esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 2 Background Introduction
[30] evaluated the performanceand adversarial sensitivity of Transformer-based neural networks. [PERSON] et al. [31] introduced multiple training methods to construct robust DNN models and evaluate them. [PERSON] and [PERSON] [32] applied attack assessment to evaluate the vulnerability of the Internet of Things device identification system and discovered new attack methods. As an important application scenario of communication systems, wireless communication has concrete manifestations and further development in which the above research results are presented. In wireless communication, [PERSON] et al. [33] proposed an adversarial attack method for automatic modulation identification. [PERSON] et al. [34] explored its threats and impacts on automatic modulation recognition. [PERSON] et al. [35] verified the effectiveness of the attack in the form of external interference. [PERSON] et al. [36] improved robustness by increasing the noisy training data. [PERSON] et al. [37] studied the channel influence and proposed a channel-aware attack method. Meanwhile, frequency-domain attacks emerged. [PERSON] et al. [38] utilized the low-frequency component attack algorithm, and [PERSON] et al. [39] discovered different responses of the defense model to high- and low-frequency disturbances. [PERSON] et al. [40] proposed the adversarial attack on DNNs by dropping information (AdvDrop) attack on neural networks. Despite these advancements, significant challenges remain. Traditional methods struggle in complex and dynamic environments, sparse estimators falter when ideal sparsity assumptions are violated, and deep learning-based approaches still require improved generalization and robustness. To address these issues, we propose a novel channel estimation framework that enhances both accuracy and robustness. Additionally, we introduce a frequency-domain adversarial sample generation method that leverages channel state information to assess the critical role of accurate estimation in communication security. This work bridges the gap between performance and robustness, contributing to the development of reliable and secure wireless communication systems.
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 3 Erosion Mechanism in Cavitating Jets
### Effect of Nozzle Surface Roughness  
It should be noted that there is an optimum finish that should be applied to the inner surface of the nozzle to achieve an optimal erosion regime. It was shown [44] that an extremely smooth surface reduces erosion rates because less cavitation bubbles are generated inside the nozzle; an excessive roughness can lead to significant energy losses, distorted jet structure, and as a result, a reduced erosion. [PERSON] et al. [44] studied six organ-pipe cavitating nozzles with different surface roughness (0.8, 1.6, 3.2, 6.3, 12.5, and 24 \(\upmu\)m). The specific energy consumption and mass loss for different inlet pressures and target distances were measured. All experiments were conducted at 20 \({}^{\circ}\)C using tap water. It was shown that the best erosion effect occurred for both inlet pressures of 10 and 15 MPa and a surface roughness of 12.5 \(\upmu\)m at the optimal target distance of 50 mm, whereas for inlet pressures of 20 and 25 MPa, the optimal roughness was 6.3 \(\upmu\)m and the optimal distance to target increased to 60 mm.
## 6 Evaluation
In summary, as shown in Figure 4, WTOL outperformed BL and POL with a precision of 70.7%, recall of 73.8%, and F-score of 69.0%. WTOL achieved the best result spanning across all users. Specifically, in the case of user I, WTOL dramatically improved the prediction result compared to BL and POL. User I shows the distinct changes in usage patterns. Therefore, BL and POL suffer from an accuracy drop due to the change in user behavior. WTOL is robust to severe usage changes thanks to time-invariant features and online learning.
###### Abstract  
In this article, a low-complexity and high-throughput sorted QR decomposition (SQRD) for multiple-input multiple-output (MIMO) detectors is presented. To reduce the heavy hardware overhead of SQRD, we propose an efficient SQRD algorithm based on a novel modified real-value decomposition (RVD). Compared to the latest study, the proposed SQRD algorithm can save the computational complexity by more than 44.7% with similar bit error rate (BER) performance. Furthermore, a corresponding deeply pipelined hardware architecture implemented with the coordinate rotation digital computer (CORDIC)-based Givens rotation (GR) is designed. In the design, we propose a time-sharing Givens rotation structure utilizing CORDIC modules in idle state to share the concurrent GR operations of other CORDIC modules, which can further reduce hardware complexity and improve hardware efficiency. The proposed SQRD processor is implemented in SMIC 55-nm CMOS technology, which processes 62.5 M SQRD per second at a 250-MHz operating frequency with only 176.5 kilo-gates. Compared to related studies, the proposed design has the best normalized hardware efficiency and achieves a 6-Gbps MIMO data rate which can support current high-speed wireless communication systems such as IEEE 802.11 ax.  
sorted QR decomposition; real-value decomposition; Givens rotation; multiple-input multiple-output detector +
Footnote †: journal: Electronics Electronics
## 2 Materials and Methods
### Methods
#### 2.2.4 Abandoned Farmland Data Extraction  
In this study, abandoned farmland data were extracted based on the differences in the NDVI changes between the growing and non-growing stages of the crops and natural vegetation. First, we visually interpreted the sampling points with GF-2, GF-6, and Google Earth images and defined the points with no crop growth in a year as abandoned points. Then, we inputted the annual NDVI difference value to the sampling points in ArcGIS and statistically obtained the range of values of the annual NDVI difference for all sampling points. After this, we divided the range into ten intervals and statistically analyzed the proportion of abandoned points to all points in each interval. The segmentation threshold was determined by the interval where the inflection point of the curve of the change in its proportion was located. Then, the data regarding cultivated and abandoned land in each year were extracted by the threshold segmentation; the parts with a value less than the threshold were considered abandoned land, while the rest were not. Finally, the area of abandoned land for each year was overlain to obtain the total area of abandoned farmland for these two years.
## 3 Results
### Changes in Annual Precipitation and Average Temperature  
Based on an analysis of time-series data of the annual precipitation and the annual mean temperature from 2000-2016, there was no obvious trend for the annual precipitation, while there was a significantly increasing trend of the annual mean temperature at the regional scale. The variation rate of annual precipitation was 1.07 mm/a (\(p\) \(>\) 0.1), and the variation rate of annual mean temperature was 0.05 \({}^{\circ}\)C/a (\(p\) \(<\) 0.01). Similarly to NDVI and NPP, the trend for annual precipitation and annual average temperature for the 2000-2014 period was analyzed. The regional climate conditions of the Sanjiangyuan National Park exhibited a significantly warming and wetting trend from 2000-2014, with an annual increase in the precipitation and temperature of 4.37 mm/a (\(p\) \(<\) 0.05) and 0.05 \({}^{\circ}\)C/a (\(p\) = 0.05), respectively.  
The Yellow River headwater region indicated a significant increasing trend in annual precipitation at a rate of 7.03 mm/a (\(p\) \(<\) 0.05) (Figure 7b), and the Yangtze River headwater region also indicated a slightly significant increase trend at a rate of 3.83 mm/a (\(p\) \(<\) 0.1) (Figure 7c), while the Lancang River headwater region showed no significant change in annual precipitation (Figure 7d). The annual average temperature at the Yellow River headwater region exhibited a significant upward trend at a rate of 0.0433 \({}^{\circ}\)C/a (\(p\) = 0.053) (Figure 8b), the Yangtze River headwater region at a rate of 0.0483 \({}^{\circ}\)C/a (\(p\) = 0.053) (Figure 8c), and the Lancang River at a rate of 0.0543 \({}^{\circ}\)C/a (\(p\) = 0.05) (Figure 8d).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Region** & **ESI** & **SI** & **IS** & **SD** & **ESD** \\ \hline Yellow River & 6.27\% & 11.40\% & 81.28\% & 0.73\% & 0.32\% \\ Lancang River & 0.36\% & 1.75\% & 94.73\% & 2.49\% & 0.68\% \\ Yangtze River & 15.20\% & 13.58\% & 70.08\% & 0.78\% & 0.36\% \\ Sanjiangyuan & 11.78\% & 11.68\% & 75.15\% & 1.00\% & 0.39\% \\ \hline \end{tabular}
\end{table}
Table 2: NDVI changes in different areas of the Sanjiangyuan National Park.  
Figure 6: Spatial distribution of NDVI change (**a**) and NPP change (**b**) from 2000–2016.  
Figure 8: Temporal variations of the annual average temperature for the Sanjiangyuan National Park (**a**), the Yellow River headwater region (**b**), the Yangtze River headwater region (**c**), and the Lancang River headwater region (**d**).  
Figure 7: Temporal variations of the annual precipitation for the Sanjiangyuan National Park (**a**), the Yellow River headwater region (**b**), the Yangtze River headwater region (**c**), and the Lancang River headwater region (**d**).
## 2 Theories, Simulation, and Experiment Methods
### Experimental Verification  
The reverberation room test is conducted to measure the performance of the sound-proof panel with the regulation of ISO 10140-5. A schematic diagram of the reverberation room consisting of two rooms is shown in Figure 2. The source room has a volume of 51.54 m\({}^{3}\) and that of 57.02 m\({}^{3}\) for the receiving room.  
The measuring devices and specifications are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Device Name** & **Specification** \\ \hline Real time analyzer & PAK MK II, Muller-BBM, Germany \\
1/2-inch microphone & 46 AE, G.R.A.S., Denmark \\ Sound level calibrator & Cal-Q2, 01 dB, France \\ Power amplifier & CONA V2-5000, Inter-M, Korea \\ Speaker & D012, FALM, Germany \\ SRX 725, JBL, USA \\ Control PC & Workstation, Xeon 4, HP, USA \\ \hline \end{tabular}
\end{table}
Table 1: List of measuring devices and specifications for the test.  
In the test, five non-directional microphones are used to derive the average value of sound pressure level as in Equation (5).  
\[L=10 log\left(\frac{1}{n}\sum_{i=1}^{n}10^{L_{i}/10}\right)\,, \tag{5}\]  
where \(L_{i}\) is the measured value of the _ith_ sound pressure level (Pa) and \(n\) is the number of measuring points.  
The equivalent absorption is defined as:  
\[A=\frac{0.16V}{T}\,, \tag{6}\]  
where \(V\) is the volume of the receiving room (m\({}^{3}\)) and \(T\) is the reverberation time in the receiving room. From the above equations, the sound reduction index is calculated as:  
\[R=L_{1}-L_{2}+10 log(s/A)\,, \tag{7}\]  
where \(L_{1}\) is the average sound pressure level in the source room (dB), \(L_{2}\) is the average sound pressure level in the receiving room (dB), and \(S\) is the area of the test surface (m\({}^{2}\)).
## 2 Numerical Simulations and Wind Tunnel Tests
### Wind Tunnel Tests  
To verify the accuracy of the numerical simulations, wind tunnel tests based on the impinging jet model were performed. The impinging jet test was conducted at the Structural Wind Engineering and Urban Wind Environment Laboratory of Beijing Jiaotong University. Figure 6 shows the impinging jet device simulating the wind field of a downburst. The outlet diameter of the downburst simulator is D = 600 mm, and the height of the nozzle to the floor can be adjusted using a hydraulic device in the range of 400 mm-800 mm. The maximum wind velocity at the outlet, \(u_{\mathrm{jmax}}\), is 12 m/s.  
In this test, a three-dimensional wind velocity tester, the Cobra Probe produced by Turbulent Flow Instrumentation (TFI), and a three-dimensional side-shifting frame were used to measure wind velocity at different radial positions. The Cobra Probe is a four-hole pressure probe with a wind velocity measurement range of 2-100 m/s and a typical measurement accuracy of \(\pm\)0.5 m/s. The sampling frequency was set to 256 Hz, and the sampling time was 30 s. The mean of three measurements was processed to reduce error. All other relevant settings of the impinging jet test remained the same as in the numerical simulation.  
Figure 5: (**a**) Comparison of vertical wind profiles at different radial position; (**b**) comparison of velocity with different grid numbers.
## 1 Introduction
### Dwell Time Delays and Passenger Volumes  
Several studies have identified the effect of passenger volumes and movement rates on dwell times. Studying the boarding and alighting movement rate of passengers on Dutch railway stations, [PERSON] [22] found that boarding and alighting times are typically around one second per passenger when passengers move in a cluster. Individual, or non-clustered, movement rates are slightly higher, taking more than four seconds. The authors of [18] present a linear relationship between dwell times and passenger flows, where dwell time increases as passenger flows increase. The authors of [20] found that dwell times can increase by up to 0.3 s for every additional boarding and 0.4 s for every additional alighting passenger. They [23] also found the dominant factor for passenger service times to be the volumes of boarding and alighting passengers, which increase service time by around 0.8 and 0.9 s per additional passenger, respectively.  
The majority of existing dwell time or passenger service time models assume a constant rate for boarding and alighting over the whole passenger service time [23], which is not necessarily the case. The authors of [11], for example, found that boarding and alighting rates are not constant across a group of passengers. A later study indicated that passenger movement rates first increase and then decrease during the boarding and alighting process [1]. Similar findings are reported by [21], also showing that the speed of boarding and alighting passengers changes during the dwelling process.
## 4 Discussion  
### Uncertainty of XCO\({}_{2}\) Global Land Mapping Dataset  
#### 4.1.1 Evaluation Using Cross-Validation  
Cross-validation is widely used in accuracy assessments for statistical models [66; 20]. In this study, we used cross-validation based on the Monte Carlo sampling technique adopted by [PERSON] et al. [20]. The Monte Carlo method is carried out by randomly sampling 5% of the data that will be left out in cross-validation, and repeating this process 100 times. Please refer to [PERSON] et al. [20] for technical details. From the outputs of cross-validation, two datasets, the predicted dataset and the original observation dataset, are obtained. The distribution of observed values and predicted values of XCO\({}_{2}\) is shown in Figure 8. We can see the predicted XCO\({}_{2}\) well agrees with observed XCO\({}_{2}\) with high R\({}^{2}\) of 0.91, indicating a very strong correlation between the original ACOS-GOSAT data and the predictions. The mean absolute prediction error (MAPE), which is the averaged prediction error, is 0.85 ppm, indicating that the averaged absolute error for each prediction is less than 1 ppm. Specifically, 70% of the prediction errors are less than 1 ppm. These statistics from cross-validation study suggest that the used mapping method based on spatio-temporal geostatistics is effective and precise in generating global land mapping of XCO\({}_{2}\) from original retrievals. The uncertainty in the mapping XCO\({}_{2}\) data comes from a combination of the uncertainty from the mapping method and the uncertainty propagated from satellite retrieval error. This latter source of data uncertainty is discussed in the following section by comparing with TCCON.
# Model Mediation to Overcome Light Limitations--Toward a Secure Tactile Internet System
## 4 Model Mediation
### Model Mediated Systems
#### 4.2.2 Common Systems
In [73], a haptic-based bilateral teleoperation system is introduced, based on a hybrid prediction mechanism. The predictor algorithm combines the signal-based prediction with the geometry-based prediction to facilitate the haptic data transfer and enable the user experience. The system employs two main modes for the signal-based prediction; linear first order mode and zero order mode, each of them identifies a prediction way. For the geometry-based prediction, the prediction is done based on the perceived tactile information about the surface includes, stiffness, friction, and position. The future geometry parameters are predicted based on the current geometry parameters. This predictor is used for the rendering of haptic signals from remote environment. The system is experimentally tested using phantom desktop haptic devices and the CHAI3D computer interface [74]. The system achieved a high performance in terms of data reduction, nearly 69% reduction. In the other side, the system model mainly considered the stiffness and the surface geometry structure as the physical parameters and neglects other main parameters such as friction coefficient and inertia of the slave device. Also, the system model did not consider the communication delay and the effect of various communication delays on the system performance and stability. In [75], authors proposed a point-cloud-based model mediated (Pcbmm) system for bilateral communication in a transparent and stable way. The system is developed for complex remote environments and these hard environments are modeled using point clouds rather simple geometry approximation. The slave device employs a time-of-flight camera that is used to capture the 3-D geometry of the environment [76]. The slave device uses the 3-D images with its position and measured forces to extract parameters of the environment model that are sent directly to the master side. These parameters are updated every 500 ms to consider any sudden changes that may occur. The master side uses these parameters to obtain or update the remote environment model and extracts the force feedback signal. One main considered component of the system is the updating controller, which is deployed to control the updates in the physical properties of the slave and the environment. Figure 6: System structure of the model mediated bilateral teleoperated system with two coherent linear predictors developed in [71]. This enhances the force disturbance and reduces the packet rate transferred between the master and the slave sides. The system is tested using master device of Omega.6 and a slave device of a KUKA lightweight arm, with a 6 DoF force sensor. The system achieves higher efficiency in terms of transparency and stability when experimentally evaluated. One more strong advantage of this system is the date reduction; the system achieves a 90% percentage reduction in haptic data. On the other side, the system does not consider movable and deformable objects in the remote environment modeling.
## 2 Materials and Methods
### Preprocessing  
Epileptic EEG signals can be divided into three stages, namely, interictal, preictal, and ictal. In our seizure prediction task, we mainly focused on the preictal and interictal periods. Therefore, we discarded the ictal EEG segments and transformed seizure prediction into a binary classification problem. At the same time, we rearranged the EEG data for each patient according to the electrode order. In this dataset, the interictal and preictal data were severely unbalanced since some patients had fewer seizures during the monitoring period. In order to overcome this problem, we used overlapping sliding windows in the training stage to obtain more preictal fragments. To ensure that the ratio of the two types of training data was close to 1, we set the window size of preictal to \(W_{p}\) as follows:  
\[W_{p}=\frac{n_{p}}{n_{i}}W \tag{1}\]  
where \(n_{p}\) and \(n_{i}\) represent the number of EEG segments in each patient's preictal and interictal stage, respectively, and \(W\) represents the size of the EEG window.  
STFT has been widely used in the field of signal processing, and many studies have proved that it has advantages in the analysis of time series [35; 36]. Therefore, in this paper, STFT is used to convert the EEG signal into a two-dimensional matrix composed of the time domain and frequency domain, and the EEG signal is analyzed in the time-frequency domain. We selected the cosine analysis window to perform the STFT on the 5 s sample, and then used \(log_{10}\) to calculate the intensity value. The EEG recordings in the CHB-MIT dataset were contaminated by 60 Hz power line noise. Therefore, in the experiment, we removed the components in the frequency range of 57-63 Hz, 117-123 Hz (power line frequency is 60 Hz), and the direct current (DC) component (0 Hz) so as to conveniently and effectively remove the interference of power line noise and DC component. After STFT processing, the dimension of each 55 EEG segment was (c, 114, 9), where c is the number of EEG channels, and 114 and 9 represent the number of frequency points and time steps, respectively.