esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Diagnostics
#### 2.2.1 Design of the Optical Access  
Further tuning of the optical setup is necessary, if the measurement technique is to be applied to high temperature and pressure conditions in order to obtain raw images of sufficient quality for post-processing. The first effect, which compromises the image quality when increasing the temperature level is a high percentage of completely blurred images like the one shown in Figure 4b. Obviously, the drastic image degradation will be a problem for the extraction of quantitative droplet data. Even though few acceptable images can be obtained at intermediate temperature, an appropriate counter measure is required, since it is crucial to evaluate a sufficient number of droplets to ensure statistical significance. This argument is supported by the fact that storage capacity limits the tolerable amount of recorded images.  
The cause of the image degradation are density gradients between the measurement volume and the camera. In particular, the temperature difference between the hot gas in the test section and the ambient air in the laboratory was found to be a major source of this problem. As a remedy, an adjustment to the optical access module has been introduced to ensure well-defined temperature boundary conditions at this interface. The design of the optical access module is illustrated in Figure 5. It consists of two quartz windows, which are separated by a water cooling module. A constant water  
Figure 4: Exemplary raw image at a distance from the atomiser of \(x=50\) mm and at a hot gas temperature of (**a**) \(T_{\text{hg}}=293\) K and (**b**) \(T_{\text{hg}}=773\) K.  
volume flow is applied to keep the modules' temperature at the outlet lower than 300 K while the hot gas reaches a temperature of up to 873 K. The safe operation of this system is ensured by directly connecting the water flow to the pressure of the hot gas. Hence, the pressure difference over the inner window was as low as 0.01 MPa, and the major stress due to the pressure difference to the ambience is imposed on the thick outer window.  
Air cooling was discarded since water cooling offers several advantages. Besides the higher heat capacity of water, the similar refractive index of water and quartz glass is advantageous. Additionally, at high hot gas temperatures, the strong absorption of infrared light by water becomes vital. By this means, the heat transfer to the ambience will be reduced and the Schlieren effect will be minimised. As a result, the quality of the recorded raw images is considerably improved, which is exemplarily shown in Figure 5a.  
Figure 5: Enhanced optical access module to adapt the measurement technique to high temperature conditions of the gas phase.  
Figure 6: Exemplary raw image at \(T_{\text{hg}}=773\) K (**a**) using only the water-cooled optical access module and (**b**) using the water-cooled optical access module and the astigmatism correction plate.
# Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
## 3 Results and Analysis
### Comparison with Previous Studies  
[PERSON] et al. [29] proposed an enhanced YOLOv3-based model for target detection. The model incorporates an up-sampling technique to increase the resolution of the feature map, which is initially downsampled by a factor of eight. This up-sampled feature map is then stitched together with a two-fold up-sampled feature map obtained from the output of the second residual block. Additionally, a target detection layer is constructed to fuse the features, resulting in a four-fold downsampling of the output. The model exhibits a notably elevated recall and average accuracy of detection in comparison to the original YOLOv3 model. However, despite exhibiting a 6.5% higher average accuracy compared to YOLOv3, the model fails to meet the engineering-related criteria necessary for its deployment on small terminals.  
[PERSON] et al. [30] introduced an enhanced R-CNN-based approach for detecting aerial targets. This R-CNN-based incorporates expansion-based accumulation, region amplification, local annotation, adaptive thresholding and consideration of the context. The proposed method addresses the limitation of the insensitivity of the Faster R-CNN [31] by improving its sensitivity towards targets, while also enhancing the detection speed accuracy. However, the precision of this approach fails to meet the demands of engineering applications. Additionally, the substantial size of the faster R-CNN model poses challenges for deployment on compact terminals even after optimization efforts have been made.  
We utilized our dataset comprising images containing trolley codes to both train and evaluate the YOLOv7 model that was proposed. Subsequently, a pruning algorithm was employed to remove redundant parameters from the model, thereby simplifying it without compromising its detection accuracy. The implementation of this approach resulted in a significant reduction of 32.92% in the total number of parameters when compared to the original model. Additionally, the optimized model exhibited a reduction in size by 24.82 MB compared to the original model, while achieving a mean average precision (mAP) of 99.24%.  
The detection method proposed in this research addresses the limitations of previous deep learning-based methods, including excessive model parameters, large size, slow detection speed, and low accuracy.
# Crossover Rate Sorting in Adaptive Differential Evolution
## 3 Results
### Numerical Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & \(\mathbf{10*D}\) & \(\mathbf{20*D}\) \\ \hline L-SHADE-RSP\_lm vs. & \(4/8/0\) & \(5/7/0\) \\ L-SHADE-RSP\_s & (27.64) & (26.19) \\ \hline NL-SHADE-RSP\_lm vs. & \(4/7/1\) & \(3/9/0\) \\ NL-SHADE-RSP\_s & (12.52) & (12.86) \\ \hline NL-SHADE-LBC\_lm vs. & \(6/6/0\) & \(5/7/0\) \\ NL-SHADE-LBC\_s & (35.73) & (29.54) \\ \hline L-NTADE\_lm vs. & \(4/8/0\) & \(3/7/2\) \\ L-NTADE\_s & (22.77) & (13.21) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Algorithms with crossover rate sorting and biased Lehmer mean, CEC 2022, Mann–Whitney tests and total standard score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & \(\mathbf{10*D}\) & \(\mathbf{30*D}\) & \(\mathbf{50*D}\) & \(\mathbf{100*D}\) \\ \hline L-SHADE-RSP\_lm vs. & \(1/28/1\) & \(7/22/1\) & \(7/22/1\) & \(5/23/2\) \\ L-SHADE-RSP\_s & (2.05) & (29.73) & (36.46) & (23.53) \\ \hline NL-SHADE-RSP\_lm vs. & \(2/27/1\) & \(1/28/1\) & \(1/28/1\) & \(0/24/6\) \\ NL-SHADE-RSP\_s & (12.63) & (4.82) & (\(-12.18\)) & (\(-22.63\)) \\ \hline NL-SHADE-LBC\_lm vs. & \(1/28/1\) & \(4/25/1\) & \(14/14/2\) & \(10/17/3\) \\ NL-SHADE-LBC\_s & (\(-12.23\)) & (28.68) & (51.67) & (46.90) \\ \hline L-NTADE\_lm vs. & \(0/29/1\) & \(1/23/6\) & \(1/21/8\) & \(5/14/11\) \\ L-NTADE\_s & (\(-2.67\)) & (\(-23.53\)) & (\(-32.82\)) & (\(-33.29\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Algorithms with crossover rate sorting and biased Lehmer mean, CEC 2017, Mann–Whitney tests and total standard score. The statistical test results are shown in Table 7; it can be seen that NL-SHADE-RSP and NL-SHADE-LBC algorithms did not perform well on this benchmark, as they were designed for CEC 2021 and CEC 2022 benchmarks. However, the L-SHADE-RSP_s was able to outperform the EBOwithCMAR and jSO in high-dimensional problems, despite losing several times in low-dimensional cases. As for the L-NTADE_s, its performance is also good, and it won against EBOwithCMAR and jSO in \(30*D\), \(50*D\) and \(100*D\), despite performing worse than L-SHADE-SPACMA. In the case of the CEC 2022 benchmark, shown in Table 8, the L-SHADE-RSP_s performed quite well, even in comparison with the best methods in the \(20*D\) problems, but showed worse results in \(10*D\), unlike NL-SHADE-RSP_s, which had worse performance. The NL-SHADE-LBC with crossover rate sorting showed similar performance compared to the top algorithms, and the L-NTADE_s was worse in the \(10*D\) case but comparable in \(20*D\). It is important to note here that the L-NTADE_s is capable of achieving high performance in both CEC 2017 and CEC 2022 benchmarks against different algorithms developed for these problem sets.
## 2 Materials and Methods  
This study is focused on the fungal melanins extracted from the mycelia of three type strains: _O. lascauxensis_ CBS131815 (=LX A1), _O. anomala_ CBS131816 (=LX CH40), and _O. tshauvstchae_ CBS100438. The protocols for melanin extractions, purification and acid hydrolysis were thoroughly described by [PERSON] et al. [19] as well as their elemental analyses and spectroscopic characteristics.  
For analytical pyrolysis, the melanin samples (1 mg) were deposited on small crucible capsules (Eco-cup, Frontier Labs., Fukushima, Japan). Pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) was performed under inert helium atmosphere using a double-shot micro-furnace pyrolyzer (Frontier Labs. model 2020 iD). The pyrolyzer was attached to a gas chromatograph model 6890N and to a single quadrupole mass selective spectrometer model 5973N (Agilent Technologies Inc., Santa Clara, CA, USA), as described elsewhere [20; 21].  
We used two different approaches, a direct pyrolysis at 500 \({}^{\circ}\)C, or alternatively, firstly, a thermal desorption performed by introducing the sample into a preheated micro-furnace set at sub-pyrolysis temperature of 300 \({}^{\circ}\)C. After 2 min the sample was moved to a cold region of the pyrolyzer and the compounds evolved directly injected into the GC-MS for analysis. After a first desorption chromatogram was acquired, the sample was again moved to the pyrolyzer hot area, now pre-heated at pyrolysis temperature of 500 \({}^{\circ}\)C, for acquiring a second pyrolysis chromatogram. For both shots, the chromatographic and spectra acquisition conditions were the same.  
A low polar-fused silica capillary column (Agilent J&W HP-5 ms Ultra Inert, of 30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m film thickness) was used for compound separation and helium, with a controlled flow of 1 mL min\({}^{-1}\), as carrier gas. The GC oven temperature was set at 50 \({}^{\circ}\)C for 1 min and then increased to 100 \({}^{\circ}\)C at 30 \({}^{\circ}\)C min\({}^{-1}\), from 100 to 300 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\), and then constant at 300 \({}^{\circ}\)C for the last 10 min. The mass spectra were acquired at 70 eV ionizing energy. Compound assignments were achieved by single-ion monitoring (SIM) and by comparison with mass spectra libraries (NIST11 and Wiley7).  
Thermochemolysis of the melanins with tetramethyl ammonium hydroxide (TMAH) was performed as described by [PERSON] [22]. The analysis of the thermochemolysis compounds was done using the same instrument previously described and under the same chromatographic and mass spectra acquisition conditions.
## 3 Biosynthesis and Function of Glandular Trichome-Produced Compounds  
The plant epidermal surface represents the first barrier for pathogens and arthropod herbivores [29] to overcome after arrival on a plant. Therefore it may not come as a surprise that trichome density is one of the main factors correlating with resistance to herbivory [26, 30]. The presence of trichomes is, however, not always beneficial for the plant, since trichomes may interfere with indirect defense by disturbing natural enemies of herbivores [26, 31]. Trichomes can contribute to plant defense in different ways. Non-glandular trichomes can physically obstruct the movements of herbivorous arthropods over the plant surface or prevent herbivores to reach the surface with their mouthparts [32, 33]. Moreover, arthropods may become entrapped in sticky and/or toxic exudates, such as acyl sugars or polyphenols, produced by glandular trichomes. Such polyphenols are quickly formed via oxidation when the contents from the glandular trichome heads are released as a result of insect-mediated rupturing of the glandular cuticle. The entrapped herbivores usually die as a result of starvation or of ingested toxins [34] or, in the case of small herbivores, of suffocation [35]. Alternatively, in some cases trichome-produced toxic compounds were found to be transported via the stalk to distal plant tissues, thereby increasing resistance of these tissues against plant attackers, as shown for pyrethrins in the plant pyrethrum (_Tanacetum cinerariifolium_). It appeared that such pyrethrins, produced by glandular trichomes on pyrethrum fruits, can be taken up by the seed and be transmitted to the seedlings, which lack glandular trichomes themselves, resulting in inhibition of fungal growth and of feeding by herbivorous arthropods [36]. Glandular trichomes, thus, function as important chemical barriers for plant parasites [30, 37]. The main classes of secondary chemicals that have been found to be produced in trichomes include terpenoids [38], phenylropenes [39] and flavonoids [40], methyl ketones [41], acyl sugars [42] and defensive proteins [37]. Although all of these compounds play a role in plant defense, both glandular and non-glandular trichomes may have many other functions as well, including attraction of pollinators [4, 43], protection against UV due the presence of flavonoids and other UV-absorbing compounds in trichomes [44, 45], temperature regulation [43, 46] and reduction of water loss [46]. Furthermore, the ability of some plants to tolerate high levels of metals is correlated with their ability to sequester these compounds in their trichomes, as shown for the rough hawkbit (_Leontodon hispidus_) [47], which can sequester calcium, and tobacco (_Nicotiana tabacum_) which is able to secrete cadmium and zinc via its trichomes [48].
## 3 Materials and Methods  
### Stimuli Design  
We designed four digital ads that had different topics (alcohol vs. smoking) and different content elements (text only vs. text with an illustration). Other design elements and visual attributes were consistent in all the ads, such as size, color, layout, and alignment. Different messages were used for different ad topics, but both consisted of two words that were presented with the same typeface and in the same type size. Translated in English, the text \"Alcohol harms.\" was used for the anti-alcohol message, and the text \"Stop smoking.\" was used for the anti-smoking message. Both ads contained a brief description of health-related issues that were directly associated with the topic. The brief description did not exceed more than three lines of text, similar to the conditions in previous studies [33]. The number of characters in the brief description ranged from 91 to 100, and the word count ranged from 18 to 19 words. Each ad took up 12.65% of the webpage area. The webpage was designed as a static page with a neutral color palette [38] and generic layout to reduce the effects of the color saturation and surrounding on the perception of the ad. Since the target group in this study was students, all textual content for the webpage was taken from the website of the University of Zagreb.  
The results from human-computer interaction research suggest that banner blindness is strongest for digital banners placed on the right side of the screen [39]. A study on teenagers' perception of advertisements [40] showed that ads in the left column have a positive effect on visual attention. Therefore, the ads in our study were positioned on the left part of the webpage.  
The illustrations used in those digital ads that contained the combination of text and illustration were originally created by one of the authors (M.M.), who made them particularly for the purpose of the study. The anti-alcohol ad contained an illustration with a person in a person in a bottle while the anti-smoking ad contained an illustration with a person in an ashtray. In creating the illustrations, the goal was to present dramatic, almost shocking visual representations of negative consequences arising from alcohol abuse and cigarette smoking. The designer sought to apply the same graphic style to both illustrations and to make their level of visual complexity as similar as possible. Both illustrations were horizontally oriented. Since previous research [30] suggests that attention to digital ads may vary depending on the viewers' familiarity with the advertised brand, the digital ads in our study contained an imaginary brand presented through a simple generic logo.
###### Abstract  
Background:Considering the critical role of physical activity (PA) beginning in early childhood education and the demonstrated validity and reliability of contemporary technological measurement tools, this paper aimed to systematically review and analyze studies comparing PA levels in preschool-aged children during weekdays versus weekend days, using objective technological devices, and highlight what factors correlate with children's PA. **Methods:** The search strategy was designed based on the PICOS framework. A systematic review was conducted using two databases (PubMed and Web of Science) to identify studies that included preschool children doing PA during weekdays, measured through technological devices, and compared to PA during weekend days. Study quality was evaluated using the MINORS scale. **Results:** From 1959 articles, 30 documents met the inclusion criteria, encompassing 32,251 preschool children. **Conclusions:** The results suggest that preschoolers were generally more active on weekdays than weekends, although it could depend more on contextual or individual factors than on the day of the week. In this sense, parental/maternal behavior (sedentary behavior, shared activities during weekend days, parents' educational level, and parental/maternal screen time) influences children's PA level, as well as other factors such as gender, morphology, motor competence level, the type of activity (indoor vs. outdoor), age, meeting PA guidelines, and the community transportation environment. Considering these factors, professionals working in preschools or kindergartens, as well as parents/mothers, should consider these factors to foster children's PA level from early childhood, which could influence children's lifespan.  
education; early childhood; kindergarten; sedentary; accelerometer licenses/by/4.0/. +
Footnote †: journal: Applied sciences
## 5 Quality Attribute Outcomes
### Gamification, Adherence, and Personalization
#### 5.3.2 Personalization  
Blockchain enhances monitoring and personalization by securely storing and analyzing user-specific fitness, health, and performance data. For example, wearable devices integrated with blockchain technology track physical activity and health metrics, tailoring fitness programs to individual needs. [PERSON] et al. describe how the SmartWalk system tracks older adults' walking routines in urban settings, recommending personalized walking routes and providing real-time feedback to healthcare providers [17]. Monitoring systems employ blockchain for tamper-proof data storage, enabling transparent and trustworthy progress tracking. For instance, [PERSON] et al. discuss rehabilitation platforms that track patients' recovery metrics and provide caregivers with accurate data for planning interventions [26]. [PERSON] et al. describe anomaly detection models integrated with a blockchain and the IoT that issue real-time alerts for irregularities in recovery, enabling timely adjustments to therapeutic plans [31].
# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 3 Results
### Developing a Combined Model for SV and Speech Assessment in Patients after  
_Laryngeal Uncosurgery_  
Regression analysis was employed in order to streamline a vast number of different results provided by the algorithm into a single numeric scale, which would be easier to interpret, and therefore more applicable in a clinical setting. Data from 309 speech recordings were used for the algorithm training. A linear stepwise backward regression was used to find the most reduced model which best explained the data, and to reduce suppressor effects that may have falsely impacted the significance of predictors. Possible predictors were probabilities and digital speech features provided by the algorithm, with the total INNFVo score chosen as the dependent variable. INNFVo was chosen as a dependent in order to make the model-based speech evaluation comparable to the INNFVo evaluation performed by the trained otorhinolaryngologist. The correlation between the observed and predicted values (R) was used to measure how well the regression model matched the data. The modified R2 was used to estimate the model's quality for the population while considering the sample size and number of predictors utilized.  
The starting variable formula was as follows: Class = PVF + PVS + AVE + PVFU + MD + MDc + Jitter + F0 + Probability the recording belongs to class 0 (Prob0) + Probability the recording belongs to class 1 (Prob1) + Probability the recording belongs to class 2 (Prob2). Out of all possible models, a model with the lowest Akaike Information Criterion (AIC) was chosen. A final model for assessment of SV quality, i.e., acoustic substitution voicing index (ASVI), consisted of the constant combined with statistically significant parameters (F0, AVE, Prob0, Prob1, and Prob2):  
\[\text{ASVI}=8.0518+29.534\times\text{AVE}-0.03\times\text{F0}-0.1876\times \text{Prob2}-0.172\times\text{Prob1}-0.0336\times\text{Prob0}\]  
The possible range of the ASVI was 0 to 30, with higher values indicating better overall speech quality. The model exhibited a correlation between the predicted and observed values (R = 0.922) with an adjusted R\({}^{2}\) of 0.842.
###### Abstract  
Numerous deep learning techniques have been explored in pursuit of achieving precise road segmentation; nonetheless, this task continues to present a significant challenge. Exposing shadows and the obstruction of objects are the most important difficulties associated with road segmentation using optical image data alone. By incorporating additional data sources, such as LiDAR data, the accuracy of road segmentation can be improved in areas where optical images are insufficient to segment roads properly. The missing information in spectral data due to the object blockage and shadow effect can be compensated by the integration of 2D and 3D information. This study proposes a feature-wise fusion strategy of optical images and point clouds to enhance the road segmentation performance of a deep learning model. For this purpose, high-resolution satellite images and airborne LiDAR point cloud collected over Florida, USA, were used. Eigenvalue-based and geometric 3D property-based features were calculated based on the LiDAR data. These optical images and LiDAR-based features were used together to train, end-to-end, a deep residual U-Net architecture. In this strategy, the high-level features generated from optical images were concatenated with the LiDAR-based features before the final convolution layer. The consistency of the proposed strategy was evaluated using ResNet backbones with a different number of layers. According to the obtained results, the proposed fusion strategy improved the prediction capacity of the U-Net models with different ResNet backbones. Regardless of the backbone, all models showed enhancement in prediction statistics by 1% to 5%. The combination of optical images and LiDAR point cloud in the deep learning model has increased the prediction performance and provided the integrity of road geometry in woodland and shadowed areas.  
Keywords:road segmentation; feature-wise fusion; LiDAR features; satellite images; U-Net; ResNet +
Footnote †: journal: Applied Sciences