esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 3 Efficient Dynamic Engineering Implementation Architecture for MIMO Radar Systems
### Engineering Implementation Process
#### 3.2.2 Efficient Dynamic Data Frame Storage Management  
In the engineering implementation process outlined in Section 3.2.1, an efficient and dynamic data frame storage management mechanism is employed to effectively manage the data interchange between the memory and the algorithmic process. The angle estimation step is initiated after target detection, and it is crucial to note that the angle estimation algorithm requires processing only the data pertinent to a specific target rather than the entire data frame. This indicates that the data requirements for the angle estimation algorithm are both delayed and selective. Consequently, rather than storing and processing all data immediately following the Doppler FFT, the system is designed to selectively extract and process data based on the outcomes of the target detection, thus optimizing both storage and processing resources. As shown in Figure 5, in the engineering implementation process, we divide the state of the system after the Range FFT into two types:  
When the system is in state I, the data frame storage management module retrieves and outputs radar data across the whole range dimension. This subset of data subsequently forms the RDM after Doppler FFT processing, fulfilling the data requirements for the target detection algorithm.  
Conversely, when the system is in state II, the data frame storage management module retrieves data according to the target distance dimension information provided by the target detection algorithm and outputs it. This subset of data forms a Doppler data set after Doppler FFT processing. Following the data reordering process of the antenna array, this data set meets the requirements of the angle estimation algorithm.  
Consequently, integrating a state judgment mechanism within the engineering implementation process, in conjunction with a real-time Doppler FFT module, facilitates the fulfillment of data requirements for both target detection and angle estimation in a time-shared approach. Thus, by implementing the state judgment mechanism in the engineering process and pairing it with the real-time Doppler FFT module, we can efficiently meet the data needs for target detection and angle estimation in a time-efficient manner. This strategy significantly optimizes the storage demands for data sets following Doppler FFT processing.
## 5 Simulation Results
### Comparison of the Proposed IT2 FS with Another Constant-Swithching Frequency Technique  
In this study, two different methods, SMC and IT2 FS, were presented. We compared the proposed sliding surface to various other surfaces in the literature and demonstrated its reliability. Similarly, to demonstrate the robustness of the IT2 FS, this subsection compares it to the flip-flop approach described in the literature for obtaining a constant switching frequency. Reference [26] obtained a constant \(f_{S}\) by taking into account the components that cause the hysteresis band to vary. As shown in Figure 16, the approach presented in [26] has been constructed for this study. It is obvious that the load resistance value is also required for an adaptive band in this methodology. However, it is not feasible to measure the load resistance directly in practical applications. Hence, this technique is unsuitable for boost converters. Additionally, this strategy calls for the employment of more sensors due to its reliance on several inputs. On the other hand, the proposed IT2 FS is unaffected by any of the issues stated above.  
Figure 14: Dynamic response of SMC strategy in output voltage under reference voltage variations.  
Figure 15: Dynamic response of switching frequency under reference voltage variations.
# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 3 Proposed Methodology
### Siamese Evaluation Network  
The calligraphy evaluation network is built upon the Siamese network architecture [32], which incorporates an enhanced VGG16 [33] as the backbone feature extraction network. The feature maps extracted by VGG16 are then input into a Transformer structure to capture inter-relationships, thereby enhancing the model's generalization capacity. The Siamese network comprises two input channels, with identical weights and parameters across all structures within these channels. After extracting features from two input samples, two feature vectors are obtained for each sample. The similarity between the two samples is evaluated by measuring the distance between their respective feature vectors. The Siamese network can be employed to quantify the dissimilarity between two Chinese characters, making it a suitable choice as the foundational network framework for the evaluation model.
## 3 Results and Discussion
### Wettability  
Wettability is an important parameter for adsorption materials, which is generally characterized by the water contact angle (WCA). A water droplet of about 2 \(\upmu\)L was dropped on the surface of the sample to be measured. If the WCA is larger than 90\({}^{\circ}\), as shown in Figure 5a, it means the sample is hydrophobic. In contrast, an oil droplet of about 2 \(\upmu\)L also dropped on the surface of the sample, as shown in Figure 5b, shows that the oil is absorbed and the oil contact angle (OCA) is about 12\({}^{\circ}\). In this study, as shown in Figure 5c, the WCAs of F1, F2, F3, F4 and F5 were127.0 \(\pm\) 1.2\({}^{\circ}\), 130.7 \(\pm\) 2.1\({}^{\circ}\), 132.1 \(\pm\) 2.6\({}^{\circ}\), 133.9 \(\pm\) 3.1\({}^{\circ}\), and 138.9 \(\pm\) 3.4\({}^{\circ}\), respectively. With a decreasing fiber diameter, the WCA increased slightly. The OCAs of the samples were 16.2 \(\pm\) 0.4\({}^{\circ}\), 16.0 \(\pm\) 0.3\({}^{\circ}\), 15.4 \(\pm\) 0.3\({}^{\circ}\), 15.2 \(\pm\) 0.3\({}^{\circ}\), and 12.7 \(\pm\) 0.2\({}^{\circ}\), respectively. Figure 5a shows the image of the WCA of the fiber membrane prepared by a 21 G needle, and Figure 5b presents the OCA of the same sample. A similar measurement was done for the absorption time of an oil drop on the porous fiber. The results are shown in Figure 6. One can see that the tendency of absorption time was similar to that of the oil contact angle. The wettability of a solid surface is determined by the chemical nature and roughness of the solid surface. When a large number of hydrophobic groups, such as ester groups, are present on the solid surface, the surface of the material is hydrophobic. If the surface of the substance is hydrophobic, it can be easily changed from hydrophobic to super hydrophobic by increasing the roughness of the substance; thus, the contact angle becomes larger. In this work, since PLA is a hydrophobic polymer, when we decreased the inner diameter of the needle, the water contact angle increased. In addition, it was found that the pore volume also increased significantly. Since the pore volume increased with a decreasing needle diameter, the oil contact angle decreased due to the increasing absorption capacity.  
Figure 4: A schematic diagram of pores formed in fibers.
## 3 Design of High-Gain Observer-Based Sliding-Mode Dynamic Surface Controller
### Designs of High-Gain Observer
\]  
From \(G_{10}(s)\), \(G_{20}(s)\), and \(G_{30}(s)\), we can see the following:  
\[A(G_{10}(j\omega))=\left|\frac{1}{-\omega^{3}j-h_{1}\omega^{2}+h_{2}\omega j +h_{3}}\right|=\frac{1}{\sqrt{\left(h_{3}-h_{1}\omega^{2}\right)^{2}+\left(h_ {2}\omega-\omega^{3}\right)^{2}}}\]  
\[A(G_{20}(j\omega))=\left|\frac{\omega j+h_{1}}{-\omega^{3}j-h_{1}\omega^{2}+h_ {2}\omega j+h_{3}}\right|=\sqrt{\frac{\omega^{2}+{h_{1}}^{2}}{\left(h_{3}-h_{ 1}\omega^{2}\right)^{2}+\left(h_{2}\omega-\omega^{3}\right)^{2}}}\]  
\[A(G_{20}(j\omega))=\left|\frac{-\omega^{2}+h_{1}\omega j+h_{2}}{-\omega^{3}j-h _{1}\omega^{2}+h_{2}\omega j+h_{3}}\right|=\sqrt{\frac{\left(h_{2}-\omega^{2} \right)^{2}+{h_{1}}^{2}\omega^{2}}{\left(h_{3}-h_{1}\omega^{2}\right)^{2}+ \left(h_{2}\omega-\omega^{3}\right)^{2}}}\]  
To make \(\widetilde{x}_{1}\to 0\), \(\widetilde{x}_{2}\to 0\), \(\widetilde{x}_{3}\to 0\), \(h_{3}\gg h_{2}\gg h_{1}\gg 1\) must be guaranteed. We let  
\[h_{1}=\frac{\alpha_{1}}{\varepsilon},h_{2}=\frac{\alpha_{2}}{\varepsilon^{2}},h _{3}=\frac{\alpha_{3}}{\varepsilon^{3}} \tag{21}\]  
where \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are real numbers, \(\varepsilon\ll 1\). Therefore, the observer expression can be written as follows:  
\[\left\{ \begin{array}{l}\dot{\hat{x}}_{1}=\hat{x}_{2}+\frac{\alpha_{1}}{\varepsilon }(y-\hat{x}_{1})\\ \dot{\hat{x}}_{2}=\hat{x}_{3}-\frac{\alpha_{2}}{\varepsilon^{2}}(y-\hat{x}_{1} )\\ \dot{\hat{x}}_{3}=\hat{F}(\hat{x},u)-\frac{\alpha_{3}}{\varepsilon^{3}}(y-\hat{x }_{1})\end{array} \right. \tag{22}\]  
If we put Equation (21) into \(G_{0}(s)=\left[ \begin{array}{c}G_{10}(s)\\ G_{20}(s)\\ G_{30}(s)\end{array} \right]\)  
\[G_{0}(s)=\frac{\varepsilon}{\left(\varepsilon s\right)^{3}+\alpha_{1}( \varepsilon s)^{2}+\alpha_{2}\varepsilon s+\alpha_{3}}\left[ \begin{array}{c }\varepsilon^{2}\\ \varepsilon^{2}s+\alpha_{1}\varepsilon\\ (\varepsilon s)^{2}+\alpha_{1}\varepsilon s+\alpha_{2}\end{array} \right]\]  
where \(\lim\limits_{\varepsilon\rightarrow\infty}G_{0}(s)=0\). Obviously, modeling uncertainty can be overcome by high gain.
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
###### Abstract  
Detecting audio deepfakes is crucial to ensure authenticity and security, especially in contexts where audio veracity can have critical implications, such as in the legal, security or human rights domains. Various elements, such as complex acoustic backgrounds, enhance the realism of deepfakes; however, their effect on the processes of creation and detection of deepfakes remains under-explored. This study systematically analyses how factors such as the acoustic environment, user type and signal-to-noise ratio influence the quality and detectability of deepfakes. For this study, we use the _WELIVE_ dataset, which contains audio recordings of 14 female victims of gender-based violence in real and uncontrolled environments. The results indicate that the complexity of the acoustic scene affects both the generation and detection of deepfakes: classifiers, particularly the linear SVM, are more effective in complex acoustic environments, suggesting that simpler acoustic environments may facilitate the generation of more realistic deepfakes and, in turn, make it more difficult for classifiers to detect them. These findings underscore the need to develop adaptive models capable of handling diverse acoustic environments, thus improving detection reliability in dynamic and real-world contexts.  
audio deepfake; generation; detection; acoustic context Article
## 2 Architectural Framework of IBSN and Its Components
### Power Scavenging and Energy Efficiency  
Power source is one of the key elements of IBSN. It often dominates the size and lifetime of the sensor nodes. Thus far, battery remains the main source of energy for the medical sensor nodes. In parallel to battery source, power scavenging can be used to prolong the lifetime of the sensor and to enable long-term monitoring of the patient. A number of power scavenging sources have currently been proposed, which include motion, vibration, air-flow, temperature difference, ambient electromagnetic fields, light, and infra-red radiation. For instance, [PERSON] et al. developed a vibration-based generator designed for implantable devices, which is capable of delivering 2 uJ/cycle [17]. Similar vibration-based thin film piezoelectric energy scavenging systems were proposed by [PERSON] et al. [18]. A thermoelectric effect energy-harvester called Micropelt was reported by [PERSON] et al. [19], which can produce up to 0.6 mW on an area of 5 mm\({}^{2}\). This is a promising option to be used in medical implants for a long-term operation. The power scavenging is an intermittent source of energy. A battery should be used to store the minimal energy needed for an emergency communication, apart from the routine communication. All other components of IBSN architecture will depend on the power scavenging. Energy-efficiency should be guaranteed by algorithms which will adapt the power usage of all components of IBSN without compromising the prime objective of the medical systems.
## 4 Result and Discussion
### Prediction Performance Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**Comprehensive Indicator Y**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Measured Value**} & **Predicted Value** & **Relative Error** \\ \hline
191 & 0.59 & 0.593268406 & 0.55\% \\
192 & 1.05 & 1.188406557 & 14.54\% \\
193 & 0.84 & 0.741827053 & 11.61\% \\
194 & 1.01 & 0.960578817 & 4.89\% \\
195 & 0.5 & 0.59110598 & 18.23\% \\
196 & 0.97 & 0.959367536 & 1.03\% \\
197 & 1.17 & 0.849890528 & 27.35\% \\
198 & 0.93 & 1.177363619 & 25.80\% \\
199 & 0.54 & 0.652859941 & 20.37\% \\
200 & 0.74 & 0.650975516 & 12.16\% \\ \hline \multicolumn{4}{c}{Average Relative Error \%} & \multicolumn{4}{c}{13.653\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction Results Table. Figure 8: Prediction results table. The table reveals that the relative error of the FA-PSOBP prediction model is relatively small. To provide a more intuitive comparison of model performance, when compared to the unoptimized BP prediction model, the FA-PSOBP prediction model demonstrates better fitting between its predicted values and the target values of the training set, exhibiting higher prediction accuracy. This proves the effectiveness of the FA-PSOBP prediction model in predicting acoustic emission time series and provides a certain theoretical basis for accurate predictions. From Table 6, it can be observed that the FA-PSOBP model exhibits higher accuracy compared to other machine learning methods.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 6 Directions for Future Research
### Sub-Canopy Operations  
Another idea raised during the paper was the idea of sub-canopy operations. These have been trialled on a limited scale, but they do not appear to be addressed in the literature. While many operations would not make sense to do under the canopy, basic technologies can be applied to facilitate sub-canopy operations. These are easier from a risk mitigation point of view: If you can close off the forest environment (e.g., a specific stand within a forest estate), then there is no risk to people, and being sub-canopy means there is no risk to aircraft. An automated flight sub-canopy would inevitably take longer, but that needs to be weighed up against the substantial cost of doing BVLOS above the canopy. Future research that examines the viability of sub-canopy operations to collect scientific data would be useful, especially if a comparison can be made with operations above the canopy. That would allow for meaningful comparison and the potential to identify logical applications of this type of operation.
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 1 Introduction
### Review and Further Discussion of BEMD  
#### 2.1.1 The BEMD Algorithm  
The main concept of BEMD is to extract a series of IMFs from multiple signals. This method assumes that multiple signals can be described as the sum of a fast rotation component superimposed on a slower rotation component. Each of the IMFs must satisfy the following conditions:  
1. The number of extrema and the number of zero crossings must be equal, or differ at most by one.
2. The mean value of the envelope at any point defined by the local maximum points and the envelope defined by the local minima must be zero.  
Moreover, the BEMD process requires projecting a multiple signal on a set of directions and then applying the sifting process of standard EMD to the projected components. This gives a bivariate signal, \(s(t)=x(t)+iy(t)\), and a set of projection directions, \(\varphi_{m}=2m\pi/N,1\leq m\leq N\). The fundamental process of BEMD can be depicted as follows:  
1. Project the signal \(s(t)\) on directions \(\varphi_{m}\): \[p_{\varphi_{m}}(t)=\text{Re}[e^{-i\varphi_{m}}s(t)],\] (1)
2. Extract all partial maximum points of \(p_{\varphi_{m}}(t)\):\(\{(t_{j}^{m},p_{j}^{m})\}\), where \(j\) indicates the number of individual partial maxima.
3. Interpolate the set of points \(\{(t_{j}^{m},e^{i\varphi_{m}}p_{j}^{m})\}\) by cubic spline interpolation to obtain the partial envelope curve in direction \(\varphi_{m}\) named \(e_{\varphi_{m}}(t)\).
4. Repeat steps 1-3 until the envelope curves in all \(N\) projections are obtained.
5. Compute the mean of all envelope curves: \[\overline{m}(t)=\frac{1}{N}\sum_{r=1}^{N}e_{r}(t),\] (2)
6. Subtract the mean \(\overline{m}(t)\) from \(s(t)\) to obtain \(g(t)\): \[g(t)=s(t)-\overline{m}(t),\] (3)
7. Test if \(g(t)\) is an IMF, if not, replace \(s(t)\) with \(g(t)\) and repeat the procedure from Step 1 until \(g(t)\) is an IMF. If yes, record the obtained IMF and remove it from \(g(t)\), i.e., \(c_{1}(t)=g(t)\), \(r_{1}(t)=s(t)-c_{1}(t)\)Consider \(r_{1}(t)\) as the original signal and repeat the above procedure until the second IMF, \(c_{2}(t)\), is obtained, and the residual component \(r_{2}(t)=r_{1}(t)-c_{2}(t)\).
9. Iterate the previous process until all IMFs are obtained. Referring to the sifting process of BEMD, the term \(s(t)\) can be expressed by the procedure: \[s(t)=\sum_{k=1}^{n}h_{k}(t)+r_{n}(t),\] (4) where \(h_{k}(t)\) denotes the \(k\)th complex-valued IMF and \(r_{n}(t)\) denotes a non-zero mean low-degree polynomial residue. Generally, the residue is mostly recognized as the vibration trend of the signal.
## 4 Stability  
The stability of Equation (20) is given in this section. The EGESO stability is already presented in Equations (9)-(11), which proves that error dynamics of original and estimated states are going to zero, and it remains in bounds of \(\mu\).  
**Proposition 2**.: _The ultimate boundedness of the sliding surface \(\sigma_{m}\) is found by defining the following Lyapunov function._  
Proof.: \[V_{s}(\sigma_{m})=\frac{1}{2}\sigma_{m}^{2}\] (25)  
By differentiating Equation (25) it leads to,  
\[\dot{V}_{s}(\sigma_{m})=\sigma_{m}\hat{\sigma}_{m} \tag{26}\]  
From the dynamics of \(\sigma_{m}\) in Equation (23), we get  
\[\dot{V}_{s}(\sigma_{m})= \sigma_{m}\Bigl{(}-k_{s}\sigma_{m}+d-\tilde{d}\Bigr{)} \tag{27}\] \[= \sigma_{m}\bigl{(}-k_{s}\sigma_{m}+\tilde{d}\bigr{)}\] \[\dot{V}_{s}(\sigma_{m})\leq-|\sigma_{m}|\left(k_{s}|\sigma_{m}|-| \tilde{d}|\right) \tag{28}\]  
Using the bounds of \(\tilde{d}_{m}\) found from GESO estimation, we obtain that  
\[\dot{V}_{s}(\sigma_{m})\leq-|\sigma_{m}|(k_{s}|\sigma_{m}|-\mu) \tag{29}\]From Equation (29), for stability we must \(K_{s}|\sigma_{m}|>|\mu|\). Thus, sliding surface is ultimately bounded by the bounds given by  
\[|\sigma_{m}|\leq\frac{\mu}{k_{s}} \tag{30}\]  
From Equation (30) it is noted that the bounds of \(\sigma_{m}\) may have any value and it depends on the value of \(k_{s}\) and therefore designer has to choose the value of \(k_{s}\) as per performance requirement; this proves the asymptotic stability of an entire system.
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 4 Results & Discuss
### Discuss  
To better evaluate the performance of different methods, uniformly set the same initializations, e.g., voltage & current of the PV cell at MPP to 35.5 V & 7.4 A, the same sampling period, etc., Figure 4 also shows the comparison for prediction error and control accuracy between the proposed W-IFL and some MPPT methods.  
Specifically, based on the heuristic algorithms, [PERSON] et al. [40] provided a new network structure of BP networks to optimize MPP oscillation. [PERSON] et al. [34] designed fuzzy rules to achieve good MPP control. Moreover, [PERSON] et al. [44] and [PERSON] et al. [45] proposed new algorithms to optimize the traditional P&O and IC MPP control models, respectively.  
As illustrated in Figure 4, the error curve of the proposed wavelet predictor is smoother and faster and reaches the convergence at around the 54 th iteration with oscillation reduction by 50%. Precisely, the subjective selection of training parameters for the existing BP network may lead to relatively poor predicted results, such as converged at the  
Figure 4: The comparison between different MPPT methods: (**a**) the prediction error of the iteration between two ANN models; (**b**) the power output of different MPP control models.  
Figure 3: The training curve of the wavelet predictor.  
167 th iteration with an average error of \(-7\) dB. Hence, the proposed predictor has about 85.7% and 67.7% improvements in terms of efficiency and error, respectively.  
Figure 4b then demonstrates that the proposed IFL controller can catch the MPP at around 0.05 s, and the others are 0.11 s, 0.1 s, and 0.13 s, respectively. The disturbance of power output is also stable, which is quite approximate to the actual MPP, i.e., \(35.5\;\mathrm{V}\times 7.4\;\mathrm{A}=262.7\;\mathrm{W}\). This indicates that the improved controller has significantly optimized the tracking speed and accuracy, which is consistent with the trend of higher predicted accuracy. In addition, extracting the output power of other methods can get an average of 98.4 W. In other words, compared to other control models, the IFL controller has, on average, 55.3% and 62.3% improvements in controlling capture time and output power, respectively.  
In general, this evaluation shows that the proposed MPP control model, namely W-IFL, has the advantages of shorter transition process time, faster response, no steady-state error, and no overshoot.
## 1 Introduction
### Contribution  
This study presents a system for predicting electricity prices in the Turkish day-ahead market and facilitating the participation of a facility composed of multiple second-life batteries in the electricity market. The contributions of this study to the literature are listed below:  
* The optimum trading model is presented for the participation of a facility established by purchasing old EV batteries as SLBs, and instead of recycling them, using them in the day-ahead electricity market.
* The next 24 h electricity prices in the day-ahead electricity market are predicted using the N-HiTS deep learning model. Additionally, price predictions are compared with five other common or trending methods in the literature (Naive Seasonal Prediction, RNNs, LSTM, GRU, N-BEATS).
* Various case studies are conducted using real prices from the day-ahead electricity market in Turkiye. This study can serve as a pathfinder for researchers and system developers exploring the arbitrage opportunities of SLBs in Turkiye and other countries.  
It should be underlined that, to the best of the authors' knowledge, this is the first proposed system in the literature where SLBs are directly taken into account for arbitrage in the day-ahead market and where electricity prices are predicted via deep learning methods using real prices from Turkiye.