esa-sceva/satcom-chunk-collection huggingface dataset:
# Movement Compensated Driver's Respiratory Rate Extraction
## 2 Methods
### Vital Signal Detection  
Figure 1 shows an example of a driver's respiration signal measured by the FMCW radar. Figure 1a is an image of the driver's IF signal, and the color bar shows the amplitude of the IF signal. Time is the scan time, n is the sampling time in a chirp. The section where the driver movement occurred is indicated at the top of the figure. The difference in the displacement of the signal caused by the driver's breathing forms a wave pattern, which indicates respiration. Irregular distortion of the respiration signal occurs in the movement section. However, in the other sections, we can see that the driver's respiration signal is regular. For comparison with the reference respiration, a respiration signal extracted from the radar is derived using the phase component of the signal obtained after fast Fourier transform processing of the IF signal. Phase component was derived from the radar signal of a specific range bin. The specific range refers to the distance between the radar and the driver and was extracted using the magnitude-phase coherency (MPC) [20]. The reference respiration signal was acquired by a pressure sensor worn on the abdomen. Figure 1b shows a comparison between the respiration derived from the IF signal and that from the reference sensor. Figure 1b demonstrates that if movement occurs, the radar signal is distorted compared to the reference respiration. Thus, to accurately detect the driver's breathing, it is necessary to compensate for the respiration signal in situations where the driver is not stationary.  
Figure 1: Measured respiratory signals. (**a**) Radar signal, (**b**) respiratory signal measured by radar at a specific range bin and from actual breathing.
## 2 Vehicular Networks  
This section presents DSRC-based networks, their usage and challenges, WAVE architecture, and frequency range standards for vehicular communication of countries such as Europe, Korea, China and Japan. This section also illustrates cellular networks for vehicular communication, including LTE, C-V2X, and 5G.
## 5 Modeling and Analysis in Ansys  
To perform modal analysis and obtain the natural frequencies, finite element analysis (FEA) is required. FEA is a computational technique used to find approximate solutions to a wide range of engineering problems [23]. A finite element model provides a piecewise approximation to the governing equations. The fundamental idea behind FEA is to replace a solution region with a collection of discrete elements (discretization), which can be combined in various configurations. This approach allows FEA to represent highly intricate shapes.  
ANSYS is a comprehensive engineering simulation software used for FEA, computational fluid dynamics (CFD), and other simulation-driven design tasks. It helps engineers and designers to model, analyze, and optimize products and systems in various industries such as aerospace, automotive, and electronics. ANSYS offers advanced tools for structural, thermal, electromagnetic, and fluid simulations, providing insights into a product's performance under real-world conditions. It aids in reducing physical testing, improving product efficiency, and accelerating the design process [24].  
To perform modal analysis in ANSYS, the first step is to create the geometry of the structure in ANSYS or import an existing CAD model. Once the geometry is ready, the next step is to define the material properties, including density, Young's modulus, and Poisson's ratio, which are essential for accurate simulation. After assigning the material properties, the structure needs to be meshed, dividing it into smaller elements to accurately model the physical system. Next, boundary conditions such as fixed supports or applied loads are defined to simulate real-world constraints. Once the model setup is complete, the modal analysis can be performed by selecting the \"Modal\" analysis type. ANSYS will solve for the natural frequencies and mode shapes of the structure. After the modal analysis, a random vibration analysis was also conducted in ANSYS by defining PSD input and computing the structural response. Finally, the results can be visualized and interpreted to assess the system's vibrational characteristics and identify potential resonance issues.  
A detailed explanation of these steps is provided below.
## 3 Problem Formulations  
In this section, we present the proposed optimal scheme for the transmission power of the cognitive satellite user firstly. In addition, then, the perfect sensing and imperfect sensing conditions are considered, respectively. Ergodic capacity is adopted as the metric to measure the performance of the satellite network with average interference power constraints in the paper. As mentioned above, there are four possible states, and the ergodic capacity of cognitive satellite uplink can be calculated as follows:  
\[C=E\left\{\frac{T-\tau}{T}\left[\mathcal{P}\left(\mathcal{H}_{i}\right)\left( 1-P_{f}\right)C_{ii}+\mathcal{P}\left(\mathcal{H}_{i}\right)P_{f}C_{ib}+ \mathcal{P}\left(\mathcal{H}_{b}\right)P_{d}C_{bb}+\mathcal{P}\left(\mathcal{H }_{b}\right)\left(1-P_{d}\right)C_{bi}\right]\right\} \tag{16}\]  
where \(E\{\cdot\}\) denotes the expectation. The expression Equation (16) is the objective function. To regulate the transmit power limit of the satellite user, the average transmit power is adopted as follows:  
\[E\left\{P_{s}^{(i)}\right\}\mathcal{P}\left(\mathcal{H}_{i}\right)\left(1-P_ {f}\right)+E\left\{P_{s}^{(b)}\right\}\mathcal{P}\left(\mathcal{H}_{i}\right) P_{f}+E\left\{P_{s}^{(i)}\right\}\mathcal{P}\left(\mathcal{H}_{b}\right) \left(1-P_{d}\right)+E\left\{P_{s}^{(b)}\right\}\mathcal{P}\left(\mathcal{H}_{ b}\right)P_{d}\leq P_{av} \tag{17}\]where \(P_{av}\) denotes the average transmit power limit of the satellite user. Furthermore, the average interference power constraints of the satellite user are as follows:  
\[E\left\{P_{s}^{(i)}G_{t}\left(\theta^{\prime}\right)G_{BS}L_{sp}h_{sp}\right\} \left(1-P_{d}\right)+E\left\{P_{s}^{(b)}G_{t}\left(\theta^{\prime}\right)G_{BS}L _{sp}h_{sp}\right\}P_{d}\leq I_{av}, \tag{18}\]  
where \(I_{av}\) refers to the average interference power constraint limit.  
Therefore, the sensing-based dynamic spectrum sharing method can be formulated as the following optimization problem:  
\[\begin{split}\max_{\left\{P_{s}^{(i)},P_{s}^{(b)},\tau\right\}}C,\\ s.t.\left(\ref{eq:P_s}\right)\emph{and}\left(\ref{eq:P_s}\right). \end{split} \tag{19}\]  
The expression Equation (19) is not a linear convex optimization problem, which contains two complicated constraints. Then, \(P_{d}\) and \(P_{f}\) are the \(Q\) functions and taking over the sensing slot duration \(\tau\). In addition, \(C\) is related to two random variables \(h_{ss}\) and \(h_{sp}\). Therefore, we modify (19) under the perfect sensing condition and the imperfect sensing condition, respectively.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results
### Lossless Compression
#### 6.2.1 MWC Datagrams  
As can be seen in Figure 9, FAPEC achieves the best compression ratios, except for two of the eleven scenes from EM2040. The improvement is especially notorious in EM712 and EM304 files. It is worth mentioning that higher ping rates, in general, lead to better compression ratios. This is an expected result, as a high sampling rate means a higher correlation between samples, which is exactly what FAPEC exploits.  
When compared to _gzip_, FAPEC yields ratios at least 12% better, excluding files with phase samples, reaching even 33% for some EM712 files. On average, FAPEC performs 22% better than _gzip_ and _Zstd_, which share very similar compression ratios. On the other hand, _bzip2_ ratios for EM2040 files are close to those of FAPEC, and even slightly better in two situations. In this case, FAPEC is 9% better on average. One could consider using _bzip2_, but in addition to the compression ratio, we should take into account the speed results. Furthermore, FAPEC results are outstanding. It is three times faster than _gzip_ and six times faster than _bzip2_. Regarding _Zstd_, it is twice as fast as FAPEC, but as we saw earlier, the compression ratios are rather poor. We should remark that even with the most aggressive compression levels, FAPEC yields better ratios and _Zstd_ becomes slower than _bzip2_.  
Clearly, FAPEC is the most optimized option to compress water column data. This is also observed in Figure 10, where we show a scatter plot of the process time (in seconds) and the inverse compression ratio for each file and compressor. Note that, in this case, the compression ratio has been defined as the compressed size divided by the original size; hence, the closer to the origin, the better. In order to facilitate visualization, the time has been cut at 65 s, which implies that some _gzip_ and _bzip2_ realizations are not shown in the figure.  
Figure 9: Lossless compression ratio (**left** panel) and lossless compression throughput (**right** panel) for the tested KMWCD files.
# Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images
## 4 Results
### Evaluation on Sea-Thru Dataset  
Since Sea-thru dataset [7] only provides unprocessed sensor data stored in RAW photo formats, the software of Imaging Edge Desktop and Adobe Photoshop Lightroom was used to convert the ARW files to TIF and JPG files without any post-processing. No reference images are provided in the Sea-thru dataset [7], so only the results obtained by the test methods are given in Figure 12. It can be seen that the proposed method could remove the color cast well and it generates more colorful and realistic images than [PERSON]'s method [37].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Image No.** & **Raw** & **GW** & **WP** & **SB** & **GWL\(\alpha\beta\)** & **Ours** & **Haze-Lines** \\ \hline
5450\#1 & 29.68 & 27.30 & 23.99 & 13.58 & 5.90 & 4.27 & 6.12 \\
5450\#2 & 28.72 & 21.90 & 22.20 & 7.69 & 4.72 &
# Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
## 3 Local Effect of the Adversarial Noise on the Target CNN
### Is Each Individual Patch Adversarial?  
To examine the adversarial effect of local image areas, we replace non-overlapping \(16\times 16\), \(32\times 32\), \(56\times 56\), and \(112\times 112\) patches of the ancestors with patches taken from the same location in their adversarial versions (this process is performed for BIM and for the EA separately), one patch at a time, starting from the top-left corner. Said otherwise, each step leads to a new hybrid image \(I\) that coincides with the ancestor image \(\mathcal{A}\) everywhere except for one patch taken at the same emplacement from the adversarial \(\mathcal{D}_{k}^{\textit{e}tk}(\mathcal{A})\). At each step, the hybrid image \(I\) is sent to \(\mathcal{C}_{k}\) to extract the \(c_{a}\) and \(c_{t}\)-label values: \(\partial_{I}^{\mathcal{C}_{k}}[a]\) and \(\partial_{I}^{\mathcal{C}_{k}}[t]\). Figure 1 shows an example of the plots of these successive \(c_{a}\) and \(c_{t}\)-label values, step-by-step, for the ancestor image \(\mathcal{A}_{5}^{4}\), the CNN \(\mathcal{C}_{6}\), and the adversarial images obtained by the EA and BIM. The behavior illustrated in this example is representative of what happens for all ancestors and CNNs.  
For all values of \(s\) and both attacks, almost all patches individually increase the \(c_{t}\)-label value and decrease the \(c_{a}\)-label value. The fact that the peaks often coincide between the EA and BIM proves that modifying the ancestor in some image areas rather than others can make a large difference. However, BIM's effect is usually larger than the EA's. Note also that no single patch is sufficient to fool the CNNs in the sense that it would create a hybrid image with a dominating \(c_{t}\)-label value.
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
###### Abstract  
This paper proposes support vector machine (SVM) based voice activity detection using FuzzyEn to improve detection performance under noisy conditions. The proposed voice activity detection (VAD) uses fuzzy entropy (FuzzyEn) as a feature extracted from noise-reduced speech signals to train an SVM model for speech/non-speech classification. The proposed VAD method was tested by conducting various experiments by adding real background noises of different signal-to-noise ratios (SNR) ranging from \(-10\) dB to \(10\) dB to actual speech signals collected from the TIMIT database. The analysis proves that FuzzyEn feature shows better results in discriminating noise and corrupted noisy speech. The efficacy of the SVM classifier was validated using 10-fold cross validation. Furthermore, the results obtained by the proposed method was compared with those of previous standardized VAD algorithms as well as recently developed methods. Performance comparison suggests that the proposed method is proven to be more efficient in detecting speech under various noisy environments with an accuracy of 93.29%, and the FuzzyEn feature detects speech efficiently even at low SNR levels.  
v 20161898029810.3390/e18080298 Article  
Voice activity detection; fuzzy entropy; support vector machine; _k_-NN
## 3 After Failure Strategy Approach
### Connecting Path
#### 3.1.1 Linear Programming Problem  
To easily identify any velocity components orthogonal to the direction \(d\), it is suggested to introduce a mobile reference frame placed on the end-effector with the \(x\)-axis aligned with \(d\). This frame is obtained after a sequence of three rotations* The first two rotations \(\mathbf{R_{z}(\alpha)}\) and \(\mathbf{R_{y}(\beta)}\) are applied to align the \(x\)-axis with the direction \(d\) of the desired velocity component;
* The third rotation \(\mathbf{R_{x}(\gamma)}\) is such that the undesired velocity component (i.e., the orthogonal one) is aligned with the \(y\)-axis. The overall rotation matrix \(\mathbf{R}\) is therefore defined as \[\mathbf{R}=\mathbf{R_{z}(\alpha)\mathbf{R_{y}(\beta)R_{x}(\gamma)}}\] (8) The end-effector velocity vector \(\mathbf{v^{\prime}}\) can be expressed in the mobile reference frame as: \[\mathbf{v^{\prime}}=\mathbf{R}^{T}\mathbf{v}= \begin{bmatrix}v^{\prime}_{x}\\ v^{\prime}_{y}\\ v^{\prime}_{z}\end{bmatrix} ^{T}= \begin{bmatrix}v^{\prime}_{x}\\ v^{\prime}_{y}\\ 0\end{bmatrix} ^{T}\] (9)  
As stated in (9) and represented in Figure 1(a), \(v^{\prime}_{z}\) is always null, \(v^{\prime}_{y}\) is always positive and \(v^{\prime}_{x}\) will be positive or negative depending on the initial velocity value. The desired velocity should therefore be obtained by reducing \(v^{\prime}_{y}\) to a suitable small value, while \(v^{\prime}_{z}\) is kept to a null one. This can be achieved if the exerted force \(\mathbf{\mathbf{f}^{\prime}}\) is defined in the mobile reference system such as  
\[\mathbf{\mathbf{f}^{\prime}}= \begin{bmatrix}f^{\prime}_{x}\\ f^{\prime}_{y}\\ f^{\prime}_{z}\end{bmatrix} \text{ with } \begin{cases}f^{\prime}_{x}>0\\ f^{\prime}_{y}<0\\ f^{\prime}_{z}=0\end{cases}  \tag{10}\]  
as represented in Figure 1(b). The exerted force \(\mathbf{\mathbf{f}^{\prime}}\) can be defined in terms of \(\mathbf{\tau_{w}}\) to obtain the required value to change the velocity direction  
\[\mathbf{\mathbf{f}^{\prime}}=\mathbf{R}^{T}\mathbf{f}=\mathbf{R}^{T}\mathbf{W}\mathbf{ \tau_{w}}=\mathbf{W_{R}}\mathbf{\tau_{w}}= \begin{bmatrix}\mathbf{w}^{T}_{Rx}\\ \mathbf{w}^{T}_{Ry}\\ \mathbf{w}^{T}_{Rx}\end{bmatrix} \mathbf{\tau_{w}} \tag{11}\]  
Figure 2: 3d view of the velocity components (**a**) in the mobile reference frame and forces needed to change the velocity direction in the \(x^{\prime}-y^{\prime}\) plane (**b**). The novelty of the proposed approach consists of introducing constraints on the exertable actuator torques.
## 2 Materials and Methods
### In Vivo Antifungal Activity of Propolis Extracts Against Stemphylum vesicularium  
_S. vesicularium_ was cultured in PDA medium and incubated for 7 to 10 days at 25 \({}^{\circ}\)C under fluorescent light (12 h day length). Subsequently, spores were harvested in 10 mL of distilled water supplemented with Tween 80 (0.01%, v/v) and the concentration was adjusted with distilled water supplemented with Tween 80 (0.01%, v/v) to 10\({}^{4}\) spores/mL, using a Neubauer's chamber. To evaluate the in vivo antifungal activity of propolis, \"Rocha\" pears cultivated in Alcobaca (western region of Portugal) and harvested at the stage of commercial maturity, were surface disinfected with 0.5% (v/v) sodium hypochlorite, rinsed with tap water for 5 min and air-dried. Fruits were artificially wounded twice (each wound 15 mm distant from the equatorial region) to a depth of 4 mm and a diameter of 4 mm [28].  
Subsequently, for the curative assay 40 wounds were inoculated with 20 \(\upmu\)L of the spore suspension previously described. After one hour, 20 \(\upmu\)L of two different concentrations of propolis extract E4 (30.0 mg/mL or 6.0 mg/mL) were applied to each of the wounds. As E4 was diluted in ethanol (70%, v/v), ethanol (70%, v/v) was used as control. In the prophylactic assay, 40 wounds were treated with 20 \(\upmu\)L of E4 (30.0 mg/mL) or 20 \(\upmu\)L of 70% ethanol (control assay) one-hour prior the inoculation with 20 \(\upmu\)L of the spore suspension. In both assays (curative and prophylactic) the pears were incubated in boxes, protected with plastic bags, at 20-22 \({}^{\circ}\)C and 70-80% relative humidity (RH) for 14 days. The number of infected wounds and the diameter of the lesions were measured daily. The wound diameter was calculated as the average of two perpendicular diameter measurements with a transparent ruler (\(\pm\)0.5 mm). The antifungal capacity was assessed by the percentage of infected wounds (disease incidence) and by the reduction of lesion diameter over time (treatment efficacy), relative to the control assay. The assays were conducted in a completely randomized design and repeated twice.  
In order to evaluate the potential cytotoxic effect of propolis extract on pear pulp an assay in which the wounds were treated only with undiluted propolis extract was also performed. In this assay the pears were also kept at 20-22 \({}^{\circ}\)C and 70-80% RH for 14 days.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 1 Introduction
A contribution of this paper is the presentation and discussion of all the procedures required to perform communications among a swarm of AUVs. A special focus is given to physical layer optimization. In addition to that, we propose a method for a smart use of the time-frequency resources with self-adaptation based on a limited knowledge of the environment for an orthogonal frequency-division multiplexing (OFDM) system (part of this work has been presented at the Underwater Acoustics Conference and Exhibition (UACE 2021)). In this new approach, the number of subcarriers and the cyclic prefix length are optimized. Moreover, we provide simulation results for showing the performance gain obtained in terms of BER with our method. This work is part of COMET and NEMOSENS projects, which is supervised by RTSYS company and is funded by Region Bretagne and BPI France. The aim is to develop swarms of AUVs for distinct oceanographic missions. Our focus is to improve the communication among these vehicles, depending on the network topology. In such network, with no central hierarchy, each node is able to self-adapt its transmission parameters. Knowing that the AUVs location and speed is periodically broadcasted, we can take advantage of this information. This channel geometry data can be used for adapting some transmission parameters, such as the number of subcarriers, cyclic prefix length, as well the guard interval. In this network, each AUV is going to adapt its parameters in order to improve the overall system performance. This paper is organized as follows. We start the paper by presenting the underwater vehicles to be used in a swarm in Section 2. In Section 3, we show the benefits that the usage of underwater acoustic communication brings to a swarm of AUVs. Section 4 presents the developed channel access method and the frame structure. The aspects regarding the physical layer are addressed in Section 5. This section contains the modem description, proposes a method for adapting some physical layer parameters, and provides simulation results. At the end, Section 6 shows our conclusions.
## 5 Results and Discussion  
### Impact Force  
Figure 14 depicts a comparison of impact force-time (\(F\)-\(t\)) curves on the soil of group A between the depicted results from the FEA and the measured results from the test under different impact heights (\(H\)). Furthermore, a comparison of peak impact forces (\(F_{\text{max}}\)) under different values of \(H\) among the depicted results from the FEA, measured results from the test (EXP), and theoretical results (THE) deduced from the above theoretical Equation (7) is depicted in Figure 15 and Table 5. It can be found that a generally good agreement was achieved among the results from the FEA, test, and theoretical calculation, which shows that the developed FEA model has a reasonable precision for predicting the impact force on the soil under impact loads and, moreover, the theoretical Equation (7) is capable of calculating the \(F_{\text{max}}\). Furthermore, the \(F_{\text{max}}\) on the soil was proportional to the impact height. From Table 5, we can also know that the average values and standard deviations of \(F_{\text{max,FE}}\)/\(F_{\text{max,EXP}}\) and \(F_{\text{max,THE}}\)/\(F_{\text{max,EXP}}\) were 0.954 and 4.61%, and 0.917 and 8.33%, respectively. Nevertheless, there were still some differences among the results, and these may have been induced by the theoretical formula based on the Hertz collision theory, which assumes that collisions are elastic. Furthermore,  
Figure 13: Schematic diagram of the three-fold line pipeline model.  
the influence of the friction force between the drop hammer and guide rail, and the soil compaction in the test were not considered in the FEA model and theoretical formula.  
Figure 14: Comparisons of impact force–time (\(F\)–\(t\)) curves between the predicted results in FEA and the measured results in the test (EXP) under different impact heights (\(H\)): (**a**) \(H\) = 0.5 m; (**b**) \(H\) = 1.0 m; (**c**) \(H\) = 1.5 m; (**d**) \(H\) = 2.0 m.  
Figure 15: Comparisons of the peak impact force (\(F_{\max}\)) among the predicted results in FEA, the measured results in the test (EXP), and the theoretical results (THE) under different impact heights (\(H\)).