esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Type of Failure  
Nine implants failed in nine patients (2.13%), 66.6% (N = 6) in male and 33.3% (N = 3) in female, with no statistically significant differences, \(p\) = 0.337. They were divided into early failures pre-prosthetic load (4 implants) with an average follow-up of 13 weeks (range: 4-20 weeks), 2 implants with early failures with an immediate load (4 th and 8 th week), and 3 implant failures after applying the definitive prosthetic load, with a mean follow-up of 16 weeks (range: 2-32 weeks). The results were statistically significant with \(p\) < 0.0001 for early failure compared to late failure. Early/late failure was also evaluated regarding implant position, also presenting statistically significant results for implants placed in the anterior region, with \(p\) = 0.014. Table 5; Table 6 show correlations between variables and failure. Table 7 summarizes the possible causes for explanation and the clinical characteristics. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Location** & \multicolumn{3}{c}{**No.**} & \multicolumn{3}{c}{**1 stQ**} & \multicolumn{3}{c}{**2 ndQ**} & \multicolumn{3}{c}{**3 rdQ**} & \multicolumn{3}{c}{**4 thQ**} & \multicolumn{3}{c}{**D**} & \multicolumn{3}{c}{**No.**} & \multicolumn{3}{c}{**\%**} & \multicolumn{3}{c}{**L**} & \multicolumn{3}{c}{**No.**} \\ \multicolumn{1}{c}{**of OH**} & \multicolumn{3}{c}{**N°**} & \multicolumn{3}{c}{**of OII/\%**} & \multicolumn{3}{c}{**N°**} & \multicolumn{3}{c}{**of OII/\%**} & \multicolumn{3}{c}{**N°**} & \multicolumn{3}{c}{**of OII/\%**} & \multicolumn{3}{c}{**N°**} & \multicolumn{3}{c}{**of OII/\%**} & \multicolumn{3}{c}{**N°**} & \multicolumn{3}{c}{**of OII/\%**} & \multicolumn{3}{c}{**of OII/\%**} \\ \hline Maxilla & 225 & 0.53 & 1.1 & 9 & 7.76 & 2.1 & 11 & 10.1 & 3.1 & 3 & 3.0 & 4.1 & 3 & 3.09 & 3.0 mm & 19 & 4.5 & 6 mm & 3 & 0.71 \\ \hline Anterior & 116 & 0.52 & 1.2 & 14 & 12.07 & 2.2 & 10 & 9.17 & 3.2 & 11 & 11.0 & 4.2 & 14 & 14.43 & 3.4 mm & 17 & 4.03 & 7.5 mm & 3 & 0.71 \\ Posterior & 109 & 0.48 & 1.3 & 15 & 12.93 & 2.3 & 10 & 9.17 & 3.3 & 5 & 5.0 & 4.3 & 5 & 5.15 & 3.8 mm & 227 & 56.16 & 9 mm & 34 & 8.06 \\ \hline Mandible & 197 & 0.47 & 1.4 & 21 & 18.01 & 2.4 & 26 & 23.9 & 3.4 & 10 & 10.0 & 4.4 & 9 & 9.28 & 4.6 mm & 133 & 31.52 & 152 & 36.02 \\ \hline Anterior & 60 & 0.30 & 1.5 & 16 & 13.79 & 2.5 & 20 & 18.4 & 3.5 & 15 & 15.0 & 4.5 & 10 & 10.31 & 5.8 mm & 16 & 3.79 & 12 mm & 217 & 51.42 \\ Posterior & 137 & 0.70 & 1.6 & 34 & 29.1 & 2.6 & 26 & 23.9 & 3.6 & 46 & 46.0 & 4.6 & 46 & 42.27 & NA & NA & NA & 15 mm & 12 & 2.84 \\ \hline  & & & 1.7 & 7 & 6.03 & 2.7 & 6 & 5.5 & 3.7 & 10 & 10.0 & 4.7 & 10 & 10.31 & NA & NA & NA & 18 mm & 1 & 0.24 \\ \hline Total & 422 & 100 & 116 & 100 & 109 & 100 & 100 & 100 & 97 & 100 & Total & 422 & 100 & & 422 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Implant distribution by location, diameter, and length. No. of OII: number of osteointegrated implants; Q. quadrant; F. frequency; D. diameter; L. length; OII: osseointegrated implant; NA: not applicable (the diameter and length of the implants were not classified by location).
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 4 Experimental Results
### Parameters Setting  
The parameters set in our experiment were as follows: the superpixels number \(N_{s}\) was set to 500, the cluster number \(K\) was set to 60, the patch size \(b\times b\) was set to \(7\times 7\), noise variance \(\delta\) was in the range of [5; 15; 25; 40; 60; 80], and the number of similar patches \(L\) was set to 10. The iteration number \(J\) was set based on the noise level, and we required more iterations for a higher noise level. From experience, we set the iteration number \(J\) to 7, 9, 13 and 16 for \(\delta\leq 15\), \(15<\delta\leq 30\), \(30<\delta\leq 60\) and \(\delta\geq 60\), respectively. Other regularity parameters were all empirical values as well, where \(\beta\) was set to 0.5, \(\rho\) was set to 0.5, \(\mu\) was set to 0.01, \(\gamma\) was set to 0.2, \(\lambda\) was set to 0.03, \(\eta\) was set to 0.3 [15; 16].  
To verify the influence of image patch size \(b\times b\) on peak signal to noise ratio (PSNR), structural similarity (SSIM), figure of merit (FOM), 100 test images were selected to calculate the average PSNR, average SSIM and average FOM with different patch size, when noise variance \(\delta\) was set to 15. Figure 4 shows the changing trend of the average PSNR, average SSIM and average FOM over the image patch size. It was evident that when the patch size was equal to \(7\times 7\), the average PSNR, average SSIM and average FOM achieved their maximum values. The influence of the cluster number \(K\) on PSNR, SSIM and FOM was tested in the same way, and is shown in Figure 5. When the cluster number was equal to 60, the average PSNR and average FOM achieved their maximum values, and the average SSIM obtained its maximum values when the cluster number equaled to 100. To compromise, we set the cluster number \(K\) to 60 for an optimal solution.  
Figure 4: The impact of patch size on average PSNR (peak signal to noise ratio), average SSIM (structural similarity), and average FOM (figure of merit) of SC-SR.  
Figure 3: The 10 standard benchmark images. (**a**) Baboon; (**b**) Fingerprint; (**c**) Airplane; (**d**) Monarch; (**e**) Lena; (**f**) House; (**g**) Peppers; (**h**) Straw; (**i**) Hill; (**j**) Woman.
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 4 Conclusions  
In this research, a low-cost, concise, and fast bioelectrical impedance spectrometer (BIS) system has been successfully developed. This system works with current sources that can be selected through software, with values of 1 uA, 10 uA, 100 uA, and 1 mA. Working frequency that can be achieved is from 10 Hz to 1 MHz, and is optimal in the frequency range of 100 Hz to 100 kHz. This is related to the working ability of GPD-AD8302 at low frequencies, and also the limitations of op-amps in high frequency operations. The results of measurement of complex impedance dispersion of the measured material are displayed in real-time in the form of Bode and Nyquist plots. The use of automatic frequency scanning techniques, the AD8302 GPD device, and the calculation of complex impedance values carried out automatically by software, is the key to this equipment being able to work quickly. To measure the full cycle in a certain frequency range, including displaying the results in the Bode and Nyquist plots, it only takes no more than one minute. The developed BIS system has been tested to measure the complex impedance of an RC circuit, and is able to detect component values with a maximum error of 1.5%. It has also been used to measure the impedance distribution of a solution in different concentrations, and is able to distinguish its concentration very well.  
Conceptualization, D.R.S. and C.S.W.; Methodology, D.R.S., B.P., and U.P.J.; Software, B.P. and D.R.S.; Validation, D.R.S. and C.S.W.; Formal analysis, D.R.S.; Investigation, B.P. and D.R.S.; Resources, D.R.S., C.S.W., and U.P.J.; Writing, B.P. and D.R.S.; Funding acquisition, D.R.S. All authors have read and agreed to the published version of the manuscript.  
Directorate of Research and Community Service, Directorate General of Strengthening for Research and Development, Ministry of Research, Technology and Higher Education, Republic of Indonesia, grant number (167/SP2H/LT/DRPM/2019).  
Thanks to the Institute of Research and Community Services (LPPM), Brawijaya University, which has supported this research (330.20/N10.C10/PN/2019).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Uniformly Distributed Chaotic Signal for Uniform Shaping of Conventional PWM Power Spectrum
This approach markedly reduces the peak value of the power spectrum, consequently lowering EMI from the power converter and enhancing its electromagnetic compatibility performance.
# Old-Fashioned, but Still a Superfood--Red Beets as a Rich Source of Bioactive Compounds
## 6 Vitamins  
Beetroots and beet products are also great sources of vitamin C, vitamin A, and vitamins from the B group. The B-group vitamins are a collection of 8 water-soluble vitamins including thiamine (B1), riboflavin (B2), niacin (B3), pantthenic acid (B5), pyridoxine (B6), biotin (B7), folate (B9), and cyanocobalamin (B12). According to [PERSON] and [PERSON] 100 g of this plant contains: vitamin A (2.6 mcg), K, (3.2 mcg), C (4.36 mg), E (0.18 mg) B3 (0.03 mg), B6 (90 mg), B2 (0.034 mg), and B5 (0.151 mg) [67]. These values are in agreement with findings published by [PERSON] et al. [76]. Similar concentrations of vitamins mentioned above were reported by the US Department of Agriculture in the most recent release of the USDA National Nutrient Database for Standard Reference, a major source of food composition data [77]. Analysis of vitamin C in different parts of the beetroot was conducted by [PERSON] et al. [68]. According to this study, the highest concentration of ascorbic acid was found in the pulp of the beet and the lowest in its leaf. The concentrations of vitamin C, a well-known antioxidant, vary by plant part; in decreasing order of concentration, the parts are: pulp > skin > leaf > stalk for organic beet and pulp > stalk > skin > leaf for conventionally grown beet. This suggests that the method of cultivation influences the concentration of ascorbic acid in a specific part of the plant. Part-specific concentrations were observed in beets from organic and conventional cultivation. However, the highest level of vitamin C was detected in each part of plants fertilized with organic manure [68].  
[PERSON] and [PERSON] also showed the differences in the concentration of vitamins between beet leaves and tubers [8]. In the case of niacin, higher concentrations of vitamins A, B6, and C were detected in leaves, while folate was identified in much higher concentrations in tubers. Vitamins from the B group were also detected in beetroot juices [25]. This study also showed that organic juice is a better source of vitamins in comparison to those obtained from beets from conventional cultivation. It can be seen in the example of pyridoxal (B6),  
Figure 5: The comparison of selenium species and betanin contents, and antioxidant properties of beetroot juice. Reprinted with permission from [71].  
whose concentration in organic juice was about three times higher in comparison to other studied juices. Also, nicotinamide (B3) was detected in organic juice and in one juice from conventionally grown beetroots. In other juices, this vitamin was below the limit of detection (0.03 mg\(\cdot\) L\({}^{-1}\)). It is difficult to compare the obtained results with others in the literature as there is no information from what vegetables the juice was made. However, the published results focused on the analysis of vitamins in the vegetable rather than in the juice made from it. It should also be noted that possible discrepancies in the content of biologically active compounds and vitamins may result from different varieties or growing conditions.
# Phytomicrobiones: A Potential Approach for Sustainable Pesticide Biodegradation
## 4 Omics Approaches in Pesticide Biodegradation
### Transcriptomics  
In the quest to understand the genetic basis underpinning pesticide biodegradation, transcriptomics emerges as a suitable tool. This omics discipline illuminates the intricate transcriptional activities of genes, unravels gene regulons, and exposes their intricate responses to various stimulants. Moreover, transcriptomics proves to be a versatile tool for mapping DNA-binding sites of genes associated with degradation, all while facilitating comparative genotyping [152]. Through the lens of transcriptomics, we gain insights into the physiology of pesticide-degrading microbial strains, revealing the ingenious optimization strategies for biodegradation. It is through transcriptomic analysis that we unearth novel, efficient pathways for dismantling these chemical adversaries. [PERSON] et al. (2012) used microarrays to dissect the transcriptional changes occurring during paraquat exposure and the ensuing oxidative stress response in _E. coli_ 0157:H7 [153]. Their findings showed that gene responses were significantly induced in cells in the logarithmic growth phase compared to those in the stationary phase, showcasing the dynamic nature of pesticide response mechanisms. Similarly, [PERSON] et al. (2003) deployed microarrays to scrutinize the expression of bacterial metabolism genes within mixed microbial communities confronted by 2,4-D [154]. Their insights reinforced the utility of microarrays as invaluable tools for the detection of bacterial gene expression in the intricate milieu of wastewater treatment and other complex ecosystems. RNA-Seq and RT-PCR methodologies took the spotlight in [PERSON] et al.'s (2013) work with _Klebsiella pneumoniae_ CG43, as they explored the transcriptional shifts induced by the overexpression of the yjcC gene [155]. This gene, responsible for encoding a probable phosphodiesterase activated by paraquat, held the key to understanding critical responses in pesticide-degrading pathways. [PERSON] et al. (2018) employed RNA sequencing to pinpoint genes controlling the biodegradation and metabolism of chlorimuron-ethyl within _Rhodococcus erythropo_ D310-1 [156]. Their work revealed 500 genes during the degradation process, with dominant metabolic pathways featuring \"toluene degradation\" and \"aminobenzoate degradation\". Hence, through transcriptomics, we unravel the genetic basis that guides pesticide-degrading microorganisms, opening up avenues for innovative biodegradation strategies and environmental stewardship.
## 4 Experiment and Its Simulation Environment
### Experimental Parameter Settings  
This experiment assumes that the autonomous vehicle can sense the 10 nearest environmental vehicles within 250 m before and after its position and accurately obtain the distance and speed information of these vehicles relative to the autonomous vehicle, which is used as an important part of the environmental state. The definition of its environmental state is Equations (33) and (34):  
\[\Delta\mathbf{s_{t}}=\left|\begin{array}{c}s_{t}-s_{t}^{environment}\end{array}\right| \tag{33}\]  
\[\Delta\
u_{t}=\left|\begin{array}{c}v_{t}-v_{t}^{environment}\end{array}\right| \tag{34}\]  
The policy update frequency is set to 1 Hz. The road in the experiment is divided into four lanes, and the surrounding environment contains 50 other vehicles. The discount factor \(\gamma\) is set to 0.95, indicating that future rewards decay exponentially, and the learning rate \(\alpha\) is 0.0002, which controls the step size of the state change in each update. Both the value network and the advantage network feature layers with 64 neurons each, defining the network's width. The training process lasts for 5000 rounds, during which \(\epsilon\) is gradually reduced from 1 to 0.05. As the training progresses, exploration is reduced, and action selection is based more on the currently learned strategy.  
The reward function \(r_{t}\) consists of the optimal control goal, which is to avoid collisions during driving and drive in the right lane as fast as possible. To achieve this control goal, the round reward function is set as shown in Equation (35):  
\[r_{t}=1-collision+0.4\times r_{v}+0.1\times(L-1) \tag{35}\]  
where \(collision\in\) [0,1] (0 means no collision, 1 means collision), \(r_{v}\) is the speed reward, \(r_{v}\in\) [24,30], and L is the lane position.
# Automated Personalized Loudness Control for Multi-Track Recordings
## 2 Materials and Methods
### Genetic Algorithm for Loudness Optimization
\[\mathcal{L}=-\frac{1}{N}\sum_{i=0}^{N-1}e_{i}=-\frac{1}{N}\sum_{i=0}^{N-1}\sum _{k=1}^{M}\text{MAE}\Big{(}y_{i}^{k},\hat{y}_{i}^{k}\Big{)}. \tag{5}\]
* Selection Process--we employ a 3-Way Tournament selection, ensuring diversity while favoring higher-fitness solutions. This means that a parent is extracted as the best solution from 3 random candidates. The best solution is always saved to the next generation using the Elitism method, and there are 4 mating parents for the crossover and mutation operations. * Crossover and Mutation--a tailored crossover strategy ensures optimal trait inheritance, while a controlled mutation process introduces necessary variability, preventing stagnation. Instead of a probability approach, we always apply a custom crossover operation between 2 parents. Each gene is selected from the 2 parents as the best one by comparing the gene error \(e_{i}\) at each track \(i\), using Equations (3) and (4). For the mutation operation, we use an adaptive mutation [27; 28] probability of 10% for the parent-crossing solutions and 1% for the elite chromosome. The customized genetic algorithm can represent a good solution for the loudness control problem because it finds a fixed set of coefficients as the best approximation for multiple songs. However, one of the problem with this approach is that there will always be the same values for the coefficients (unless retraining is demanded). Because of this, if the training songs are very different between each other, the genetic algorithm will try to find the best solution that can obtain the minimal fitness function, but this may not always be the best auditory experience when mixing the tracks with the corresponding coefficients.
## 2 Antenna Design and Analysis
### AMC Structure Design and Analysis  
The antenna works in the indoor environment, so it is indispensable to increase the radiation of the antenna in the +z direction to reduce the backward radiation. The AMC structure is used as the reflector plate to increase the radiation in the +z direction by using its characteristic of in-phase reflection. Both the PEC plate and the AMC structure can reflect \(-\)z direction radiation and enhance +z direction radiation, while the in-phase reflection characteristics of the AMC structure can significantly reduce the antenna profile and miniaturize the antenna. Figure 5a,b describe the corresponding impedance bandwidth and AR bandwidth with and without the AMC structure loaded. We can understand intuitively that the introduction of the AMC structure has little influence on the bandwidth,  
Figure 4: Distribution of the surface current on the antenna at 4 GHz in the phases of (**a**) 0\({}^{\circ}\), (**b**) 90\({}^{\circ}\), (**c**) 180\({}^{\circ}\) and (**d**) 270\({}^{\circ}\).  
Figure 3: \(S_{11}\) and AR curves for different antennas. (**a**) \(S_{11}\). (**b**) AR curves.  
Figure 2: Four reference prototypes in the design process of circular slot antenna. (**a**) Ant.1. (**b**) Ant.2. (**c**) Ant.3. (**d**) Ant.4.  
as only a certain frequency shift occurs. The reflected phase bandwidth of the AMC unit can be seen in Figure 5c, and we define the frequency band in the interval [\(-90^{\circ}\), \(90^{\circ}\)] as the in-phase bandwidth, centered on the reflected phase at 0 degrees. The air layer height, H3, has a large effect on the in-phase reflection bandwidth of the AMC structure, and a red shift occurs with the gradual increase of H3. The in-phase reflection bandwidth of the AMC covers the working bandwidth of the antenna. The antenna gain is significantly improved, as depicted in Figure 5d. The antenna has a good broadband performance but deteriorates in the 3.18-3.76 GHz range. In view of the above situation, four metal plates are added around the AMC structure. In the actual processing, copper plates are used. Vertical metal plates have no obvious influence on impedance bandwidth and gain performance. The surface current distribution of the metal plate is shown in Figure 6. By observing the current distribution, it can be seen that part of the electromagnetic wave radiated by the SP-fed wide-slit antenna above is coupled to the edge of the vertical metal plate and the ground floor, which increases the current path and achieves the purpose of widening the antenna axis ratio bandwidth.  
Figure 5: Influence of AMC structure on antenna performance and parameter scanning of air layer height. (a) S\({}_{11}\). (b) AR curves. (c) Air layer height H3. (d) Gain curves.  
Figure 6: Surface current distribution diagram of a metal plate.
# Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms
## 2 Materials and Methods
### Iterative Deconvolution  
Deconvolution is the process by which pressures are assigned for each gas considered to be present in the MS. It is one of the most widely used techniques in the reconstruction and analysis of samples obtained by spectrometry and spectroscopy in many different areas of study, including Raman spectrometry for pediatric diagnoses [26], time-resolved fluorescence spectroscopy [27], nuclear instruments MS [28] and spectrometry analytics [29; 30], among others.  
Deconvolution is an iterative process which starts with a pre-selection of candidate species in order to limit the computational cost [8]. The gas pre-selection can be done by calculating certain parameters derived from the profile studied combined with human expertise on the subject, which, taking into account these calculations, allows the selection of possible present gases. This could be considered to be a semi-automatic or assisted method, as human intervention is required. Another way to address this pre-selection is to leverage ML techniques, which could provide probability scores associated with the degree of presence of each gas. These probabilities combined with a selection threshold allow a fully automated gas pre-selection, where no input from human operators is needed, apart from choosing the threshold value. As explained in the previous section, and based on the accuracy results of the ML classifier, the latest was the selected methodology for this work.  
Once a pre-selection of species is completed, the deconvolution process can start. When all gas species contributing to a spectrum are estimated, the mass spectrum can be deconvoluted by iteratively varying the partial pressures through the entire range, species by species. This is done in small increments. At each increment, the mass spectrum is calculated, transformed into the normalized logarithmic scale, and the integral error (IE) between the calculated and pre-treated measured spectrum is determined [31].  
A full iteration process consists of several rounds of iterations. For each species, the partial pressures for each gas are always calculated in increments over the whole pressure range (partial pressure scan). Such a partial pressure scan may have one or several minima. The partial pressure, which presents the lowest error with respect to the target MS, corresponds to the best fitting partial pressure in the presence of the other species selected in the process so far at their corresponding partial pressure. Once a partial pressure value is selected for a gas, it is kept constant for the current scan. In this way, the error can only decrease or remain constant when sequentially adding species at their selected partial pressure. After a few rounds of iterations, the partial pressures of most species that are not present in the measured spectrum should be reduced to values that are close to the one that corresponds to the limit of detection of the ion current (referred to as cut-off limit). The whole process is further explained in [8].