esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Molecular dynamics simulations are carried out to investigate the structure and capacitance of the electrical double layers (EDLs) at the interface of vertically oriented graphene and ionic liquids [EMIM]\({}^{+}\)/[BF\({}_{4}\)]\({}^{-}\). The distribution and migration of the ions in the EDL on the rough and non-rough electrode surfaces with different charge densities are compared and analyzed, and the effect of the electrode surface morphology on the capacitance of the EDL is clarified. The results suggest that alternate distributions of anions and cations in several consecutive layers are formed in the EDL on the electrode surface. When the electrode is charged, the layers of [BF\({}_{4}\)]\({}^{-}\) anions experience more significant migration than those of [EMIM]\({}^{+}\) cations. These ion layers can be extended deeper into the bulk electrolyte solution by the stronger interaction of the rough electrode, compared to those on the non-rough electrode surface. The potential energy valley of ions on the neutral electrode surface establishes a potential energy difference to compensate the energy cost of the ion accumulation, and is capable of producing a potential drop across the EDL on the uncharged electrode surface. Due to the greater effective contact area between the ions and electrode, the rough electrode possesses a larger capacitance than the non-rough one. In addition, it is harder for the larger-sized [EMIM]\({}^{+}\) cations to accumulate in the narrow grooves on the rough electrode, when compared with the smaller [BF\({}_{4}\)]\({}^{-}\). Consequently, the double-hump-shaped C-V curve (which demonstrates the relationship between differential capacitance and potential drop across the EDL) for the rough electrode is asymmetric, where the capacitance increases more significantly when the electrode is positively charged.  
Keywords:ionic liquid; vertically oriented graphene; electrical double layers; charge density; capacitance +
Footnote †: journal: Applied Physics Letters B
## 2 Materials and Methods
### Intracellular Active Tyrosine and Melanin Levels  
Cells at a density of 3.4 \(\times\) 10\({}^{4}\) cells/well were cultured in 6-well plates. Depending on \(\upalpha\)-MSH stimulation and sample treatment, treatment groups were established as follows: blank group (B): untreated, unstimulated; control group (C): untreated stimulated with 100 nM \(\upalpha\)-MSH; kojic acid group (KA), stimulated with 100 nM \(\upalpha\)-MSH and treated with 10 \(\upmu\)M kojic acid; crude extract and fractions: stimulated with 100 nM \(\upalpha\)-MSH and treated with corresponding sample at given concentration (5, 10, and 20 \(\upmu\)g/mL). Fractions were obtained by separation of crude extract depending on its solubility in different solvents and named according to fractionating solvent: distilled water (H\({}_{2}\)O), butanol (\(n\)-BuOH), 85% aqueous methanol (85% aq. MeOH), and hexane (\(n\)-hexane). Plates were incubated for 24 h, and after the incubation period, melanogenesis in cells was stimulated by treating cells with 100 nM \(\upalpha\)-MSH for 24 h [20]. Subsequently cells were introduced to different concentrations of _Rosa rugosa_ crude extract and its solvent fractions for a further 24 h. After treatment, wells were washed twice with 1\(\times\)PBS, and 200 \(\upmu\)L lysis buffer was added to each well. Plates then were kept at 4 \({}^{\circ}\)C for 30 min to induce lysis. Then the lysate was harvested and separated by centrifugation at 13,000 rpm for 10 min, and the supernatant was used to determine the intracellular tyrosinase activity, while the remaining cell pellet was used to detect melanin content. For active tyrosine levels, the protein content of the supernatant was determined using Bio-Rad Protein Assay Dye Reagent Concentrate (Bio-Rad Laboratories, Hercules, CA, USA). The reaction mixture was prepared by adding 0.1% (\(w/v\)) L-DOPA to the supernatant mixed in 0.1M sodium phosphate buffer (pH 7.0) in 96-well plates after the protein content of each sample normalized to 20 \(\upmu\)g. The reaction mixture then was placed into a 37 \({}^{\circ}\)C incubator for 1 h. Using a microplate reader (Multiskan GO, Tecan Austria GmbH, Grodig, Austria), the absorbance at 490 nm was detected to measure tyrosinase activity. For intracellular melanin content, the cell pellet was resuspended in 200 \(\upmu\)L 1N NaOH after it was washed with 75% ice-cold ethanol. After the resuspended cell pellet was heated for 10 min at 90 \({}^{\circ}\)C, the absorbance values of wells were detected at 405 nm (Multiskan GO, Tecan Austria GmbH, Grodig, Austria), and intracellular melanin content was measured according to the standard curve.
## 3 Numerical Visualization  
### Bias Deviation with Single-Arm Driven Mode  
In this subsection, we numerically calculate the transfer functions of the single-arm driven mode, as shown in Figure 2a-d. The fiber length is set as 20 km, and the dispersion factor \(D\) is 16 ps/nm/km throughout Section 3. Note that in Section 3, the factor of 2 in Equations (4), (7), (11) and (15) is niglected for normalization. The dashed gray curve represents the transfer function with differential driven mode, which is employed as a reference. We found that the first fading frequency was 14.0 GHz for conventional IMD transmission. Thanks to the upper-arm single driven mode with bias around \(\pi/4\), the first fading frequency increased to \(\sim\)17.1 GHz, as in Figure 2a. Additionally, the available transmission bandwidth slightly enhanced by reducing the constant phase of bias. However, it should be noted that the frequency components near zero were suppressed as the fading notch shifted toward a higher frequency. Unfortunately, when the bias was switched to \(3\pi/4\), as in Figure 2b, the frequency of the first notch was 9.9 GHz, which was smaller than the differential driven mode. In addition, for lower-arm driven mode, as in Figure 2c,d, the performance was exactly the opposite. To be specific, the fading frequency with upper-arm driven biasing at \(\pi/4\) was equivalent with lower-arm driven biasing at \(3\pi/4\). Meanwhile, the frequency of notch moved toward, rather than away from, zero, as the increasing of bias induced the constant phase.  
Figure 2: Calculated transfer function of (**a**) upper-arm single driven mode with bias around \(\pi/4\), (**b**) upper-arm single driven mode with bias around \(3\pi/4\), (**c**) lower-arm single driven mode with bias around \(\pi/4\), and (**d**) lower-arm single driven mode with bias around \(3\pi/4\). Diff.: standard differential mode.
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 2 Improved Artificial Potential Field Method  
### Classical Artificial Potential Field Method  
In the APF, the obstacle and the target point generate repulsive and gravitational potential fields, respectively, which act simultaneously on the robot to make it move safely from the starting point to the target point without collision.  
The robot path planning problem is studied in two dimensions, and the robot is regarded as a particle. Assuming that its position in the potential field is \(W_{1}=(x,y)\), then the position of the target point is \(W_{2}=X_{N}=[x_{n},y_{n}]\), and the gravitational force on the robot is as follows:  
\[F_{G}(X)=-\Delta[\frac{1}{2}k(X-X_{n})^{2}]=k_{1}\alpha_{1} \tag{1}\]  
where \(k_{g}>0\) is the gravitational gain coefficient; \(\alpha_{1}\) and \(\epsilon_{1}\) represent the unit vector and linear distance of the robot from the target point in the potential field, respectively, where \(\epsilon_{1}=\|X-X_{n}\|\). The robot is subject to the repulsive force function of the obstacles in the environment as follows:  
\[F_{Z}(x)=k_{z}(\frac{1}{\mu}-\frac{1}{\mu_{0}})\frac{1}{\mu^{2}}\cdot\frac{ \partial\mu}{\partial X} \tag{2}\]  
where \(k_{z}>0\) is the repulsive gain coefficient of the function; \(\mu\) is the distance between the robot and the obstacle; \(\mu_{0}\) is the range of the obstacle, \(\mu\leq\mu_{0}\).The direction and magnitude of the robot's motion in the potential field depends on the combined force \(F(X)\):  
\[F(X)=F_{G}(X)+F_{Z}(x) \tag{3}\]
## 4 Dual-Band Passive Metasurface Based Beam Steering Techniques
### Reflectarray-Based Beam Steering  
Introduced initially in 1963 [51], RAs are a type of antenna that combines the attributes of reflector antennas with array antenna principles. It consists of a feed antenna illuminating a reflecting surface. The feed is usually a horn antenna placed at a focal distance from the reflecting surface. In RAs, planar metasurfaces are mostly used to emulate the behavior of a curved reflector. These metasurfaces comprise an array of scatterers that produce a predefined phase shift to compensate for the path lengths of a radiated wave from the feed placed at the focal length from the surface. As a result, the beam can be tilted in any direction based on the arrangement of the phase-shifting scatterers. A comparative analysis of passive mechanically reconfigurable single band RAs is given in [52]. Reconfigurable RAs that comprise a combination of active and passive metasurfaces are presented in [53]; however, beam steering is not explored. Dual-band beam steering has been made possible with reconfigurable RAs or a hybrid of active and passive RAs that can change the phase shift of cells to make adaptive beam steering possible. Such RAs are reported in [38; 54; 55; 56; 57].
## 2 Related Work
### Fast Fourier Transform in Deep Learning  
A new method to address signal-based fault detection and diagnosis for rotating machinery was developed by [PERSON] et al. [23]. For feature extraction, the proposed model integrates two distinct signal transform techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), alongside statistical features derived from the raw signal to capture all fault signatures. By employing a Convolutional Long Short-Term Memory (CLSTM) architecture, the framework efficiently handles multi-channel input data and learns its spatiotemporal characteristics. Through a sensitivity analysis conducted on the input channels, the research demonstrated that implementing these multi-domain features enhances the model's accuracy. The study of [PERSON] et al. [24] focused on implementing an innovative ensemble deep learning model for monitoring cutting tool wear by utilizing audio sensors. An audio denoising technique combined with FFT and bandpass filters along with dependent component analysis was employed to extract tool wear data during machining. Subsequently, a detection model based on ensemble convolutional neural networks was trained and the audio signals were transformed into audio images using various algorithms. The findings affirmed the high accuracy of the proposed method in predicting tool wear values across various cutting conditions. Moreover, [PERSON] and [PERSON] presented a novel approach to classify electroencephalogram (EEG) signals using a deep learning model [25]. It automates the learning of relevant features within a supervised learning framework. FFT is employed to generate the EEG matrix. Subsequently, a PCA neural network extracts hidden information from the frequency matrix of EEG signals. These deep features are then utilized as inputs to train a support vector machine model to recognize epileptic seizures.
## 3 Model Parameter Estimation
### Propulsion Noise
#### 3.2.4 Background Noise Corrections  
On the test rig, the main background noise sources were the tire noise, the airstream fan, the room ventilation and the dynamometer itself. Firstly, to confine the tire noise, the measurements were performed at low vehicle speeds, _i.e._, low gears. Secondly, during the measurements, the airstream fan (depicted in Figure 8) was briefly switched off for periods of about 10 s. However, the dropping tonal components of the fan still strongly interfered with the propulsion noise of the car (see Figure 18). Therefore, several shifted analysis time windows were deployed, and the minimal levels and the maximum level standard deviations, \(\sigma\), across these windows were exploited. Thirdly, background noise measurements with a switched off engine at different vehicle speeds were performed. For each ordinary measurement, the corresponding background noise was identically analyzed and used for level corrections.  
Figure 17: Level fluctuation standard deviations, \(\sigma\), in 1/3 octave bands measured at the front of two diesel engine and three gasoline engine cars idiling at 900 rpm.  
Figure 18: Normalized spectrogram of a microphone position in front of a Ford Focus 1.8i at 4000 rpm and full load on the dynamometer. The dropping tonal component around 100 Hz stems from the briefly switched off airstream fan of the lab.
# A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images
## 1 Introduction
### Scope and Objectives  
Imaging techniques for the human body have grown increasingly popular in recent times, with the development of equipment in this field, as well as with the development of hardware and software systems in the field, capable of managing the volumes of data involved in medical imaging. This has allowed today more and more advanced methods in the field of MR (Magnetic Resonance) imaging, CT (Computer Tomography) or PET (Positron Emission Tomography). All of these methods require advanced image processing in order to extract useful diagnostic information from the available volume of data. For example, 3D rendering techniques allow better visualization of three-dimensional volumes, feature extraction techniques from images allow better assisted diagnosis, image segmentation techniques allow the separation of different areas or tissues of interest, and noise elimination techniques from images allow the improvement of image quality [55]. Some of these image processing techniquescan be used in a processing chain. For example, in order to segment certain parts of the human body and obtain good segmentation results, the noise from the images must be removed [56; 57; 58].  
In this article we propose and characterize by simulations a novel algorithm used to reduce the white Gaussian additive noise in the images. The algorithm exploits both types of dependencies between wavelet coefficients. We characterized the algorithm through simulations. The proposed algorithm exploits the intra-scale dependence of wavelet coefficients by determining the local dispersion of the signal, and the inter-scale dependence by determining the contours based on the hierarchical correlation map. The results obtained by applying this algorithm show that the reduction of noise in images is achieved when the quality of the contours is maintained or even improved. At the same time, by choosing a parameter, it is possible to establish the compromise between the amount by which the noise is reduced and the quality of preserving the details in the image [59].  
This article is divided in four sections, which include Introduction, Materials and Methods, Results and Discussions and Conclusions. In the Introduction, we introduced the subject of our research and presented the state of the art related to the topic. The second part is Materials and Methods where all the steps of the filtering technique are presented: Adaptive spatial filtering based on inter-band and intra-band correlation of wavelet coefficients and Adaptive spatial soft truncation based on the hierarchical correlation map and with the establishment of the threshold value based on local statistics. In the third part we presented our Results and Discussions in which we conducted studies both on usual images and also on CT images. Finally, in the last part, we presented the Conclusions of the obtained results.
## 2 Materials and Methods
### Experimental Detail  
In the current study, to further overcome the issue related to a limited amount of data, the five-fold cross-validation strategy was adopted by randomly dividing the whole dataset into five folds (9 or 13 studies). Consequently, we trained five models by combining four of the five folds into a training set and by keeping the remaining fold as the validation fold. In our experiments, to avoid including slices from the same patient into the training and validation sets, the patients were firstly split randomly into a training set and validation set and then the slices corresponding to the patients were used to construct the training and validation sets. Therefore, the issue of potential overlap between the training and validation sets does not exist. We used an initial set of 20 lesions to determine the best learning rates experimentally. Specifically, for the training task, the following parameters were used: (i) a learning rate of 0.0001 and 0.00001 with Adam optimizer [28] for ENet and ERFNet, respectively; (ii) a batch size of 8 slices for all studies; (iii) a maximum of 100 epochs with an automatic stopping criterion (if the loss did not decrease for 10 consecutive epochs); and (iv) the Tversky loss function [29] with \(\alpha\) = 0.3 and \(\beta\) = 0.7. Data augmentation was obtained by randomly rotating; translating in both the x and y directions; and applying shearing, horizontal flip, and zooming to the input training slices. Consequently, six different types of data augmentation techniques were used to reduce overfitting, while data standardization and normalization were used to help the models converge faster and to avoid numerical instability. A graphics processing unit (GPU), i.e., NVIDIA QUADRO P4000 with 8 GB VRAM and 1792 CUDA Cores, was adopted to train and run inference.
## Appendix A The Bit Error Rate in Non-AWGN Channels
### The Decoding Performance Difference of \(L\) Factor Graphs Is Small  
Assuming that the decoding performance varies across different \(L\) factor graphs, without loss of generality, we assume that the transmitted codeword is 1. Let \(Pe_{i}\) represent the bit error rate, then:  
\[Pe_{i}=p(\lambda_{i}\leq 0). \tag{2}\]  
Assuming the factor graph decoding performance is ranked from best to worst, let \(Pe_{1}<Pe_{2}<\cdots<Pe_{L}<0.5\). At this point, \(w_{1}>w_{2}>\cdots>w_{L}\), \(\mu_{1}>\mu_{2}>\cdots>\mu_{L}\), \({\sigma_{1}}^{2}<{\sigma_{2}}^{2}<\cdots<{\sigma_{L}}^{2}\).  
\(\sum\limits_{j=1}^{L}w_{j}\lambda_{j}\) follows a distribution with parameters \((\sum\limits_{j=1}^{L}w_{j}\mu_{j},\sum\limits_{j=1}^{L}w_{j}^{2}\sigma_{j}^{ 2})\). When the decoding performance difference of \(L\) factor graphs is small, \(\mu_{1}\approx\mu_{2}\approx\cdots\approx\mu_{L}\) and \(\sigma_{1}\approx\sigma_{2}\approx\cdots\approx\sigma_{L},\sum\limits_{j=1}^{L }w_{j}\mu_{j}=\mu_{1}\sum\limits_{j=1}^{L}w_{j},\sum\limits_{j=1}^{L}w_{j}^{2} \sigma_{j}^{2}={\sigma_{1}}^{2}\sum\limits_{j=1}^{L}w_{j}^{2}\) the bit error rate of BPL decoding using the path selection criterion proposed in this paper is:  
\[p(\frac{\sum\limits_{j=1}^{L}w_{j}\lambda_{j}}{\sum\limits_{j=1}^{L}w_{j}}\leq 0 )=p(\sum\limits_{j=1}^{L}w_{j}\lambda_{j}\leq 0)=\Phi(\frac{-\sum\limits_{j=1}^{L}w_{ j}\mu_{j}}{\sqrt{\sum\limits_{j=1}^{L}w_{j}^{2}\sigma_{j}^{2}}})=\Phi(- \frac{\sum\limits_{j=1}^{L}w_{j}\mu_{j}}{\sqrt{\sum\limits_{j=1}^{L}w_{j}^{2} \sigma_{j}^{2}}}). \tag{3}\]  
When the BPL bit error rate is lower than the bit error rate of each BP decoder, we have:  
\[p(\frac{\sum\limits_{j=1}^{L}w_{j}\lambda_{j}}{\sum\limits_{j=1}^{L}w_{j}}\leq 0 )<p(\lambda_{1}\leq 0), \tag{4}\]  
i.e.,  
\[\Phi(-\frac{\sum\limits_{j=1}^{L}w_{j}\mu_{j}}{\sqrt{\sum\limits_{j=1}^{L}w_{ j}\sigma_{j}^{2}}})<\Phi(-\frac{\mu_{L}}{\sigma_{L}}). \tag{5}\]Since the cumulative function is a monotonically increasing function, (A5) holds if and only if:  
\[\frac{\sum\limits_{j=1}^{L}w_{j}!\mu_{j}}{\sqrt{\sum\limits_{j=1}^{L}w_{j}!^{2} \sigma_{j}^{2}}}=\frac{\mu_{1}\sum\limits_{j=1}^{L}w_{j}}{\sigma_{1}\sqrt{\sum \limits_{j=1}^{L}w_{j}^{2}}}>\frac{\mu_{1}}{\sigma_{1}}\] (A6)  
Since \((\sum\limits_{j=1}^{L}w_{j})^{2}>\sum\limits_{j=1}^{L}w_{j}^{2}\), the formula (A4) holds.
###### Abstract  
Biomass burning plays a key role in the interaction between the atmosphere and the biosphere. The nearly two-decade-old Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product provides critical information (e.g., fire radiative power or FRP) for characterizing fires and estimating smoke emissions. Due to limitations of sensing geometry, MODIS fire detection capability degrades at off-nadir angles and the sensor misses the observation of fires occurring inside its equatorial swath gaps. This study investigates missing MODIS FRP observations using the 375 m Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data across Africa where fire occurs in the majority of vegetation-covered areas and significantly contributes to global biomass-burning emissions. We first examine the FRP relationship between the two sensors on a continental scale and in grids of seven different resolutions. We find that MODIS misses a considerable number of low-intensity fires across Africa, which results in the underestimation of daily MODIS FRP by at least 42.8% compared to VIIRS FRP. The underestimation of MODIS FRP varies largely with grid size and satellite view angle. Based on comparisons of grid-level FRP from the two sensors, adjustment models are established at seven resolutions from 0.05\({}^{\circ}\)-0.5\({}^{\circ}\) for mitigating the underestimation of MODIS grid FRP. Furthermore, the investigation of the effect of equatorial swath gaps on MODIS FRP observations reveals that swath gaps could lead to the underestimation of MODIS monthly summed FRP by 12.5%. The quantitative information of missing MODIS FRP helps to improve our understanding of potential uncertainties in the MODIS FRP based applications, especially emissions estimation.  
biomass burning; MODIS FRP; 375 m VIIRS FRP; swath gap; FRP adjustment; Africa
## 2 Experimental Procedure
### AE Signal Analysis  
AE sensors were mounted on the surface of the composite specimens, and the recorded AE signals were associated with the damage sources in the composites. Hence, AE parameters can be used to assess the damage behavior of the composites. Figure 3 shows a typical wave shape of a burst-type AE signal. With respect to the damage evaluation, different AE characteristic parameters reveal different physical significance. The cumulative AE hits are strongly associated with the diversity of signals in composites. The peak amplitude, rise time, duration, and frequency relate to the damage mode (e.g., matrix cracking, delamination, and fiber damage).  
In addition, AE energy can be adopted to describe acoustic activity and fulfill the unsupervised pattern recognition based on the SF. This function is based on the idea that the specimens store strain energy during the loading process and simultaneously release part of the stored energy due to internal damage and focuses on a global variable between AE activity and the mechanical behavior of composites, which is defined by:  
\[f(x)=Ln\bigg{[}\frac{E_{s}(x)}{E_{a}(x)}\bigg{]} \tag{1}\]  
Here, \(x\), \(E_{s}(x)\), and \(E_{a}(x)\) are the displacement, the strain energy, and the AE events energy, respectively.  
On the basis of the previous studies [33; 34; 35], this function, \(f\), can be defined by five different behaviors, as shown in Figure 4. S\({}_{1}\) is related to an increasing trend of the strain energy storing and S\({}_{2}\) represents the sudden drop of \(f\) that indicates the emergence of internal damage in materials. S\({}_{3}\) indicates the constant behavior of \(f\), which corresponds to an equivalent energy release due to material damage progression. The subsequent Bottom-Up (BU) trend indicates that a strengthening event induced a new energy storing capability in the materials. S\({}_{4}\) represents the decreasing behavior of \(f\), which indicates that the damage has reached a maximum and the material has no resources to sustain the load.  
Figure 4: Five different behaviors of sentry function (SF) vs. displacement.  
Figure 3: A typical waveform for a burst-type AE signal.