorithms are made: stereo-to-multichannel upmix algorithms should enhance and extend the listening experience without adding artificial effects or contents and provide virtual sound sources true to original. The virtual sound sources in an original stereo configuration are placed between the two (front) loudspeakers. So, it is assumed that upmix algorithms are designed to have no virtual sound sources in the rear, only between the front loudspeakers. Therefore, the remaining amount of direct signal in the surround channels should be as low as possible. Furthermore, stereo-to-multichannel upmix algorithms should provide the listener with front channels that are louder than the surround channels under the condition that there is always an existing virtual sound source in the used stereo input signal. In addition, it is assumed that the effects of the correlation of the surround channels are perceived subjective, and that the surround channels should have a certain correlation to prevent uncomfortable perception. Finally, stereo-to-multichannel algorithms should create a high subjective perceived spatial quality with all loudspeakers in order to enhance the listening experience.  
For the objective evaluation of stereo-to-multichannel upmix algorithms, the following tests were defined: 1. panning test, 2. direct signal test, 3. volume test, 4. phase test, 5. perception test. In every single test a special test signal is used as input signal for the tested upmix. The generated output signals are then analyzed and evaluated according to defined criteria (see Figure 1). Note that the evaluation test will measure how well the assumptions were met according to defined criteria.  
The overall evaluation score \(score_{upmix}\) of a tested upmix results from the weighted single-test evaluation scores \(score_{i}\), and is given by  
\[score_{upmix}=\frac{\sum_{i}g_{i}\text{-}score_{i}}{\sum_{i}g_{i}} \tag{1}\]  
The higher a score, the better the test result. Zero is the worst, one the best evaluation score. Appropriate results should be visualized here based on two upmix algorithms available on the market with two modes for music (a) and movies (b) in each case. Hereinafter, they are denoted as upmix 1(a), 1(b), 2(a) and 2(b).
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### In-Depth Analysis
#### 5.3.2 Effect of Lighting Conditions  
An important factor that leads to attenuation of the underlying blood volume pulse in the extracted rPPG signal is the illumination/lighting conditions in the video. We test our method under various lighting conditions on datasets containing labelled illumination settings: COHFACE, VIPL-HR, ECG-Fitness, and MoLi-PPG; and the results are presented in Figure 7. We see that bright frontal lighting counter-intuitively leads to a degradation in performance in VIPL-HR. This is likely caused by pixel saturation, where the full colour depth of the camera cannot be exploited. Constantly flickering illumination from a computer monitor in MoLi-PPG also degrades the performance severely since such fluctuations can interfere with the pulsating component in the skin pixels. The colour temperature of the light also seems to have an influence as seen in ECG-Fitness, potentially due to dissimilar light wavelength absorption characteristics of the skin. In COHFACE, as can be expected, better performance is obtained when the room is evenly lit in comparison with uneven natural lighting. Finally, the method has good performance in the case of dark/dim lighting in MoLi-PPG and VIPL-HR.
## 5 Machine Learning Adversaries against IDS
### White-Box Attacks against IDS
#### 5.1.4 Monte Carlo (MC)  
Researchers in [11] simulated a white-box assault named a Monte Carlo (MC) simulation for the random generation of adversarial samples and compared their samples to several machine-learning models to clarify performance across a wide range of platforms and detect the vulnerability in NIDS, which assists the organizations in protecting their networks. Moreover, this research used three adversarial attack methods, which are: particle swarm optimization (PSO), genetic algorithm (GA), and generative adversarial network (GAN). In addition, the researchers used the NSL-KDD and UNSW-NB15 datasets for evaluation. Then, they confirmed that these two techniques could deceive 11 ML models. Based on their findings, the MLP model has the best accuracy under adversarial attacks with an 83.27% classification rate, followed by the BAG at 80.20% and the LDA at 79.68% with the NSL-KDD dataset. In particular, attackers with knowledge of a target network NIDS may use the most efficient perturbation process to attack that network.
## 3 Fast Handover Algorithm
### Moving Direction of HST  
After the completion of the construction of wireless communications for HSRs, the SCells information attached to UE on the train and the NCells information have strong regularity. The sequences of the handover chain of two UEs in different directions on the same line are opposite. The chain of the handover pointing to Beijing's direction is defined as positive, and the chain of the handover deviating from Beijing's direction is reverse. In the handover chain, if gNodeB\({}_{i}\) is SCell, gNodeB\({}_{(i-1)}\) and gNodeB\({}_{(i+1)}\) are NCells. At time \(t_{1}\), the reference signal receiving powers of gNodeB\({}_{(i-1)}\) and gNodeB\({}_{(i+1)}\) are SS-RSRP\({}_{(i-1)}\) and SS-RSRP\({}_{(i+1)}\), respectively. At time \(t_{2}\), the reference signal receiving powers are SS-RSRP\({}^{\prime}_{(i-1)}\) and SS-RSRP\({}^{\prime}_{(i+1)}\), respectively. When Equations (7) and (8) are both satisfied, the train is moving forward and the chain of the handover in the forward is selected. When Equations (7) and (8) cannot be satisfied at the same time, the train moves in reverse and the chain of the handover in reverse is selected. When Equations (7) and (8) satisfy either one, the measurement and calculation are re-performed.  
\[\text{SS}-\text{RSRP}^{\prime}_{(i-1)}-\text{SS}-\text{RSRP}_{(i-1)}<0 \tag{7}\]  
\[\text{SS}-\text{RSRP}^{\prime}_{(i+1)}-\text{SS}-\text{RSRP}_{(i+1)}>0 \tag{8}\]
## 5 Simulation Method Uncertainties
### Copper Model Comparison  
It is important to validate how well our simulation results compare with real measurement values in a highly controlled setting. Ideally, this would be performed on the actual aircraft, at the frequencies of interest, and for the full bistatic geometry of interest. Unfortunately, however, it was not possible for us to perform such measurements. Instead, we performed monostatic anechoic chamber measurements of a 1:8 scale copper model  
Figure 19: The variation in the simulated RCS with frequency at the 13 error points.  
of the PC-12 over the range of 8-16 GHz in 10 MHz increments, and with a 0.2\({}^{\circ}\) angular resolution. We performed equivalent SBR+ simulations of an all-copper version of the CAD model shown in Figure 9a from 1-2 GHz (equivalent to 8-16 GHz on the scale model). An example of direct comparison between the simulated and measured RCS values at 2 GHz are shown in Figure 20, where generally good agreement can be seen.  
These comparisons do not guarantee that lower frequency bistatic RCS measurements of the real aircraft will compare well with the simulations, but they do point toward the simulations being a reasonably accurate representation of reality.
# Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission
###### Abstract  
Herein, we focus on energy efficiency optimization for massive multiple-input multiple-output (MIMO) downlink secure multicast transmission exploiting statistical channel state information (CSI). Privacy engineering in the field of communication is a hot issue under study. The common signal transmitted by the base station is multicast transmitted to multiple legitimate user terminals in our system, but an eavesdropper might eavesdrop this signal. To achieve the energy efficiency utility-privacy trade-off of multicast transmission, we set up the problem of maximizing the energy efficiency which is defined as the ratio of the secure transmit rate to the power consumption. To simplify the formulated nonconvex problem, we use a lower bound of the secure multicast rate as the molecule of the design objective. We then obtain the eigenvector of the optimal transmit covariance matrix into a closed-form, simplifying the matrix-valued multicast transmission strategy problem into a power allocation problem in the beam domain. By utilizing the Minorize-Maximize method, an iterative algorithm is proposed to decompose the secure energy efficiency optimization problem into a sequence of iterative fractional programming subproblems. By using Dinkelbach's transform, each subproblem becomes an iterative problem with the concave objective function, and it can be solved by classical convex optimization. We guarantee the convergence of the two-level iterative algorithm that we propose. Besides, we reduce the computational complexity of the algorithm by substituting the design objective with its deterministic equivalent. The numerical results show that the approach we propose performs well compared with the conventional methods.  
energy efficiency optimization; massive MIMO; privacy engineering; utility-privacy trade-off; statistical CSI; beam domain power allocation +
Footnote †: journal:  
_Article_
## 3 Results and Discussion
### Water Absorption  
The water absorption test was completed on all mixes and the results are shown in Figure 7. The water absorption of 4.19% obtained for control sample (CS) however a higher water absorption rate was obtained for the mixes containing EGA. The water absorption of E50 and E100 mixes were 7.47% and 14.74% respectively, which shows a 78% and 252% increase in the permeability of the matrix, compared to the control sample. The increase in water absorption is due to the high porosity of EGA in comparison to NA. The results revealed that the water penetration increased by increasing the EGA  
Figure 6: Flow table test.  
Figure 7: Density and water absorption of mix specimens.  
content. The water absorption rate obtained for the E100 (values of approximately 14%) was higher than the acceptable range (\(<\)10%) [43; 44]. The addition of nTiO\({}_{2}\) reduced the water absorption value by 28%, 17%, and 2% for samples containing 0%, 50%, and 100% EGA respectively. The decrease in water absorption upon the inclusion of nTiO\({}_{2}\) coincides with previous studies [45] and aligns with the density results. The reduction in water absorption was attributed to the filling effect of nTiO\({}_{2}\) and reducing the porosity of the cement matrix. It is worthy to note that the sonication process resulted in tiny bubbles of air uniformly distributed in the mortar. These small bubbles are like entraining air that improves the workability of the mixes. Indeed, nTiO\({}_{2}\) acted as nanofillers in mortar and improved the resistance to water penetration of the cement composite [46].
## 6 Conclusions  
Through the tests above, the single DAC circuit test results represent the common signal generation methods which is usually just one DAC to generate signals. From the test results in the table, we can know that the signal quality of the single DAC circuit is better than that of the TIDAC circuit when the output signal frequency is low. However, with the increase in signal frequency, the output signal quality of the single DAC circuit becomes worse and worse. However, it can be seen from Table 1 that the output signal quality of the TIDAC circuit can be maintained at 95 dB up to 200 Khz, especially 103.49 dB (5.49--(98)) at 10 K, as shown in Figure 14.  
So, the TIDAC-based signal generation method can generate high-resolution signals required for high-precision ADC chip testing. The TIDAC circuit designed by this method with a simple filter which could reduce the harmonic components of the signal as in Figure 13 can reach a SNR of 104.50 dB. According to the test results, the TIDAC-based signal generation method can be used in high-resolution ADC testing.  
Conceptualization, L.W. and W.Z.; hardware, W.C. and L.W.; software, R.H.; validation, W.C. and R.H.; formal analysis, W.C. and L.W.; resources, K.C.; data curation, R.H. and W.C. and L.W.; writing--original draft preparation, W.C. and K.C.; writing--review and editing, L.W. and W.Z.; visualization, W.C.; supervision, K.C.; project administration, W.Z.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This work is partly supported by the Fundamental Research Funds for the Central Universities under Grants ZYGX2019J063 and ZYGX2020 ZB001 and the second batch of industry-university cooperation collaborative education projects of the Ministry of Education in 2019 (201902059007).  
Not applicable.  
\begin{table}
\begin{tabular}{c} **SNR (dB)** \\ \end{tabular} \\ \hline
1 & 9 K & 5.35 & \(-\)101.00 & 106.35 & 5.50 & \(-\)99.00 & 104.50 \\ \hline
2 & 10 K & 5.32 & \(-\)99.00 & 104.32 & 5.49 & \(-\)98.00 & 103.49 \\ \hline
3 & 50 K & 5.30 & \(-\)97.00 & 102.30 & 5.49 & \(-\)97.00 & 102.49 Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the follow-up project has not been finalized.  
The authors declare no conflict of interest.
## 5 Multi-Giga-Hertz Agile Radio Front-End Design
### Remarks  
The multi-giga-Hertz agile radio is a new technical trend in communications and radar applications, in response to the need for efficiently sharing the scarce spectral resource. The front-end design and implementation is the most difficult part in this revolutionary radio. High level front-end design has been proposed through an example. The methodology used here can be applied to different situations, depending on specific frequency band planning, required minimum signal bandwidth, and hardware availability.
# Crowdsourcing Framework for QoE-Aware SD-WAN +
## 4 Proposed Framework
### QoE Crowdsourcing
**User-Side QoE feedback Collection Application**  
Establish a TCP connection to the server on a predefined port,  
While TRUE {  
Wait for user input,  
If input received  
Send an HTTP POST message to the server  
Server-Side QoE feedback Rating Application  
Listen on a predefined port for connection requests;  
While in a connection {  
Listen for HTTP POST messages;  
If POST message includes negative feedback  
Increment the negative feedback counter;  
If negative feedback counter >=THRESHOLD  
Send a REST message to the controller
## 2 Method and Materials
### Establishment of the Numerical Simulation Models
#### 2.2.2 Boundary Conditions and Solution Setup  
The resistance characteristics of the porous media were determined using the surface permeability (\(\alpha\)) and the pressure jump coefficient (\(C_{2}\)). These parameters were derived experimentally based on [PERSON]'s law for fluid flow through porous media, with a quadratic polynomial used to relate pressure drop to velocity. The relevant equations are provided in Equations (8) and (9) [37]:  
\[\
abla p=-\bigg{(}\frac{\mu}{\alpha}p+C_{2}\frac{1}{2}\rho v^{2}\bigg{)} \Delta n=a\cdot v^{2}+b\cdot v, \tag{8}\]  
\[D=\frac{1}{\alpha}. \tag{9}\]  
The filter bag used in this study is a PTFE membrane filter bag, commonly applied in practical engineering. The hydrodynamic viscosity at the time of the test was \(\mu\) = 1.81 \(\times\) 10\({}^{-5}\) Pa-s, the air kinematic viscosity was \(\
u\) = 1.5 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s, and the density was \(\rho\) = 1.205 kg/m\({}^{3}\). These values were used as the basis for subsequent CFD numerical simulations of the fluid parameters. The results of the resistance performance test are shown in Figure 2a. Based on these results, the following calculations were made:  
\[C_{2}=\frac{2a}{\rho\Delta n}=3.76\times\mathrm{e}^{6}\,\mathrm{m}^{-1},\]  
\[D=\frac{1}{\alpha}=\frac{b}{\mu\Delta n}=1.28\times\mathrm{e}^{12}\,\mathrm{m} ^{-2}.\]  
Given the significant difference in scale between the filter bag thickness and the filter geometry, using the porous zone model requires a large grid count, leading to high computational costs. In contrast, the porous-jump model approximates the filter bag as a thin membrane, requiring fewer grids and offering higher efficiency and robustness. Both models were used to simulate the airflow through a single filter bag, with the velocity contours shown in Figure 2b. The porous zone model accurately captures the filter bag boundary, while the porous-jump model lacks a distinct boundary but provides similar velocity distributions on both sides of the bag boundary. This indicates that the porous-jump model reliably represents the overall flow field and velocity profile along the filter length. Therefore, the porous-jump model was chosen for the simulations in this study.  
In the simulation, the inlet boundary is defined as a velocity inlet, the outlet as an outflow boundary, and the walls as no-slip stationary surfaces. The filter bag boundaries are modeled with the porous-jump method. Upon implementing the discrete phase model, the injection source was defined at the dust hopper inlet, with the filter bag surface and hopper walls designated as collection boundaries, assuming a 100% capture efficiency. The remaining chamber walls and deflectors were assigned as reflective boundaries. The model was configured to maintain consistent particle densities across all groups, with gravity effects incorporated. The solver employed a steady-state, three-dimensional, pressure-based approach with fully implicit coupling, using a second-order upwind scheme for convective terms.
###### Abstract  
The quality inspection of solder joints on aviation plugs is extremely important in modern manufacturing industries. However, this task is still mostly performed by skilled workers after welding operations, posing the problems of subjective judgment and low efficiency. To address these issues, an accurate and automated detection system using fine-tuned YOLOv5 models is developed in this paper. Firstly, we design an intelligent image acquisition system to obtain the high-resolution image of each solder joint automatically. Then, a two-phase approach is proposed for fast and accurate weld quality detection. In the first phase, a fine-tuned YOLOv5 model is applied to extract the region of interest (ROI), i.e., the row of solder joints to be inspected, within the whole image. With the sliding platform, the ROI is automatically moved to the center of the image to enhance its imaging clarity. Subsequently, another fine-tuned YOLOv5 model takes this adjusted ROI as input and realizes quality assessment. Finally, a concise and easy-to-use GUI has been designed and deployed in real production lines. Experimental results in the actual production line show that the proposed method can achieve a detection accuracy of more than 97.5% with a detection speed of about 0.1 s, which meets the needs of actual production  
quality inspection system; solder joints; aviation plugs; fine-tuned YOLOv5
###### Abstract  
A new feature extraction technique called DNST-GLCM-KSR (discrete non-separable shearlet transform-gray-level co-occurrence matrix-kernel spectral regression) is presented according to the direction and texture information of surface defects of continuous casting slabs with complex backgrounds. The discrete non-separable shearlet transform (DNST) is a new multi-scale geometric analysis method that provides excellent localization properties and directional selectivity. The gray-level co-occurrence matrix (GLCM) is a texture feature extraction technology. We combine DNST features with GLCM features to characterize defects of the continuous casting slabs. Since the combination feature is high-dimensional and redundant, kernel spectral regression (KSR) algorithm was used to remove redundancy. The low-dimension features obtained and labels data were inputted to a support vector machine (SVM) for classification. The samples collected from the continuous casting slab industrial production line--including cracks, scales, lighting variation, and slag marks--and the proposed scheme were tested. The test results show that the scheme can improve the classification accuracy to 96.37%, which provides a new approach for surface defect recognition of continuous casting slabs.  
continuous casting slabs; surface defect classification; discrete non-separable shearlet transform; gray-level co-occurrence matrix; kernel spectral regression +
Footnote †: journal: Applied Sciences
## 4 System Model and Description of the Proposed Protocols
### Proposed Protocol 1 (EP-VIR-Three)
#### 4.1.3 Description of the Algorithm 1  
In this subsection, the data forwarding algorithm for EP-VIR-Three is discussed. Firstly, the input parameters for the routing protocols are initialized. Then, the sensor, sink and relay nodes are deployed in the network with different surface gateways. Afterwards, a beacon message is broadcasted, which helps the neighbor nodes to update their routing table. Moreover, the protocol checks the 3-hop neighbors availability to avoid the void hole problem. In the end, transmission begins by selecting the next forwarder node after confirming 3-hop neighbors availability with a minimum number of neighbors. Furthermore, if the current forwarder lies within the range of the respective sink, then the data is directly transmitted to the destined sink; otherwise, the next forwarder node is selected (provided by 'CF' value, 3-hop neighbors information and its depth should be greater than the sender node).  
```
Input: (nodes, initial energy, sinks, PS, DR, \(T_{p}\), \(R_{p}\), area)  Deployment of surface gateways, sink and relay nodes  Broadcast the beacon message for Node i \(\epsilon\) Nodes do  Check for 3-hop neighbors if 3-hop neighbors exist then  Calculate the \(yd\) from the surface  Consider this node as not void node  Maintain a neighbor table endif endfor for  Node i \(\epsilon\) Nodes do if Sink is in \(R_{t}\)then  Packet will deliver to sink  Go at the start of the loop else  Select the forwarder node with minimum neighbors  Select the forwarder node having minimum distance from the surface if Forwarder node not found then  Packet will drop  endif endif endfor Output: (EC, Affordable E2E delay and low packet loss ratio)
```  
**Algorithm 1** Algorithm of EP-VIR-Three for data forwarding