esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### MAA Correlation with the Environmental Conditions  
According to Equation (1) [18], the MAA depends on the CO\({}_{2}\) decay step by step within the environment, so it also depends on the environmental conditions. Firstly, the correlation between CO\({}_{2}\) concentration and environmental condition and then the one with MAA will be discussed. For the clarity of discussion, the mean values calculated for each experimental campaign divided for case studies and experimental configurations are reported in Table 4.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 1 Introduction
### Underwater Glider Path Planning
Further strategies to address underwater glider path planning (UGPP) under complex scenarios (static and dynamic obstacles) are discussed in ([PERSON] et al., 2016), as the classical path planning might not be adequate. Those strategies include pointing the vehicle to the destination and make small adjustments during the mission or in adverse zones position the vehicle across the current, in order to reach a place with weaker and/or more favourable currents. The same work suggests some metrics to evaluate performance, in order to determine how well defined the path-planning arrays are, considering the mission objectives. The work presented by ([PERSON] et al., 2016) concluded that adequate path planning contributes to a substantial energy saving set of solutions compared to straight line trajectories, in cases when ocean currents and vehicle's speed are comparable. The authors also point out that a straight line trajectory from start to end can only be an optimum path when the currents' velocity does not exceed half of the vehicle's velocity. Energy and time saved with path planned missions can be used to either increase the distance travelled and/or add more sensors. A stochastic optimization methodology was carried out by [PERSON] et al. (2016), focused on obtaining the best paths from the viewpoint of energy-optimal missions. They also concluded that vehicles that can change their displacement speeds can reach the programmable locations by using favourable currents, saving energy for crossing areas with unfavourable conditions. UGPP challenge has also been tackled using evolutionary techniques, like in [PERSON] et al. works (2016, 2017), including eddy sampling applications ([PERSON] et al., 2017).
## 3 Materials and Methods
### Training and Test Sets to Evaluate the Prediction Ability of the PLS2-CM Model  
The evaluation of prediction capability can be done through a test set independent of the training set or by cross-validation (CV), also named k-fold procedure, that includes leave-one-out cross-validation. Despite its popularity, CV use has been criticized [53], and it has recently been formally proved that it is not a correct procedure to estimate prediction capability [54]. It has therefore been decided to divide dataset \(\mathbf{X}\) into two subsets: \(\mathbf{X}_{TR}\) for training and \(\mathbf{X}_{TS}\), the test set, for the evaluation of predictive capability. The Kennard-Stone algorithm [55], the most relevant for this task, was used by means of 'kennardstone' function of PLStoolbox [56]. The method initially selects the pair of sEMG signals with the largest distance and ranked as most representative. Then, in each following step, the remaining samples with the greatest distance from the already selected samples are chosen and added to the bottom of the previous rank list. This procedure is repeated until a predefined number of gestures had been chosen and ranked in each class. The method assures a uniform distribution of the sEMG signals selected, and it also includes boundary gestures of the class. Remaining gestures are assigned to test subset. In each class, 70% has been assigned for training and 30% for test subset. Resultantly, training matrix \(\mathbf{X}_{TR}\) has a size of 56 \(\times\) 4860 and 24 \(\times\) 4860 for prediction \(\mathbf{X}_{TS}\).
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Experimental Set-Up
#### 2.5.1 Frequency Band Considerations  
Acoustic underwater communication influences the maritime habitat, hence system and acoustic experiment designs have to minimize the interruption of natural communication [52] and ideally avoid mimicking animal calls (compare [53]). Fish and sharks have no hearing sensitivity for frequencies far above 10 kHz [15; 52]. Sea mammals, e.g., dolphins, seals, and whales on the other hand are highly sensitive to frequencies of up to 150 kHz [54; 55; 56; 57; 58]. The general structure of sea mammal's sound creation and hearing is known from anatomic considerations and follows similar mechanisms, with some whales having acoustic matching melons in their frontal part of the head that also serve as an acoustic bandpass and lens [57; 59]. From a behavioral point of view, seals and dolphins are highly relevant, as they are well researched and commonly found near harbors and shores. As a simplified design rule, we regard that seals' hearing is sensitive to sound frequencies below 80 kHz, with already increased sound pressure level thresholds, i.e., decreased hearing sensitivity above 60 kHz [60]. For dolphins, this hearing threshold is approximately 200 kHz, with decreased sensitivities above 150 kHz [54; 55; 56; 57]. For all practical purposes, it has to be assumed that every transmission will be audible to sea mammals in the vicinity and can cause potential harm or changes in behavior. Additionally, the attenuation of acoustic underwater waves exceeds 20 dB km\({}^{-1}\) for those frequencies, limiting the spatial sphere of influence. A limiting factor for coastal applications is natural and artificial noise, e.g., from the surf and ship traffic, which we regard as Brownian noise decaying at about 18 dB per decade [16; 61]. For our system, we limit the communication band therefore as in Table 1.
## 4 Conclusions  
This study provides a simulation study of transfer I-V characteristics, gate leakage current, and saturation current characteristics for MIS-HEMT structures by considering SHE. The gate leakage current increased with increasing temperature, but the MIS-HEMT gate leakage current was significantly lower than the basic AlGaN/GaN-based HEMT, even when temperature was increased by applying the gate dielectric layer. The gate dielectric layer, consisting of Si\({}_{3}\)N\({}_{4}\) and Al\({}_{2}\)O\({}_{3}\), reduced the gate leakage current more than a single Si\({}_{3}\)N\({}_{4}\) gate dielectric layer did. However, the threshold voltage, gate leakage current, and breakdown voltage all changed depending on the dielectric layer composition and thickness. The MIS-HEMT gate leakage current and breakdown voltage with Al\({}_{2}\)O\({}_{3}\) on Si\({}_{3}\)N\({}_{4}\) were significantly improved compared with the device incorporating only Si\({}_{3}\)N\({}_{4}\). However, the saturation current characteristics became too unstable to operate as a normally-on device for thicker Al\({}_{2}\)O\({}_{3}\) layers.  
Figure 12: Saturation current characteristics for MIS-HEMT with and without the self-heating effect (SHE) as a normally-on device: (a) sample A, (b) sample B, and (c) sample C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{\(\Delta\)**\(\mathbf{\Omega_{D}}\) (mA/mm) (at \(\mathbf{V_{DS}}\) = 40 V)**} \\ \cline{2-6}
**Type** & \(\mathbf{V_{GS}}\) = \(-\)2.0 V & \(\mathbf{V_{GS}}\) = \(-\)1.5 V & \(\mathbf{V_{GS}}\) = \(-\)1.0 V & \(\mathbf{V_{GS}}\) = \(-\)0.5 V & \(\mathbf{V_{GS}}\) = 0.0 V \\ \hline
**A** & \(-\)45.17 & \(-\)58.38 & \(-\)73.23 & \(-\)89.49 & \(-\)107.41 \\
**B** & \(-\)44.49 & \(-\)60.10 & \(-\)77.99 & \(-\)98.01 & \(-\)119.92 \\
**C** & \(-\)138.17 & \(-\)161.87 & \(-\)187.07 & \(-\)213.58 & \(-\)241.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Brain current variation due to the self-heating effect.  
Future work will study MIS-HEMT structures in more detail, with wider ranges of dielectric layer compositions and thicknesses.  
Experiments and simulations, I.-T.H., K.-W.J., and H.-J.K.; fabrication, S.-H.L. and J.-W.L.; formal analysis, J.-M.Y. and H.-S.K. ([PERSON]); supervision, H.-S.K. ([PERSON]).  
This work was supported by the program of Defense Acquisition Program Administration and Agency for Defense Development.  
The authors declare no competing financial interests.
## 3 Results
### NEMA/IEC Phantom  
Signal recovery \(RCj\) measured in spherical inserts of the NEMA/IEC phantom are reported in Figure 9. The results obtained for \(RCj\) prove that the max-values method is the most accurate. However, in the present study and in the clinical practice, the physical-volume method value was established as reference method for activity correction due to its reproducibility.  
Figure 8: AVHs of three ROIs delineated on cylindrical homogenous phantom are reported. ROI1 = delineation performed considering the physical volume of the phantom. ROI2 = delineation performed considering 80% of the inner volume of the cylinder. ROI3 = delineation performed with the Whole Body Tool to include phantom wall and cap.  
Figure 9: Recovery Coefficient (RC) as a function of the insert diameter for the NEMA/IEC phantom using different segmentation methods.  
The AVHs of NEMA/IEC phantom for the 6 ROIs obtained by segmentation of the physical volume of each spherical insert are reported in Figure 10. The spill-in and spill-out phenomena are comparable for spherical insert of diameter larger than or equal to 22 mm.
# Acceleration of Particle Swarm Optimization with AVX Instructions
## 2 Particle Swarm Optimization
### Parallelization of PSO  
A real-world optimization requires much computational effort typically. In the case of the PSO algorithm, there could be thousands or even millions of dimensions. Parallelization of these high-dimensional problems provides a needed performance boost. The PSO is among the most efficient stochastic search algorithms. It is intrinsically parallel and best suited for parallelization, being better than other evolutionary or swarm intelligence algorithms [5].  
Improvements in vector processors allow efficient parallelization even without a GPU. The vectorization of PSO using AVX2 instructions is natural because all particles act in parallel implicitly [21].  
The theoretical performance peak of a GPU is a hundred times higher than the performance of a single CPU. However, a GPU shows only about a 30\(\times\) speedup for PSO and similar problems [22]. Various research confirms the potential of multicore SIMD execution, providing a similar performance boost [23; 24].  
Vectorized PSO requires sharing of a few values: an array with \(G_{best}\) position and corresponding fitness evaluation for this position [15; 25]. All other values and particles are mutually independent and easily parallelizable.  
Most compilers have the ability of automatic vectorization. If they detect potentially vectorized code, they will try to replace it with a vectorized counterpart. However, the compiler usually fails to vectorize a part code for various reasons, as confirmed by our test of auto-vectorization of C++ PSO code. There are several ways how to achieve vectorized C++ code:  
* Compiler's auto-vectorization (scalar);
* Vectorized classes (avavx);
* AVX intrinsics (intavx);
* ASM code with AVX instructions (asmavx).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Param** & **Value** \\ \hline PSO iterations & 1000 \\ \hline Particles in swarm & 32, 64, 128, 256, 512 \\ \hline Number of dimensions & 8, 16, 32, 64, 128, 256, 512 \\ \hline Inertia weight & 0.729 \\ \hline Max. velocity & by problem’s range \\ \hline Local weight (c1) & 1.49445 \\ \hline Global weight (c2) & 1.49445 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSO parameters during experiments.  
The methods are ordered from the most programmer-friendly to the sophisticated approach [26; 27; 28]. Writing highly vectorized and fast code decreases readability and maintainability. The most convenient way is using the auto-vectorization capability of the compiler. Even the auto-vectorization of simple loops has some hidden requisites, which are specific to parallel programming [22]. One of the most efficient ways to achieve vectorized code is using already vectorized libraries, vector classes and SIMD intrinsics.
## 4 Discussion  
To check the findings from the proposed noise-reduction algorithm, we conduct a correlation analysis between the signal without noise and the signal after being processed to reduce the noise. This also lets us know that we only have noise and interferences that do not provide information.  
Figure 20 shows the correlation values' behavior to the output of the proposed noise reduction algorithm as a function of the signal-to-noise ratio (SNR) input values. We see that the correlation values decrease exponentially as the SNR ratio at the input decreases.  
With a threshold correlation value of 0.5, we have a linear intrinsic relationship between the input and the output. The algorithm emits correlation values above 0.5 for an input SNR of \(-\)15 dB (low or ambient with high noise concentration), which is relevant since it does not require additional information apart from the frequency, amplitude, and phase components in the signal to be processed.  
In Figures 21 and 22, respectively, we show the spectral pattern and another graphic making zoom, that identifies the number of broken bars shown within the motor. We point out that there is a unique pattern when the motor presents one broken bar and another unique pattern when it has two broken bars that do not depend on the relative position of the bars.  
Figure 19: A comparison for all signals spectra after the noise-reduction process.  
Figure 20: Output correlations values in terms of the input signal to noise ratio (SNR).  
The procedure is based on the spectral subtraction between the healthy motor's power spectrum and the power spectrum of the damaged motor. The spectral patterns obtained from subtraction converge to zero since the proposed algorithm is based only on highlighting the differences in the power spectrum according to the fault treated and with respect to the spectrum of the healthy motor. We also see that the result is not influenced by the number of samples to be processed, which is 80,000 in this case.  
To prove that there is a relation between the spectral patterns with two broken bar, we compute the Pearson relationship coefficients of the vectors (red, green, blue) inferred from the output appeared in Figure 18. The results are presented in Table 3. We see that the Pearson correlation coefficients are  
Figure 21: Spectral pattern obtained from the application of the proposed pattern recognition algorithm showed in Figure 3, to identify broken bars.  
Figure 22: Zoom of the spectral pattern obtained from the application of the proposed pattern recognition algorithm showed in Figure 3, to identify broken bars.  
close to one, which yields that we are in the presence of a common pattern when the engine has two broken bars independently of the relative of the bars.
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 4 Experimental Results and Discussion
### Constancy Repeatability  
Another criterion that is much more important than the convergence rate is the stability and repeatability of the algorithm. The similarity rate of the results for different executions (implementations) with the same input values is called algorithm constancy repeatability [89]. In this study, it was calculated by executing both algorithms 20 times. Figure 8 shows the best result of sequential executions of the algorithms. According to the figure, the changes in the best fitness values for the PSO algorithm are greater than those of BA in the 20 executions. These changes are from 0.702 to 0.752 for BA and from 0.713 to 0.817 for the PSO algorithm. To more accurately calculate the constancy, the standard deviation of the results of the sequential executions is obtained as follows:  
\[\text{STDEV (f(x))}=\sqrt{\frac{\Sigma^{ii}\left(\xi_{i}^{\text{best}}-\mu \right)^{2}}{ii}} \tag{19}\]  
Figure 6: Convergence rate of two network deployment algorithms for 20 executions: (a) BA; (b) PSO.  
Figure 7: The average of the best values over 20 executions for two algorithms: (a) BA; (b) PSO.  
where _ii_ is the number of executions, \(f_{i}^{\text{best}}\) is the best global fitness of each execution, and \(\mu\) is the mean of the results.  
The value of the variance will be obtained between zero and one, as the result values are normal; the values are between zero and one. Whenever the variance is closer to zero, the algorithm is more constant. The standard deviation for both BA and PSO algorithms was obtained 0.0156 and 0.0338, respectively. Although the variance value of the BA is lower than that of PSO, both values are low and near to zero. This proves the constancy and repeatability of both algorithms for the GIS-based network deployment problem.
# Impact of Environmental Factors on Stilbene Biosynthesis
## 3 Impact of Environmental Factors on the Biosynthesis of Stilbenes
### Wounding  
It is well known that the biosynthesis of stilbenes in plants can be triggered by physical stimuli such as wounding stress. This abiotic stress has been shown to affect stilbene accumulation in _Vitaceae_[224], _Fabaceae_[203], and _Pinaceae_[221].  
Freshly pruned canes of _V. vinifera_ cv. Pinot Noir showed a transient expression of _PAL_ and _STS_ genes, followed by a rapid increase in _trans_-resveratrol and _trans_-piceatannol, when cut in short segments (from 0.2 to 10 cm), with the highest increase in 0.5 cm-length sections [224]. According to previous studies [217], only _trans_-resveratrol and _trans_-piceatannol biosynthesis was elicited in pruned grape canes, while no increase in the content of _trans_-\(\varepsilon\)-vinifera, ampelopsin A, _trans_-miyabenol C, _cis_- and _trans_-vitisin B, hopeaphenol, and isohopeaphenol was recorded. The sequential induction of lipoxygenase (_VvLOX_, involved in jasmonic acid biosynthesis) and _VvSTS_ genes suggested that the activation of stilbenoid metabolism in response to wounding stress involves the jasmonate signaling pathway [224].  
The induction of grapevine stilbene biosynthetic genes after wounding has been reported in several studies. The genome-wide analysis carried out by [PERSON] and colleagues [60] on _V. vinifera_ cv. Pinot Noir showed an increase in the transcription level of several _VvSTS_ gene family members. Induction of _VvSTS_ in canes from grapevine Cabernet Franc has been recorded during the first 4 weeks of storage by [PERSON] and co-workers [217], indicating that grapevine wood is still transcriptionally active after pruning. In 2017, [PERSON] et al. [220] observed a significant induction by wounding stress of _VqSTS36_ promoter activity in _Vitis quadrangularis_ L. In 2018, [PERSON] and co-workers [218] reported that in response to wounding the transcript level of _VviSTS29, -41_ and _-48_ gradually increases, coupled with the induction of WRKY and R2R3-MYB transcription factors. Four _WRKY_ genes, namely _VviWRKY03, VviWRKY24, VviWRKY43, and _VviWRKY53_, were thus reported for being involved in the regulation of the stilbene biosynthetic pathway.  
Several studies on Scots pine have shown remarkable increases in pinosylvin and pinosylvin monomethyl ether accumulation following wounding stress, associated with overexpression of _PsSTS_, as well as of genes coding for O-methyltransferaseness involved in stilbene methylation (_PsMT1_ and _PsMT2_) [134; 221; 223].  
Increases in _trans_-resveratrol accumulation coupled to overexpression of stilbene genes in response to wounding stress were also observed in _A. hypogaea_[202; 203; 225].
## 5 Ensemble Hidden Markov Model (EHMM)
As a result, this article refers to HMM as an ensemble hidden Markov model (EHMM) because the K-MC and GMM techniques are embedded in the Gaussian emission process, as shown in Figure 1.
3. Decoding: HMM identifies the hidden states or path based on the observation, \(Q\). The Viterbi algorithm (\(V_{alg}\)) [32] is used to complete this phase. The \(V_{alg}\) is an error-correcting algorithm that searches for a new path using the knowledge from a previous path. It subsequently outputs the path with optimal probability by calculating all probable hidden paths. This conventional HMM approach, utilizing MLE and the \(V_{alg}\), has been applied in prior studies to predict credit card fraud. However, the reliability of the extracted feature vector is crucial to the performance of EHMM. In fact, the more reliable the feature vector, the better the performance of EHMM. Therefore, this article places significant emphasis on the feature extraction technique.
## 3 Optimization Problem Characterization
### Power Constraint  
The power designated for every user device in the cell is a fraction of the whole power \(P_{t}\) sent from BS, therefore the power percentage for each device must conform with [22]:  
\[\sum\limits_{i=1}^{M}\eta_{i}\leq 1 \tag{17}\]  
where \(\eta_{i}\) is the power percentage allocated for the \(i\)th user.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Uniformly Distributed Small Cells
#### 3.2.2 Interference
The total interferences in the HetNet environment can then be given by using Equations (15) and (18) as:  
\[I_{\rm HetNet}=I_{\rm macro}+I_{\rm small} \tag{19}\]
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 5 Conclusions  
In this research, a deformable attention-guided network (DAGN) for pedestrian detection was developed, in which deformable regions and attention features were used with global context information by proposing the DCAM. In order to optimize the prediction localization, an optimal loss function was designed, which combines BCE loss with CIoU loss for classification and regression. DIoU-NMS was followed to refine the prediction boxes, which further promotes the detection performance of occluded instances. Furthermore, the ALTM algorithm was applied as a preprocessing procedure to improve the detection performance under low illuminance conditions. Extensive evaluations demonstrated that the proposed DAGN achieves promising performance and outperforms other state-of-the-art methods, especially for heavily occluded pedestrians.  
In future work, we plan to further decrease the computational cost and run time without sacrificing the performance. Network prune and distillation will be explored to make a lightweight, real-time pedestrian detector while keeping the detection accuracy.  
[PERSON] developed the idea, implemented the experiments, and wrote the manuscript. [PERSON] prepared the Caltech dataset and participated ablation experiments. [PERSON] supervised the research and performed revisions and improvements. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (10080619).  
Not applicable.  
The data in this study can be requested from the corresponding author.  
This work was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (10080619).  
Figure 10: Example detection results of our proposed DAGN method on the ECP validation set. Green boxes, ground truth and red boxes, detection results. We visualized the detection boxes with the confidence score larger than 0.3.