esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Classification
### Classification of RL-Based Routing Approaches with Regard to Learning Process  
According to this classification, RL-based routing methods are centralized or distributed in accordance with learning process. Table 6 presents the advantages and disadvantages of centralized and distributed routing protocols.  
* **Centralized routing protocols:** In these protocols, RL algorithm is executed by a central agent, for example, a central server to obtain the optimal path. Then, the central agent loads this discovered route in the memory of the source node. This path is used for transferring data packets to the destination. The most important advantage of these routing schemes is to decrease communication overhead and computational costs because these methods do not need to exchange control packets between nodes. However, they have a major problem: These routing schemes may damage from a single point of failure. This means that if the central agent fails for any reason, the whole network will be disrupted. On the other hand, VANET is an extremely dynamic network and the central agent cannot adapt itself to topological changes in real-time. Furthermore, these methods have not scalability and are not suitable for large-scale VANET.
* **Distributed routing methods:** In these protocols, RL algorithm is locally executed on the network to learn the optimal path. In these methods, computational costs and communication overhead are greater than that in centralized routing approaches because vehicles must share local information about the network topology. However, these approaches are scalable and suitable for the dynamic network environment and real-time applications.
# Myths and Realities about Genetically Modified Food: A Risk-Benefit Analysis
## 2 Legislative Framework of Genetically Modified (GM) Crops
Hence, individuals rely on those people they perceive as being trustworthy experts to reach informed conclusions, which they might use to form their own views. Consequently, trust is critical in layperson evaluations of GM foods [24]. The first labeling legislation on GM foods was framed by the European Union (EU) in 1997. Later, more modifications were made to legislation on GMO derivatives [25]. In 2007, the Codex Alimentarius Commission made an attempt to produce labeling guidelines for biotech products, but it was prevented because no consensus was reached by different countries [26]. Nonetheless, an increasing number of countries have been involved in labeling with distinct regulatory characteristics [17]. At least 64 countries worldwide, such as the EU countries, China and Australia, expect some form of GMO labeling [24]. Policies and the extent to which they are adopted differ from one country to the next. The Codex Committee has addressed the costs incurred to implement and enforce labeling, as well as potential advantages. Test requirements, agricultural production traceability, processing and distribution, document verification, analytical method feasibility and detection limits, and consumer education are only some of the main concerns being addressed. It would appear unlikely that a worldwide agreement will be reached in the near future because the USA strongly opposes labeling. The US GM labeling position is based on the \"substantial equivalence\" (SE) premise [17]. This concept first appeared in 1993 and embodies the notion that if a novel food has the same composition and features as conventional food, then it must be taken as safe as conventional food [20]. Proponents of the SE principle state that requiring labeling is not necessary because customers are more concerned about health and safety, functionality, and food usage than about manufacturing processes. Mandatory GMO labeling gives an impression, and even serves as a warning, that these foods differ or are less safe than their non-GM counterparts. The EU takes a \"precautionary stance\". As the perceived or true risk of GMOs in long-term exposure remains unknown, labeling is vital for traceability considerations, along with consumer \"right to know\" legislation. According to the EU, it is no easy task to contemplate and measure the effects of these foods because the population has not been exposed for very long. Lack of evidence does not rule out the time-lag potential between exposure to health/environmental dangers and their consequences [17].
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 4 Experiments and Results
### Comparative Experiments
#### 4.4.2 Comparison on the ISIC2017 Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _Acc_ & _Dice_ & _Spec_ & _Sens_ & _mIoU_ \\ \hline UNet [9] & 0.9361 & 0.8555 & 0.9195 & 0.8563 & 0.7667 \\ UNet3+ [11] & 0.9419 & 0.8750 & 0.9569 & 0.9175 & 0.7983 \\ Atten UNet [12] & 0.9543 & 0.8745 & 0.9718 & 0.8716 & 0.7912 \\ UNetXt [38] & 0.9586 & 0.8873 & 0.9746 & 0.8723 & 0.8067 \\ REDAUNet & 0.9571 & 0.9029 & 0.9785 & 0.8971 & 0.8298 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of segmentation results on the ISIC2017 dataset. Figure 11: Comparison results on the ISIC2017 dataset. (**a**) Segmentation result from UNet; (**b**) segmentation result from UNet3+; (**c**) segmentation result from Atten UNet; (**d**) segmentation result from UNeXt; (**e**) segmentation result from REDAUNet. our method achieved a balance in metrics on the ISIC2017 dataset. Of the seven models compared, four of the five metrics we measured ranked second, except for the Spec metric, but the Spec metric also outperformed the vast majority of the compared models. In addition, our model has a very small gap with the better-performing FAT-Net, where the mIoU metric, which is the most important among the segmentation metrics, differs by 0.66%. In contrast, our Sens metric is 0.92% higher, indicating a better differentiation ability. All in all, the good performance and segmentation effect of our network model are proved by comparing it with advanced segmentation networks.
## 2 Materials and Methods
### Data Sources and Preprocessing  
A dataset of monthly precipitation and temperature data was extracted from climate sites of the National Meteorological Science Data Center ([[https://data.cma.cn/data/index.html](https://data.cma.cn/data/index.html)]([https://data.cma.cn/data/index.html](https://data.cma.cn/data/index.html)), accessed on 11 June 2020) for the period from 1986 to 2019, meeting the minimum requirements for SPEI calculation of at least 30 years [36], and the horizontal resolution was 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). In this study, we used 448 climate grid points of the Loess Plateau and its surrounding areas to calculate the grid point SPEI index with R software (SPEI package), and the results were interpolated into SPEI raster data with a resolution of 500 m \(\times\) 500 m by the ANUSPLIN package; the average SPEI index for each season from 2001 to 2019 was obtained, which could be used to characterize the drought of each season. Gross primary productivity (GPP) and evapotranspiration (ET) were derived from  
Figure 1: Map outlining the geographic location (**a**) and vegetation types (**b**) of the Loess Plateau.  
remote sensing data. MODIS GPP (MOD17A2H) and MODIS ET (MOD16A2) product data were from the United States Land Processes Distributed Active Archive Center ([[https://lpdaacsvc.cr.usgs.gov/appeears/task/area](https://lpdaacsvc.cr.usgs.gov/appeears/task/area)]([https://lpdaacsvc.cr.usgs.gov/appeears/task/area](https://lpdaacsvc.cr.usgs.gov/appeears/task/area)), accessed on 13 March 2020), the spatial resolution was 500 m, the time resolution was 8 days, and the study period was from 2001 to 2019. Preprocessing, including image mosaic, reprojection, and format conversion, was completed with the MODIS reprojection tool (MRT).
## 3 Results and Discussion
### Secondary Atomization Mechanisms
It was possible to identify some behaviors such as the increase of crown thickness with the increase of dimensionless thickness of the liquid film (Figure 10) and with the decrease of viscosity (Figure 11). The crown thickness also increased when the droplet diameter decreased, widely noticeable for H\({}_{2}\)O. It seemed that the impact of smaller droplets enhanced the number of splashed products. However, only quantitative measurement could confirm these assumptions. Furthermore, for all impact conditions considered in the experiments, splashing always occurred. This confirms that all Weber numbers obtained for this middle height were equal or superior to the critical Weber number setting the onset of splash. For the highest impact distance, the behavior was similar to the observed for the middle height, with crown splashing occurring for all impact conditions, and a few cases resulted in jetting for all the fluids. Bubble encapsulation was once spotted for the 75%/25% mixture. Similarly to \(z_{2}\), prompt splashing was only spotted for the mixtures. The assumptions made for the middle height (\(z_{2}\)) were also spotted for the highest impact (\(z_{3}\)). One of the major differences in the outcomes observed with \(z_{2}\) was the larger amount and size of ejected secondary droplets. Figure 10: The noticeable increase of the crown thickness while the dimensionless film thickness increases for 75% JF/25%, \(D_{0}=3.05\) mm, \(z_{2}=0.500\) mm, \(t=20\) ms: (**a**) \(\delta=0.1\); (**b**) \(\delta=1\). Figure 9: Final spread at \(t=80\) ms for \(h_{1}=0.175\) m, \(\delta=1\), \(D_{0}=2.8\) mm: (**a**) for H\({}_{2}\)O; (**b**) for 50% JF/50% HVO.
# Investigation of Vocal Fatigue Using a Dose-Based Vocal Loading Task
## 3 Participant Recruitment  
The human research ethics protocol (A09-M46-11A) was approved by the Institutional Review Board at McGill University. No occupational voice users such as singers, teachers, and voice actors were recruited, because previous studies showed that their voice had greater endurance for vocal loadings than normal voice users [1; 34]. The purpose of the study was not communicated to the participants before the experiment was concluded. The participants were only informed that they had to perform reading sessions. This was to help reduce the participants' biases towards the SAVRa ratings. For example, if the participants were aware that the study was measuring vocal fatigue, they would expect their voice quality to degrade throughout the sessions. This may increase the risk of biased ratings through the introduction of a psychological variable into the cross-session analysis of the participants' voice quality.  
Participants were recruited in Montreal, Canada. The inclusion criteria were that the participants should be female, native English speakers with no history of voice disorders. Sex and gender differences in voice performance were found to be significant by [PERSON] et al. [35; 36]. The present study only used female participants to exclude sex as a variable in the pilot study. Male participants will be used in future experiments. The participants' demographic information is shown in Table 1. The experiments were conducted in a sound-proof voice recording studio located in the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT). All experiments took place in the morning, around 9:00 a.m. The participants were instructed not to use their voice often over a period of 8 h before the experiments. The participants were required to withdraw from the study if they were found to have a voice problem, such as cough and cold, in the early morning. During the experiment, participants who reported any severe physical discomforts, such as unceasing cough and voice loss, were asked to withdraw from the recording session.
# Effect of Admixtures on Selected Properties of Fly Ash-Based Geopolymer Composites
## 4 Results and Discussion
### Bulk Density Results  
The average results of the bulk density for all the series are shown in Figure 7. All the series obtained similar bulk densities. However, a certain correlation can be identified. Mortars with admixtures are characterized by a decrease in the bulk density and an increase in the activator content.  
After flexural strength testing, all the samples were subjected to visual analysis. The series with a plasticizing admixture and an admixture based on modified polynaphthalenes were characterized by large pores visible in the cross-sections. The plasticizing admixture (AD2), according to the technical data sheet, causes an increase in volume by introducing micro air bubbles which was also observed in the samples. Thus, it was confirmed that this admixture works in a similar way in geopolymer mortars. Despite the use of a plasticizing admixture to activate the additives, i.e., fly ash and blast furnace slag, no results were obtained to conclude that the admixture activates the fly ash used to make the geopolymer composite.
## Appendix B Embedding Techniques Adopting Local Patches  
A local structure is described by a neighborhood graph associated with each patient's position \(\mathbf{x}_{i}\) in the initial feature space, which links the node representing the latter to its \(m\) nearest neighbors patients or to those satisfying \(d\left(\mathbf{x}_{i},\mathbf{x}_{j}\right)<\rho\), with \(\rho\) an input radius. This topological information is encoded in an adjacency matrix \(A_{ij}=1\) if for nodes \(i\) and \(j\) the presented proximity property holds true, otherwise \(A_{ij}=0\). A complementary graph is built to take into account the information associated with far apart nodes according to the adjacency matrix such that \(W_{ij}=1\) if \(A_{ij}=0\) and viceversa.  
The neighborhood graph structure becomes a weighted graph by assigning a gaussian weight \(W_{ij}=e^{-d\left(x_{i},\mathbf{x}_{j}\right)^{2}/\sigma^{2}}\) to each edge, as shown in the main text. The cost function is explicitly evaluated  
\[E=\sum_{i,j}d\left(\mathbf{y}_{i^{\prime}}\mathbf{y}_{j}\right)^{2}W_{ij}=2\sum_{i}{ \mathbf{y}_{i}}^{2}D_{ii}-2\sum_{i,j}\mathbf{y}_{i}\mathbf{y}_{j}\,W_{ij}=2\;Tr\left(Y^{T}L \;Y\right)\]  
where \(D_{ii}=\sum_{j}W_{ij}\) used to define the Laplacian matrix \(L=D-W\), \(Tr\) is the trace of a matrix and \(Y\) is a matrix with a patient in the relevant space \(\mathbf{y}_{i}\) in each row. In this setting the embedding yielded by the Laplacian eigenmap coincides with the solution of angrin \(Tr\left(Y^{T}L\;Y\right)\). The solution associated with DVE uses this solution as a constraint during the mapping of distant patients in the initial feature space as far as possible in the relevant space  
\[\underset{Y^{T}L\;Y=I}{\operatorname*{argmax}}Tr\left(Y^{T}L^{\prime}\;Y\right)\]  
with \(L^{\prime}=D/-W/\) the Laplacian of the complementary graph without weights [18].
## 3 Major Factors Affecting Enzyme Immobilization Efficiency  
Immobilization attaches enzymes to solid supports, forming a heterogeneous system that mimics the natural state of enzymes in living cells, where they bind to membranes or organelles. This enhances stability, making immobilized enzymes more resistant to environmental changes than are free enzymes [25]. Figure 1 shows a successful enzyme immobilization procedure, in which the following factors must be considered: substrate, carrier, enzyme, immobilization mechanisms, and carrier-enzyme interactions [26].  
Enzymes, substrates, and products exhibit diverse properties and applications, necessitating tailored immobilization strategies, each with distinct pros and cons. For instance, membrane reactor confinement, entrapment, and microencapsulation face diffusional constraints, whereas adsorption is simple, cost-effective, and efficient, but often reversible. In contrast, covalent attachment and cross-linking offer stability but may impair enzyme activity and incur higher costs [27].  
The optimal immobilization technique depends on carrier properties such as porosity, chemical composition, and surface area [28]. Both the enzyme and the support must maintain stability during immobilization while ensuring maximum interaction. The enzyme-support affinity ultimately dictates the choice of the biocatalyst, support material, and immobilization method for a given process.  
It is crucial to assess an enzyme's chemical and physical stability prior to immobilization, since variables including byproduct production, adhesion to the support material, and environmental changes can drastically impact how well the enzyme functions [26]. Furthermore, maintaining the enzyme's functioning requires protecting its active site and tertiary structure.  
Figure 1: Flow chart of parameters for enzyme immobilization.
## 2 Design, Fabrication and Characterization
### Fabrication Process  
The fabrication of the microstrip is shown in Figure 2. The substrate of the microstrip is based on a commercial silicone elastomer, PDMS (Sylgard 184, Dow Corning, Inc., Midland, MI, USA). Firstly, the silicone elastomer base (SEA) and curing agent (CA) were mixed with a weight ratio of 10:1, followed by a 15-min stirring and vacuum degassing process. The mixture was poured into a resin mold with a groove (60 \(\times\) 15 \(\times\) 1 mm\({}^{3}\)), cured at 80 \({}^{\circ}\)C and peeled off from the resin mold. The strip conductor was fabricated based on a tape-assisted lift-off process. The mask for the lift-off procedure was prepared by attaching invisible matte-finish tapes (Scotch Magic, 3M Company, Saint Paul, MN, USA) onto the PDMS substrate surface. A rectangular area (50 \(\times\) 2.7 mm\({}^{2}\)) was left blank for the strip conductor. The conductive AgMP/PDMS composite was prepared by mixing silver microparticle powder of 2 \(\upmu\)m in diameter (2 \(\upmu\)m AR grade, Nangong Xindun Alloys Spraying Co., Ltd., Beijing, China) and the same uncured PDMS mixture in a weight ratio of 9:1 with vast stirring. Then the AgMP/PDMS composite was spread onto the PDMS film surface by a glass stirrer of 7 mm in diameter. After it was peeled off the mask, the sample was baked on a hot plate at 150 \({}^{\circ}\)C for 15 min. Then, the sample was turned over to fabricate the ground plane. The AgMP/PDMS composite was spread and cured with the same procedure as the strip conductor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Length of microstrip, \(L\) & 50 mm \\ \hline Width of strip conductor, \(W\) & 2.7 mm \\ \hline Thickness of the strip conductor, \(T_{\mathrm{s}}\) & 0.14 mm \\ \hline Thickness of the PDMS substrate, \(T\) & 1 mm \\ \hline Dielectric constant of the PDMS substrate & 2.68 \\ \hline Loss tangent of the PDMS substrate & 0.0375 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The geometric parameters of designed microstrip.  
Figure 2: The fabrication process of the proposed microstrip. Preparing of (**a**) PDMS substrate, (**b**) Ag microparticles/PDMS (AgMP/PDMS) strip conductor, (**c**) AgMP/PDMS ground plane.
# Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products
## 6 Conclusions  
The research work carried out in this paper puts forth a comparative analysis of different pre-trained models combined with the ensemble of machine learning algorithms. The study iterates a layer-wise approach for monitoring anomalies in the printed component of a 3D printer. This study aimed to determine which combination gives maximum accuracy in lesser computational time. Density-wise classification using pre-trained models was also carried out. The following conclusions can be derived:  
1. A real-time dataset consisting of defective and non-defective 3D printed samples were created for this study. All the AI models were effectively able to perform anomaly classification on this dataset.
2. It was observed from the combination of the pre-trained models with the machine learning techniques that the combination of Alexnet with SVM technique gave the highest accuracy of 99.70%, followed by the combination of Alexnet with K-NN at 99.40%. The other pre-trained models also exhibited decent performance.
3. Further, the analysis of pre-trained models using ensemble learning was carried out to increase the system's accuracy. Alexnet outperformed other pre-trained models by providing 100% accuracy, followed by EfficientNet-B0 at 99.10%.
4. A separate parameter-wise density classification was performed, for which Resnet50 gave an accuracy of 100%.  
In the future, the authors propose to implement this fault classification framework on real-time condition monitoring data. This work can be further enhanced by applying explainable fault visualization algorithms to identify the anomalies on images itself. In addition, reinforcement learning can be used improve the model accuracy for finding very fine faults, which are difficult to detect by human eyes.  
Conceptualization, V.K. and S.K.; methodology, S.K. and S.W.; software, V.K. and S.K.; validation, A.B., P.K., and S.P.; formal analysis, S.K.; investigation, V.K.; resources, S.K. and A.B.; data curation, S.K.; writing--original draft preparation, V.K., S.K., and A.B.; writing--review and editing, P.K., S.W., S.P., and A.B.; visualization, A.B.; supervision, S.K. and S.W.; project administration, S.K.; funding acquisition, S.K., A.B., P.K., and S.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Symbiosis Institute of Technology, Symbiosis International (Deemed University) under Research Support Fund (RSF).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflict of interest.
## 3 Results
### Evaluation of C\({}_{4}\) Photosynthesis Routine at Mongu, Zambia  
The gross primary productivity (GPP) rates estimated by the C\({}_{4}\) photosynthesis routine in the DCHM-V are evaluated against the MODIS MOD17A2 GPP product and eddy-covariance flux tower data collected at the ZM-Mon tower (Figure 7). The DCHM-V estimates compare well against the tower observations, particularly during the dry season and green-up periods. The MODIS GPP product severely underestimates both the DCHM-V and flux tower throughout most of the year, and by a difference of more than 5 g C/m\({}^{2}\)/day during the peak of the growing season, and it is not able to capture the variability in amplitude and overall seasonality of photosynthetic activity in this region. The fact that MODIS underestimates productivity in African savanna and grassland ecosystems has been documented previously and attributed to the uncertainties in incoming radiation, the insufficiency of vapor pressure deficit as an indicator of water stress in the MODIS algorithm, differences in spatial scale between MODIS pixels and flux tower footprints and subgrid heterogeneities (e.g., [80; 81; 82]). Thus, the remainder of the results and the discussion regarding carbon and water fluxes in the UZRB will focus on the DCHM-V and will not consider the MODIS data as a result of poor performance in this region.  
Figure 6: Maps of wetland fractions for the days of minimum and maximum wetland extent displayed alongside the corresponding MODIS MCD12Q1 static wetland land cover class in 2005 and 2010. Topographic contours are displayed as black lines at every 100 m between 650 m and 1550 m.  
Figure 7: Time series of daily GPP in 2008 from MODIS MOD17A2 (square), CarboAfrica flux tower data (circle), and the DCHM-V estimates at the same location. Note the significant difference between MOD17A2 and the DCHM-V estimates. The location of the ZM-Mon tower within the UZRB is shown in Figure 2.