esa-sceva/satcom-chunk-collection huggingface dataset:
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 6 Results and Discussion
### The Influence of the SNR  
Figure 9 illustrates the variation of the percentage of hits as a function of the SNR of the audios. We can observe that most of the audio notes are in the [\(-2\) dB, \(2\) dB] range. Although it is difficult to find clear trends in terms of the evolution of the SNR hit rate, we can observe that it is in the middle ranges ([\(-2\) dB, \(7\)dB]) when it is easier to detect deepfakes. The extreme values (ranges [\(-7\) dB, \(2\) dB] and [\(7\) dB, \(12\) dB]) have the highest number of misclassified samples. This occurs for all classifiers except for the Naive Bayes case, where the range ([\(2\) dB, \(7\)dB]) has a higher percentage of misclassified audios than the range [\(-7\) dB, \(-2\) dB]. From the results obtained, it can be observed that the SNR has a significant impact on the accuracy of the classifiers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Linear SVM** &
\begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} & **KNN** & **Naive Bayes** \\ \hline V11O & 28.57\% & 50\% & 37.50\% & 68.18\% \\ \hline T02 & 14.28\% & 0\% & 6.25\% & 4.54\% \\ \hline T03 & 0\% & 0\% & 0\% & 0\% \\ \hline T01 & 28.57\% & 16.66\% & 1  
Figure 8: Percentage of audio misclassified for each classifier by user. The values displayed in red represent the percentage of audio samples incorrectly classified relative to the total number of samples for that user. For example, for Naive Bayes, for user _V110_, if the classifier displays 28.8% in red, it means that 28.8% of the total audio samples for that user are misclassified.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 2 Related Works  
In this section, we introduce underwater transmission protocols in turn, which can be divided into three main categories: reliable transmission protocols, low-latency transmission protocols and energy-efficient transmission protocols. Among these, each type of protocol is composed of MAC-layer and routing-layer protocols. A few cross-layer design protocols are also embedded in these three types of protocol.
# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
## 4 Proposed Methodology
### Data Preprocessing Phase
#### 4.1.1 Detecting and Correcting the Noisy Ratings
Add r(user, item) to the Weak_user set
7. else if r(user, item) \(\geq\) T^1_u and r(user, item) < T^2_u
8. Add r(user, item) to the Average_user set
9. else
10. Add r(user, item) to the Strong_user set
11. if r(user, item) < T^1_i,
12. Add r(user, item) to the Weak_item set
13. else if r(user, item) \(\geq\) T^1_i, and r(user, item) < T^2_i
14. Add r(user, item) to the Average_item set
15. else
16. Add r(user, item) to the Strong_item set
17. end for
18. for each user in the r(user, item)
19. Classify each user into Critical, Average and Strong user as follows:
20. Critical user:
21. if | Weak_user | \(\geq\) | Strong_user | + | Average_user |
22. Average user:
23. if | Average_user | \(\geq\) | Strong_user | + | Weak_user |
24. Strong user:
25. if | Strong_user | \(\geq\) | Average_user | + | Weak_user |
26. end for
27. for each item in the r(user, item)
28. Classify each item using the above discussed sets
29. Critical items:
30. if | Weak_item | \(\geq\) | Strong_item | + | Average_item |
31. Average item:
32. if | Average_item | \(\geq\) | Strong_item | + | Weak_item |
33. Strong item:
34. if | Strong_item | \(\geq\) | Average_item | + | Weak_item |
35. end for
36. for each rating r(user, item)
37. if user is Critical, item is Weakly_recommended, and r(user, item) \(\geq\) T^1
38. Add r(user, item) to the [Possible_noise]
39. if user is Average, item is Averagely_recommended and (r(user, item) < T^1 or r(user, item) \(\geq\) T^2)
40. Add r(user, item) to the [Possible_noise]
41. if user is Benevolent, item is Strongly_recommended, and r(user, item) < T^2
42. Add r(user, item) to the [Possible_noise]
43. end for
44. #Correct the noisy ratings
45. for each rating r(user, item) in [Possible_noise], predict a new rating R(user, item) using Matrix Factorization approach
46. k = 3 # it represents the features
47. if (abs (R(user, item) - r(user, item)) > \(\delta\))
48. Replace r(user, item) by R(user, item) in the original rating set r
49. end if
50. end for
```  
**Algorithm 1**Steps of Detecting and Correcting Noisy Ratings
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 2 Related Work
### Siamese Networks for Change Detection  
With the advent of big data and the popularization of computing resources, deep learning methods, especially those utilizing Siamese networks [23], have demonstrated great potential for remote sensing change detection [24; 25]. Siamese networks process bitemporal images in parallel, allowing shared weights and consistent feature extraction, which is well suited for identifying changes between two images.  
Traditional convolutional neural network (CNN)-based methods, such as FCN [26] and U-Net [27], laid the foundation for Siamese-based architectures. U-Net, with its skip connections [28], retains high-resolution features and improves pixel-level classification accuracy in high-resolution remote sensing images. By leveraging Siamese structures, models can efficiently compare features extracted from bitemporal images and identify changes. For instance, SNUNet-CD utilizes a Siamese U-Net backbone to enhance spatial and temporal consistency during feature extraction [29].  
The integration of Transformer architectures has further improved the performance of Siamese networks in change detection tasks [30,31]. For example, [PERSON] et al. [32] proposed BIT (Bitemporal Image Transformer), which combines Transformer-based global feature modeling with Siamese networks to capture contextual information more effectively than pure CNN-based methods. Similarly, UVACD by [PERSON] et al. [33] combines a CNN backbone for semantic feature extraction with a Transformer-based module for temporal information interaction, suppressing irrelevant features and enhancing discriminative change detection capabilities.  
Hierarchical structures, such as those used in the Swin Transformer [34], have been applied to Siamese networks to improve computational efficiency and multi-scale feature extraction. [PERSON] et al. [35] proposed SwinSUNet, which employs a Siamese structure with Swin Transformer blocks to process bitemporal images in parallel, capturing global and local features at multiple scales. Additionally, [PERSON] et al. [36] introduced a fully Transformer-based Siamese network (FTN) that combines global feature extraction with pyramid-based multi-level visual feature fusion, further enhancing change detection performance.  
Overall, Siamese networks, particularly those integrating CNNs and Transformers, have become a dominant framework for remote sensing change detection, demonstrating significant improvements in accuracy and efficiency.
# Adaptive, Observer-Based Synchronization of Different Chaotic Systems
## 2 Adaptive Observer for an Output Nonlinear Parametric System
\begin{split}\dot{V}&=\dot{\mathbf{\varepsilon}}^{T} \mathbf{R}\mathbf{\varepsilon}+\mathbf{\varepsilon}^{T}\mathbf{R}\dot{\mathbf{\varepsilon}}+ \widetilde{\mathbf{\theta}}^{T}\mathbf{\Gamma}^{-1}\dot{\widetilde{\mathbf{\theta}}}=\mathbf{ \varepsilon}^{T}\mathbf{A}_{\mathbf{o}}^{T}\mathbf{R}\mathbf{\varepsilon}+\mathbf{\varepsilon}^{ T}\mathbf{R}\mathbf{A}_{\mathbf{o}}\mathbf{\varepsilon}-\widetilde{\mathbf{\theta}}^{T}\mathbf{ \Gamma}^{-1}\dot{\widetilde{\mathbf{\theta}}}=-\mathbf{\varepsilon}^{T}\mathbf{Q}\mathbf{ \varepsilon}-\widetilde{\mathbf{\theta}}^{T}\mathbf{P}\mathbf{c}e_{1}\\ &=-\mathbf{\varepsilon}^{T}\mathbf{Q}\mathbf{\varepsilon}-\mathbf{\varepsilon}_ {1}^{T}+\mathbf{c}^{T}\mathbf{\varepsilon}e_{1}=-\mathbf{\varepsilon}^{T}\mathbf{Q}\mathbf{ \varepsilon}+\frac{1}{2}\left(\mathbf{c}^{T}\mathbf{\varepsilon}\right)^{2}-\frac{1}{ 2}\mathbf{\varepsilon}_{1}^{2}-\frac{1}{2}\left(\mathbf{c}^{T}\mathbf{\varepsilon}\right) ^{2}+\mathbf{c}^{T}\mathbf{\varepsilon}e_{1}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad=-\mathbf{\varepsilon}^{ T}\left(\mathbf{Q}-\frac{1}{2}\mathbf{c}\mathbf{c}^{T}\right)\mathbf{\varepsilon}-\frac{1}{2} \mathbf{e}_{1}^{2}-\frac{1}{2}\left(\mathbf{e}_{1}-\mathbf{c}^{T}\mathbf{\varepsilon}\right) ^{2}.\end{split}  \tag{13}\]  
The standard reasoning, commonly used in adaptive control and presented in [41; 42] is applied to make conclusions from (13): The Lyapunov function derivative is negative except the set \(M=\left\{\left(\mathbf{e},\widetilde{\mathbf{\theta}}\right):\;\mathbf{e}-\mathbf{P}^{T} \widetilde{\mathbf{\theta}}=\mathbf{0},\;\mathbf{e}_{1}=0\right\}\). Therefore, we determine that \(\mathbf{\varepsilon}\rightarrow\mathbf{0}\) and \(\widetilde{\mathbf{\theta}}\) is bounded, and analyzing the set \(M\), assuming the persistency of excitation of \(\mathbf{P}\mathbf{c}^{T}\mathbf{P}^{T}\), we find that \(\mathbf{e}=\mathbf{0},\;\widetilde{\mathbf{\theta}}=\mathbf{0}\) is the only possible trajectory in \(M\); hence, \(\mathbf{e}\rightarrow\mathbf{0},\;\widetilde{\mathbf{\theta}}\rightarrow\mathbf{0}\). As a matter of fact, in the adaptive case, we consider the tracking, closed-loop system stability rather than the observer alone.
# Review Quantum Circuit Synthesis for [PERSON]'s Algorithm Oracle
## 2 Background
### [PERSON]'s Algorithm  
Based on quantum circuits and amplitude amplification, [PERSON]'s search algorithm was proposed in 1997 to search an unstructured dataset faster than any known classical algorithm [15]. The algorithm consists of three steps:  
* Superposition of the initial state.
* Oracle that marks the desired state.
* Diffusion operation that amplifies the desired state.  
The superposition step represents all the search elements as a linear combination with equal probabilities [17]. After having a superposition step, the oracle marks the desired state of the search problem, which the diffusion operator later amplifies. A measurement procedure is conducted after a series of iterations, making the probability of collapsing the superposition to our desired result higher [16]. In this case, at most, we must iterate \(\frac{\pi}{4}\sqrt{N}\) times [18], where \(N\) is the number of search terms available, to obtain a better approximation of the desired search state. The general structure of the [PERSON] algorithm is shown in Figure 2.  
The main section of the algorithm is the oracle. It is a subcircuit used in quantum computing to introduce an instance of a mathematical function into a bijective function that is then represented as a quantum circuit [19]. They represent the \"black box\" function \(f(x)\)[10]. This applies to [PERSON]'s algorithm and multiple quantum algorithms that can have oracles to represent one of their sections.  
In the case of [PERSON]'s algorithm, the oracle represents a function \(f(x)=1\) where \(x\) is the term searched those outputs \(1\) when used in the function. A quantum representation of the function is shown in Equation (8). Where operation \(U_{f}\) is the [PERSON]'s oracle, \(|s\rangle\)  
Figure 1: Layout of a fundamental quantum circuit.  
Figure 2: Basic schema of the [PERSON]’s algorithm.  
represents the current state of the qubits, and \(\omega\) is the desired state (the searched element). These representations allow inverting the phases only on the desired output state.  
\[U_{f}|s\rangle=\begin{cases}|s\rangle&s\
eq\omega\\ -|s\rangle&s=\omega\end{cases} \tag{8}\]  
The main problem with this representation is that it requires the construction of an algorithm that does the work of the oracle (Bernhardt), and the user is responsible for generating the oracle circuit. This poses multiple challenges that compromise the quadratic improvement reported by the algorithm ([PERSON], 2017), such as:  
* The oracle is not optimized.
* The oracle depends on the data input, generating it each time.
* The oracle circuit generation is not a direct task.  
Due to these problems, multiple approaches have been developed to make the oracle's logical synthesis more approachable, optimized, and automated. Three main groups of methodologies have been identified: classical synthesis, metaheuristics, and neural networks.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 2 Sinkhorn Algorithm
### Sinkhorn-Knopp Algorithm  
Iterative Bregman projections onto \(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\) converge to a point in the intersection \(\mathcal{C}_{1}\cap\mathcal{C}_{2}\)[30]. Hence, the so-called Sinkhorn-Knopp (SK) algorithm [3] that performs alternate Bregman projections can be considered to compute the regularized transport matrix:  
\[\gamma^{0}=e^{-c/\epsilon} \gamma^{\ell+1}=P_{\mathcal{C}_{2}}(P_{\mathcal{C}_{1}}(\gamma^{ \ell})),\]  
and we have \(\lim_{\ell\to+\infty}\gamma^{\ell}=P_{\mathcal{C}_{1}\cap\mathcal{C}_{2}}( \gamma^{0})=\gamma^{*}\).  
In the discrete setting, these projections correspond to diagonal scalings of the input:  
\[P_{\mathcal{C}_{1  
Another way to interpret the SK algorithm is as an alternate maximization algorithm on the dual of the regularized optimal transport problem; see [31], Remark 4.24. The dual problem of (1) is:  
\[\max_{\begin{subarray}{c}\alpha\in\mathbb{R}^{n}\\ \beta\in\mathbb{R}^{n}\end{subarray}}\ E(\alpha,\beta):=\langle\alpha,\mu_{1} \rangle+\langle\beta,\mu_{2}\rangle-\varepsilon\sum_{i,j}e^{(\alpha_{i}+\beta _{j}-c_{ij})/\varepsilon}. \tag{6}\]  
As the function \(E\) is concave, continuously differentiable, and admits a maximizer, the following alternate maximization algorithm converges to a global optimum:  
\[\alpha^{\ell+1} =\operatorname*{argmax}_{\alpha}E(\alpha,\beta^{\ell}) \tag{7}\] \[\beta^{\ell+1} =\operatorname*{argmax}_{\beta}E(\alpha^{\ell+1},\beta). \tag{8}\]  
The explicit solutions of these problems are:  
\[\alpha_{i}^{\ell+1} =\varepsilon\log\Biggl{(}\sum_{j}\exp\Big{(}\log(\mu_{1})_{i}- \left(\beta_{j}^{\ell}-c_{ij}\right)/\varepsilon\Big{)}\Biggr{)} i=1\cdots n_{1} \tag{9}\] \[\beta_{j}^{\ell+1} =\varepsilon\log\Biggl{(}\sum_{i}\exp\Big{(}\log(\mu_{2})_{j}- \left(\alpha_{i}^{\ell+1}-c_{ij}\right)/\varepsilon\Big{)}\Biggr{)} j=1\cdots n_{2} \tag{10}\]  
and we recover the SK algorithm (5) by taking \(u_{i}=e^{\alpha_{i}/\varepsilon}\), \(\
u_{j}=e^{\beta_{j}/\varepsilon}\), and \(\gamma_{i,j}^{0}=e^{-c_{ij}/\varepsilon}\).  
Efficient parallel computations can be considered [2], and one can almost reach real-time computation for large-scale problems for certain classes of cost matrices \(c\), allowing the use of separable convolutions [32]. For low values of the parameter \(\varepsilon\), numerical issues can arise, and the log stabilization version of the algorithm presented in Relations (9) and (10) is necessary [12]. Above all, the linear rate of convergence degrades as \(\varepsilon\to 0\) (see for instance Chapter 4 in [31]). In the following sections, we introduce different numerical schemes that accelerate the convergence in the regime \(\varepsilon\to 0\).
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 4 Test and Results  
Protocols are instructions that follow a strict sequence to generate good communication between entities. In a cryptographic protocol, the information travels protected using some cryptographic primitive. In this context, protocols are intended to provide confidentiality, entity authentication, information integrity, and non-repudiation. Protocols can be prone to security or semantic failures. Hence, different tools help in the formal verification of protocols. These tools verify the cryptographic properties of protocols, including their semantics and security, under certain scenarios raised in the tools. The methodology used by verification tools is specified in [37]. This methodology uses security services, the protocols designed together with the properties to be tested, and the model of the attackers. In this way, verification tools can obtain proof of the security of a protocol or the description of the attacks that can be performed on it. Therefore, it is possible to know if the protocol is well constructed or not. Figure 10 shows this methodology graphically. Some tools, in the results, provide information about the attacks that can be performed on the protocol to correct security breaches.  
The tools for protocol verification vary depending on how the verification can be performed. Among the tools considered in this work, some are automatic since they can analyze the security of the protocols and the semantics of the security properties that are verified. In addition, they provide results that can be used to improve the protocol itself, if necessary.  
As shown in Figure 10, automatic verification tools take as input the cryptographic protocol and the security properties it must comply with. Subsequently, the tool analyzes the security by taking these inputs and performing attacks. If the protocol is found insecure, the tool reviews the type of attacks performed. Otherwise, the protocol is found to be secure. A representation of this process can be seen in Figure 11. The tool used for this purpose is Scyther [19; 20].  
Figure 9: Phase 4: Transmission of MI data from entity B to D for signature verification issued by entity A.
## 3 Methodology
### Formulation of the Optimization Algorithm
#### 3.7.3 Parameters  
The load and PV scenarios are generated according to the forecast mean values and deviations. These scenarios are expressed as Equations (15) and (16), respectively. They represent the active power of load and the available PV power.  
\[L_{t,p,s}^{c},\;\forall\;s\in S,\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{15}\]  
\[L_{t,p,s}^{PV},\;\forall\;s\in S,\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{16}\]  
The battery capacity, the initial SOC, the battery efficiency and the maximum and minimum battery active power are written as \(Cap^{bat}\), \(SOC^{i}\), \(\eta^{bat}\), \(P^{maxB}\) and \(P^{minB}\), respectively. The maximum and minimum active power of each diesel unit are expressed as \(P^{maxD}\) and \(P^{maxD}\), respectively. The minimum frequency is expressed as \(f^{mn}\). The diesel generators performs a frequency control through a PI controller. To provide a power reserve for frequency regulation, a power margin of diesel generators is reserved. This power margin is denoted as \(marge_{dies}\).
# Assist-as-Needed Controller of a Rehabilitation Exoskeleton for Upper-Limb Natural Movements
## 3 Experimental Result
### Parameter Identification of Friction Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Model** & **J1** & **J2** & **J3** & **J4** & **J5** \\ \hline \multirow{8}{*}{\(R^{2}\)} & M1\_f & 0.9372 & 0.9713 & 0.9597 & 0.9892 & 0.9114 \\  & M2\_f & 0.9933 & 0.9994 & 0.9906 & 0.9968 & 0.9517 \\  & M3\_f & 0.9935 & 0.9994 & 0.9982 & 0.9967 & 0.9517 \\  & M1\_r & 0.9199 & 0.9595 & 0.9553 & 0.9827 & 0.9201 \\  & M2\_r & 0.9860 & 0.9974 & 0.9899 & 0.9981 & 0.9083 \\  & M3\_r & 0.9914 & 0.9974 & 0.9976 & 0.9991 & 0.9084 \\ \hline \multirow{8}{*}{\(RMSE\)} & M1\_f & 1.9292 & 0.9357 & 2.4775 & 0.0592 & 1.0432 \\  & M2\_f & 0.6311 & 0.1394 & 1.1752 & 0.0320 & 0.7704 \\  & M3\_f & 0.6197 & 0.1390 & 0.5211 & 0.0329 & 0.7699 \\  & M1\_r & 2.0902 & 1.1122 & 2.5684 & 0.0820 & 0.9546 \\  & M2\_r & 0.8745 & 0.2819 & 1.2224 & 0.0273 & 1.0227 \\  & M3\_r & 0.6829 & 0.2818 & 0.5945 & 0.0182 & 1.0218 \\ \hline \multirow{8}{*}{\(\sigma\) (%)} & M1\_f & 7.5385 & 4.4991 & 5.4049 & 0.7564 & 19.2490 \\  & M2\_f & 2.2334 & 0.5751 & 1.7412 & 0.3552 & 5.3526 \\ \cline{1-1}  & M3\_f & 2.1778 & 0.5681 & 1.1477 & 0.3794 & 5.2395 \\ \cline{1-1}  & M1\_r & 8.2486 & 5.4993 & 5.6672 & 1.0181 & 16.3971 \\ \cline{1-1}  & M2\_r & 2.0808 & 0.7506 & 1.8865 & 0.3089 & 6.7566 \\ \cline{1-1}  & M3\_r & 1.5845 & 0.7472 & 1.3080 & 0.2428 & 6.8118 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of Three Friction Parameter Model. demonstrating higher stability. In the J3 data, the RSD of M3 is 1.1477, which is 21.2% of that of M1 (5.4049) and 65.9% of that of M2 (1.7412), indicating superior stability.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 3 Proposed Method  
Battery degradation in LEO satellites occurs as a result of various factors, including extended operational periods, changes in internal resistance, and extreme charge-discharge conditions. During its mission in orbit, a satellite undergoes multiple charge-discharge cycles daily. These repeated cycles contribute to the degradation of battery electrode materials and a gradual reduction in capacity.  
An increase in internal resistance is one of the key indicators of battery degradation. As internal resistance increases, power losses during the charge and discharge processes become more significant and the output voltage decreases. These effects collectively reduce a battery's overall efficiency and operational lifespan, posing challenges to the corresponding satellite's performance and reliability.  
This decline in battery voltage directly impacts the satellite's systems. As the voltage decreases, the battery's available power capacity is reduced, which can hinder the normal operation of the satellite. Consequently, it is essential to employ technologies that are capable of detecting battery degradation and integrate this information into mission planning. Such measures are critical for ensuring the efficient and reliable operation of LEO satellites over their lifespans.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 3 Systems and Materials
The received echo and monitoring signals are coherently mixed with their respective LO signals, allowing the balanced photodetector (BPD) to isolate the target information by filtering out the carrier and DC components. In the direct intermediate frequency sampling structure employed, the signals collected by the BPD in Ch.1 and Ch.2 retain characteristics identical to those of the signals produced by the AWG, ensuring the continuity of phase between pulses. This continuity contrasts with the characteristics of analog-domain-deciphered signals. The analog-to-digital converter (ADC) then collects the output signals from the BPDs for subsequent error compensation and imaging. In addition to outputting the LO signal, the EVOA directs a portion to extract laser phase noise. This signal is directed through an acousto-optic frequency shifter (AOFS), introducing a fixed frequency shift \(f_{AOFS}\). Next, a 50/50 beam splitter divides the signal into two beams, which travel through fibers of different lengths, generating two phases with distinct delays. The delayed beam is coherently mixed with the LO signal and detected by the BPD. According to coherent detection principles, the resulting signal has a center frequency of \(f_{AOFS}\), which is set far enough from the fundamental frequency to minimize the influence of the 1/f noise. Reference channel 1, received by BPD3, has a longer delay fiber and is intended for coarse DPN compensation. This channel's fiber length is 20 m, with an optical path length calculated at 29.3 m, considering the refractive index of 1.466--approximately equal to the laser's coherence length. Similarly, reference channel 2, received by BPD4, has a shorter delay fiber for fine compensation, with a length of 4 m, or approximately 1/5 of the length used in reference channel 1. The ADC simultaneously samples the BPD output signals from all four channels. In the proof-of-concept experiment, we set the ADC sampling rate to 16 GS/s to ensure full signal frequency band sampling without aliasing. Although this high sampling rate may appear to increase system costs, we have shown that even in a direct intermediate frequency sampling scheme, sampling rates as low as 62.5 MS/s remain feasible [32]. Additionally, we synchronized the AWG and ADC clocks to address the potential chirp rate mismatch between the two devices.