esa-sceva/satcom-chunk-collection huggingface dataset:
# OTFS-Based Handover Triggering in UAV Networks
## 3 UAV to Ground Handover Scenarios and Channel Modeling
where  
\[\mathcal{A}\Big{(}i_{q},\beta_{i_{q}},k\Big{)}=e^{-j\pi\frac{(2^{n}q-u^{\prime}-1)( k-T\beta_{i_{q}}N)}{N}}\frac{\sin\left(n^{\prime}\frac{\pi}{N}\left(k-T\beta_{i_{q}}N \right)\right)}{\sin\left(\frac{\pi}{N}\left(k-T\beta_{i_{q}}N\right)\right)}, \tag{3}\]  
\[\mathcal{B}\Big{(}i_{q},\alpha_{i_{q}},l\Big{)}=e^{j\pi\frac{(M-1)(l-\Delta/i_ {q_{q}}M)}{M}}\frac{\sin\left(\pi\left(l-\Delta f\alpha_{i_{q}}M\right)\right) }{\sin\left(\frac{\pi}{M}\left(l-\Delta f\alpha_{i_{q}}M\right)\right)}, \tag{4}\]  
Herein, \(\mathcal{A}\Big{(}i_{q},\beta_{i_{q}},k\Big{)}\) and \(\mathcal{B}\Big{(}i_{q},\beta_{i_{q}},l\Big{)}\) are the Dirichlet kernels of Fourier transform. Also, \(0\leq l\leq M-1\) and \(0\leq k\leq N-1\), where \(l\) and \(k\) indicate the normalized delay and Doppler dimensions of the DD grid, and \(M\times N\) is the dimension of the OTFS symbol. In this context, the delay resolution is equal to \(\frac{1}{N\Delta f}\), while the Doppler resolution is equal to \(\frac{1}{NT}\). For notation simplicity, \(h_{DD}[l,k]\) will be used for the subsequent equations to indicate the DD CIR. As stated in [8], for each coherence period \(q\), \(h_{i_{q}}\) follows Rayleigh distribution, and \(\alpha_{i_{q}}\) comes from uniform random distribution in the range of \([-\alpha_{\max},\ \alpha_{\max}]\), where \(\alpha_{\max}\) is the maximum delay spread of the channel. Similarly, \(\beta_{i_{q}}\) is drawn from uniform random distribution in the range of \([-\beta_{\max},\ \beta_{\max}]\), where \(\beta_{\max}\) is the maximum Doppler frequency, which is equal to \(vf/c\), where \(v\) is the speed of the object, \(f\) is the operating frequency, and \(c\) is the speed of light. Detailed derivations of this time-varying DD channel modeling can be found in [8], where an algorithm for generating the DD CIR in HST scenario was proposed.
## 4 Results
### Comparison with Global LAI Products  
The validation of moderate-resolution products are key steps to assess the quality of the global LAI products. In this study, we performed a comparison among GEOV3, MODIS LAI products and upscaled Sentinel-2 LAI to assess the quality of these two products in Northwest China. To conduct  
Figure 11: Agricultural fields boundaries collected by manual vectorization.  
Figure 12: Final cotton LAI map obtained using GBRT model and masked by the extracted cotton fields. (**a**) Early June 2018; (**b**) Mid July 2018;(**c**) Mid August 2018.  
the comparison of the cotton LAI results obtained using the GBRT model with global LAI products, we choose the closest dates of the products at a spatial resolution of the products to minimize the spatial and temporal differences [2].  
All Sentinel-2 LAI maps were resampled to the same spatial resolution of MODIS and GEOV3 LAI products, and then GEOV3 LAI product were also resampled to the same spatial resolution of MODIS LAI product to make a straight comparison of these two products. Figure 13 shows the scatter plots among the comparison of GEOV3, MODIS and Sentinel-2 cotton LAI over three observation dates. Regarding the comparison of the MODIS LAI product and Sentinel-2 LAI maps, it was found that both the MODIS LAI product and the Sentinel-2 LAI suffered from the influence of the background, and are kind of overestimated in some areas in early June 2018. MODIS LAI product underestimated a little bit in mid-August 2018. In terms of GEOV3 LAI product and Sentinel-2 LAI maps, the GEOV3 LAI product overestimated at some areas both in early June and mid-July 2018, and underestimated in mid-August 2018. Finally, with respect to the comparison of the two global LAI products, relatively, large differences are found in early June and mid-July 2018, whereas a good relationship appeared in mid-August 2018.  
Figure 13: Scatter plots among GEOV3, MODIS LAI products and upscaled Sentinel-2 LAI in the 2018 cotton season.
###### Abstract  
In the integrated services digital broadcasting-terrestrial (ISDB-T) system, the combination of digital terrestrial transmission and MPEG-4 advanced video coding (MPEG-4 AVC) has offered ways to provide a variety of digital high-definition television (HDTV) programs. Using band segmented transmission orthogonal frequency division multiplexing (BST-OFDM), the delivery of innovative video-on-demand and HDTV services is supported. To take full advantage of the attractive benefits of BST-OFDM, it is important to estimate integer frequency offset (IFO) without a priori knowledge on the segment type that is transmitted over transmission and the multiplexing configuration control (TMCC) signal. To address this issue, an efficient IFO detection method is proposed for the ISDB-T system employing BST-OFDM. To enable IFO detection independent of the segment type, information-bearing TMCC signals that are asymmetrically distributed in the frequency domain are used as pilot symbols. Numerical analysis is performed to present the relationship between error probability and design parameter. We show via the numerical results that the multiple transmitted TMCC information is efficiently used for blind estimation of the IFO, achieving robust estimation in the presence of the fractional frequency offset.  
orthogonal frequency division multiplexing; band segmented transmission; integer frequency offset; transmission and multiplexing configuration control
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 3 Experimental Investigation
### The Coda Wave Test under Uni-Axial Stress
#### 3.4.3 Data Processing and Parameter Fitting  
According to the CWI analysis method, we calculated the wave velocity change of different stress levels. The least square method was used to fit the parameters \(k_{\text{c1}}\), \(k_{\text{f1}}\), \(k_{\text{f2}}\), \(k_{\text{c2}}\), \(k_{\text{f21}}\), and \(k_{\text{f22}}\) in Equation (22). The data from the increasing stage of the sound velocity change was used to fit \(k_{\text{c1}}\) and \(k_{\text{c2}}\) in Equations (22a) and (22c), and the data from point at which the change of sound velocity begins to decline was used to fit \(k_{\text{f1}}\), \(k_{\text{f2}}\), \(k_{\text{f21}}\), and \(k_{\text{f22}}\) in Equations (22b) and (22d). The fitting results are given in Tables 3 and 4.  
After inserting the fitted parameters into the models, we obtain:  
Axial relative sound velocity:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Parameter** & **Fitted Value** & **Correlation Coefficient R** \\ \hline \(k_{\text{c2}}\) & \(9.58\times 10^{-4}\) & 0.9237 \\ \hline \(k_{\text{f21}}\) & \(1.326\times 10^{-4}\) & \\ \(k_{\text{f22}}\) & \(6.147\times 10^{-6}\) & 0.9915 \\ \(\sigma_{\text{u}}\) & 12.33 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fitting results of horizontal crack parameters.  
Figure 10: Relation between the relative change of horizontal sound velocity and stress.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Parameter** & **Fitted Value** & **Correlation Coefficient R** \\ \hline \(k_{\text{c1}}\) & \(6.314\times 10^{-3}\) & 0.9359 \\ \hline \(k_{\text{f11}}\) & \(2.395\times 10^{-4}\) & \\ \(k_{\text{f12}}\) & \(5.454\times 10^{-7}\) & 0.9109 \\ \(\sigma_{\text{u}}\) & 10.77 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting results of axial crack parameters.  
Crack closure stage \[\frac{\Delta V_{1}}{V_{\text{p0}}}=\frac{1}{1-6.314\times 10^{-3}\sigma}-1\] (25a) Crack expansion stage \[\frac{\Delta V_{1}}{V_{\text{p0}}}=\frac{1}{0.9320+2.395\times 10^{-4}\times( \sigma-10.77)+5.454\times 10^{-7}\times(\sigma-10.77)^{3}}-1\] (25b) The horizontal sound velocity can be expressed as: Crack closure stage \[\frac{\Delta V_{2}}{V_{\text{p0}}}=\frac{1}{1-9.58\times 10^{-4}\sigma}-1\] (25c) Crack expansion stage \[\frac{\Delta V_{2}}{V_{\text{p0}}}=\frac{1}{0.9882-1.326\times 10^{-4}(\sigma-12. 33)+6.147\times 10^{-6}(\sigma-12.33)^{3}}-1\] (25d)  
Compared with the experimental data, as shown in Figures 11 and 12, it was found that the results obtained by the fitted model agree well with the experimental data, indicating that the proposed model can reflect the propagation law of ultrasonic wave when stress changes in concrete.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 1 Introduction
On the basis of Ultrasonography, CT, MRI, PET and other imaging technologies, the key for optimizing multidisciplinary treatment for liver cancer is to improve and combine existing medical imaging technologies, which would allow the clear visualization of tumor lesions along with their characteristic indicators, such as tumor size, margin, the absence of vascular invasion, adjacent structure involvement, lymph node metastasis and distant spread. The remainder of this article is divided into five sections. Section 2 discusses the imaging methods commonly used in liver tumor diagnosis and treatment; Section 3 provides a brief introduction of the general procedure used for medical image fusion; Section 4 presents several image fusion algorithms; Section 5 describes the application of image fusion in liver tumor imaging; and Section 6 covers our Conclusion and suggestions for further research.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method  
### Overall Approach  
The purpose of this evaluation is to find a high-performance processor suitable for space applications. Therefore, the constructed benchmark tests need to be relevant to the space domain, using representative input data while covering as many space domains as possible. This ensures the correctness of the benchmark tests and that the implementation follows relevant standards, with corresponding reference outputs to check the correctness of the target platform's output. This performance evaluation includes the CPU and GPU of the device, and it is conducted from multiple perspectives, as shown in Figure 1.  
* The algorithms and input configurations in the benchmark need to cover existing spaceborne processing applications, as well as future scenarios and their performance requirements in different space domains;* The benchmark tests should not be limited to a given processor, such as supporting a single programming model or architecture, but should be able to run on multiple platforms;
* The platform-independent parts of the benchmark should be the same for all platforms to enable fair comparisons;
* It is necessary to use common performance metrics for comparison. The performance metrics used in this evaluation include commonly used metrics such as total execution time, throughput, and FLOPS.
## 5 Discussion
#### Potential Implications
By monitoring trainees' emotional responses to high-stress scenarios, instructors could gain valuable and impactful insights into how individuals manage pressure and stress. Such a system could dynamically adapt training scenarios in real-time and gradually increase intensity to help trainees develop resilience and learn to manage stress in demanding environments. This adaptive training approach could result in more effective preparation for real-world challenges, underscoring the importance and impact of the proposal. Healthcare and Well-Being: Beyond psychotherapy, emotion recognition systems could be applied to general healthcare and well-being monitoring. In virtual fitness environments, these systems could detect signs of physical or emotional fatigue during a workout session. A virtual coach could then adjust the intensity or modify the exercise, promoting a balanced and mindful workout. Similarly, emotion recognition could be integrated into wellness applications, helping users manage stress and anxiety through real-time feedback in virtual relaxation or meditation environments.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 2 Related Work  
MOT has attracted significant interest from researchers in recent years. MOT algorithms can be divided into two categories according to whether the detection and prediction use the same network: one-stage and two-stage MOT.
## 4 Simulation Test and Result Analysis  
### Experimental Platform and Data  
In this section, the performance of the proposed FDIA detection mechanism is evaluated in the context of IEEE 118-bus power systems. The grid topologies were acquired from MATPOWER [27]. All simulations were executed on a computer system featuring an AMD Ryzen 5 5600H with Radeon Graphics CPU, NVIDIA GeForce RTX 3050 Laptop GPU, AMD Radeon (TM) Graphics, and 64 GB of RAM. The GAN-GRU and Transformer neural network were constructed utilizing the Torch to enhance computational efficiency.  
In this experiment, the original load dataset was derived from [24]. To broaden the scope of authentic data and generate unattacked datasets, a manual approach was employed to simulate the load distribution across individual buses. This approach serves a dual purpose, not only augmenting the diversity and volume of datasets but also furnishing a more comprehensive array of training samples to underpin our classification endeavor. When generating these load data points, a normal distribution was assumed, whereby the mean load data are aligned with the baseline load value, and the standard deviation equates to one-sixth of the baseline load value. This meticulous manual simulation process allowed us to generate unattacked datasets that faithfully represent the load distribution across individual buses. By assuming a normal distribution, where the mean load data correspond to the baseline load value, and the standard deviation is set as one-sixth of the baseline load value, we ensured that our generated load data points adhered to realistic distribution patterns. This rigorous approach significantly enriched the diversity and volume of our datasets, laying a solid foundation for our classification efforts and enhancing the authenticity of the data we employed for this experiment.  
Consequently, a dataset encompassing 9098 sets of unattacked normal original data samples was effectively assembled. These samples were drawn from 180 m within the IEEE 118-bus network, representing load data reflecting typical operational conditions. The meticulous procurement of these data samples assumes a paramount role in ensuring experimental precision and dependability. Through the execution of the aforementioned experimental design and subsequent data processing, a dependable and representative dataset was meticulously formulated, thereby constituting a robust cornerstone for our research pursuits. Meanwhile, in order to simulate the historical data's inherent imbalance as encountered in real-world applications, an imbalanced sample dataset was formulated from the existing 9098 arrays, tailored to generate vectors of false data injections, spanning a spectrum of distinct attack modes.  
Furthermore, adhering to the guidelines presented in Section 3, explicit labels were assigned to each data category to uphold the precision and feasibility of the experiments. These labels, serving as inputs to the Transformer neural network, concurrently form an integral segment of the network's output classification. A detailed account of the quantity of datasets employed is provided in Table 1.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Cover Timing Patterns
#### 4.3.7 Artificial Resets (PT16)  
Pattern  
Similar to the _artificial reconnections_ pattern, connection resets are produced artificially by the covert sender. In contrast to reconnect, the hidden data are transferred via the connection states, which are reset during the reconnection. Within the PDCP layer, any entity has a certain state including several parameters and values for controlling transmission and operation of the PDUs. If the covert sender induces a re-establishment of the transmitting PDCP entity, the receiving PDCP entity performs a reset of these values. To decode the hidden data, the covert receiver has to monitor the changes in this state variables.
## 4 Configuration of DQN Agent and Environment
The moving averages are initialized with 0 and are updated at each iteration by the following formulas:  
\[m(t)=\beta_{1}\times m(t-1)+(1-\beta_{1})\Delta L(t) \tag{11}\]  
\[v(t)=\beta_{2}\times v(t-1)+(1-\beta_{2})\Delta L(t)^{2} \tag{12}\]  
where \(L\) is the loss function; \(\Delta L(t)\) is the gradient of the loss function at iteration \(t\); and \(\beta_{1}\) and \(\beta_{2}\) are hyperparameters that control the decay rate of the moving averages. In order to prevent bias in the initialization, the Adam uses bias-corrected versions of \(m(t)\) and \(v(t)\) denoted as \(\hat{m}(t)\) and \(\hat{v}(t)\), respectively:  
\[\hat{m}(t)=m(t)/(1-{\beta_{1}}^{t}) \tag{13}\]  
\[\hat{v}(t)=v(t)/(1-{\beta_{2}}^{t}) \tag{14}\]  
The update rule for the weights is then defined as follows:  
\[\omega(t+1)=\omega(t)-\alpha\times(\hat{m}(t)/\sqrt{\hat{v}(t)+\varepsilon}) \tag{15}\] \begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Learning rate & 0.001 \\ Target update frequency & 4 \\ Discount factor & 0.99 \\ Mini batch size & 256 \\ Activation function & ReLU \\ Optimizer & Adam \\ Gradient threshold & 1 \\ Max. episode & 10,000 \\ \hline \end{tabular}
\end{table} 
Table 2: Default hyperparameter values and function. where \(\omega(t)\) is the weight at iteration \(t\), \(\alpha\) is the learning rate, and \(\varepsilon\) is a small value added to the denominator to prevent division by zero. The training Q-network and the target Q-network provide eleven Q-values corresponding to eleven command voltages, respectively. The DQN agent selects the command voltage corresponding to the maximum Q-values to control the MR damper. The purpose of DQN learning is to make the Q-values of the target network and those of the training network the same. To this end, the training Q-network's weights are updated by minimizing the mean-squared error (MSE) loss between the predicted Q-values and the target Q-values as presented in Figure 10. This minimization process is conducted based on the Adam optimization algorithm described above. The variation of the MSE loss value according to the episode is shown in Figure 11. The flat part of the graph showing the early episodes means Not a Number (NaN), representing an undefined number in floating-point operations. Figure 11 shows that the MSE loss value decreased rapidly up to 1000 episodes and then gradually decreased to 8000 episodes. After that, the MSE loss value remained almost unchanged. This variation tendency of the MSE loss value indicates that the network training was well done.
## 4 Results
### Image Processing with OpenCV
#### 4.3.1 Colour Detection Algorithm  
The low-load processing algorithm consisted of recognising a given colour in each image delivered from Unity. For this case, the blue colour was detected in each frame. The algorithm in both languages was the same (see Algorithms 2 and 3). OpenCV provides a mask filter on the hue-saturation-value (HSV) model to obtain only the matched colour to detect. Contours were obtained and a green rectangle was drawn around the detected colour. The processing results were the same in C++ and Python, as shown in Figure 6. Both colour detection algorithms correctly identified the avatar's blue trousers in all frames over time.  
```
findContours(redOnly,contours,hierarchy,cv::RetrievalModes::RETR_REE,cv::ContourApproximationModes::CHAN_APPROX_SIMPLE); vector<Rect>boundRect(contours.size()); intx0=0,y0=0,w0=0,h0=0; for(inti=0;i<contours.size();i++){ floatarea=contourAre((Mat)contours[i]); if(area>300){ bpundRect[i]=boundingRect((Mat)contours[i]); x0=boundRect[i].x; y0=boundRect[i].y. w0=boundRect[i].width; h0=boundRect[i].height; rectangle(frame,Point(x0,y0),Point(x0+w0,y0+h0),Scalar(0,255,0),2,8); }
```  
**Algorithm 2** Colour detection algorithm in C++.  
```
contours,hierarchy=cv2.findContours(blue_mask,cv2.RETR_REE,cv2.CHAN_APPROX_SIMPLE) forpic,contourinenumerate(contours): area=cv2.contourArea(contour) if(area>300): x,y,w,h=cv2.boundingRect(contour) img=cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2,8)
```  
**Algorithm 3** Colour detection algorithm in Python.  
Three-hundred images of the Unity virtual environment were processed at 5 FPS using the two implemented colour algorithms to measure the differences in processing time between C++ and Python. The processing time of each algorithm is plotted in Figure 7.  
On the basis of the results, the colour detection algorithm implemented in C++ was found to have a shorter processing time. On average, it took 0.7 ms as compared to more than 2.5 ms for the Python algorithm. In both cases, fluctuations in processing time were observed, something more evident in Python.
## 2 Related Work and Contributions  
### Technology-Specific Detection Solutions  
Matched-filtering, waveform-based sensing and cycle-stationary feature detection are the most known technology-specific detections [15; 16]. Matched-filtering is commonly applied at the early stage of a wireless decoder to maximize the signal-to-noise (SNR) ratio. It outperforms energy detection in terms of sensitivity, however only for a given technology. Known patterns, such as preambles, are often utilized in wireless systems for the receivers to achieve coherence with the transmitters. Detectionbased on these patterns is highly precise, though some level of timing and carrier synchronization has to be achieved beforehand. Cycle-stationary feature detection exploits the built-in periodicity of modulated signals, which often involves autocorrelation or spectral correlation function [17]. The main advantage is its robustness to uncertainty in noise. However, it is computationally complex and requires a significantly long observation time [15]. The work in [18] leverages on the fact that modulated signals follow a Rayleigh distribution, while noise follows a Gaussian distribution, and transmitters are identified by extracting Rayleigh and Gaussian sub-components from the raw spectra. Although the primary goal of [18] is to establish a spectrum inventory for regulators, the utilization of sophisticated machine learning techniques and off-line observation makes it unsuitable for real-time applications. Given the state-of-the-art of technology-specific recognition, there is still a need to further reduce complexity and to break the interdependency between algorithms and technologies.