esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, the color, microstructure, acidity, sugars, phenolic compounds, antioxidant activity, 5-HMF, acrylamide and vitamin C content of fresh, solar-dried, oven-dried and two commercial _Royal Gala_ apple snacks were evaluated. The \(\Delta E\) and BI analyses proved the observable differences in the color of the studied samples, especially in commercial ones, which are significantly browner. Similar variations were observed in microstructure, with denser and more compact surfaces in CC and CF samples. The results indicated that apple snacks maintained some acidity, despite a slight increase in malic acid percentage in OD65\({}^{\circ}\), OD85\({}^{\circ}\) and CC samples. The drying process led to a concentration of sugars in the samples compared to fresh apples, observable in soluble solids content but not in individual sugar quantification. Antioxidant activity, in both DPPH and ABTS tests, shows the same tendency, with low values in dried snacks compared with fresh apple. Also, phenolic compound content decreased across all drying methods due to the drying process, temperature increases and processing time, particularly in SD samples, but the same compounds were obtained in all considered samples. 5-HMF, which is not present in fresh, SD and OD85\({}^{\circ}\), was found in OD65\({}^{\circ}\) and commercial samples. Due to its toxicity and potential carcinogenic activity, its content in food should be monitored. Acrylamide is a potential health concern due to its classification as a probable human carcinogen by the International Agency for Research on Cancer (IARC). The high variability of quantification values in commercial samples, especially CC, emphasizes the need for better control over processing conditions. The dried samples with undetected or lower acrylamide levels, such as SD, OD65\({}^{\circ}\) and OD85\({}^{\circ}\), are preferable for the consumer. Research in these areas is improving and continues to be assessed to minimize acrylamide formation, which should be mitigated.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15126711/s1](https://www.mdpi.com/article/10.3390/app15126711/s1)]([https://www.mdpi.com/article/10.3390/app15126711/s1](https://www.mdpi.com/article/10.3390/app15126711/s1)).  
L.F.: conceptualization; investigation; methodology; data acquisition and analysis; original draft preparation; review. J.S.: methodology; data acquisition and analysis; C.G.: validation and review. P.B.T.: conceptualization; resources; formal analysis; methodology; validation and review. F.M.N.: conceptualization; methodology; resources and review. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We would like to acknowledge CIDE-UME/UTAD, CQVR UIDB/QUI/00616/2020 and Food and Wine Chemistry Lab.  
The authors declare no conflict of interest.
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 4 Experiments and Result Analysis
### Comparison of the Proposed Method and State-of-the-Art Methods  
In this section, we compare the proposed 3D-SE-Net with state-of-the-art fatigue detection methods. In order to ensure the objectivity of the experiments, the methods we compared, [PERSON] et al. (2018), [PERSON] et al. (2019), [PERSON] et al. (2020), [PERSON] et al. (2020), are all based on the YawDD dataset. We train both Double-branch 3D-SE-Net (D) and Three-branch 3D-SE-Net (T). A 300-frame test set is constructed based on the YawDD dataset, the hyperparameter \(r\) is 16 and the driver's fatigue state is obtained through the SE module. The experimental results are shown in Table 3 and the method proposed in this paper outperforms the current state-of-the-art methods.  
Figure 10: The detection accuracy of each behavior under different hyperparameter \(r\) values.  
[PERSON] et al. [14] combined the residual network ResNet and the recurrent neural network DHLSTM to construct a driver fatigue detection system that copes with complex environmental disturbances. The introduction of recurrent neural network can solve the problem of gradient disappearance and gradient explosion during the training of long sequences, with efficiently fatigue detection accuracy, while the simple structure of the residual network makes the model work well in real time. The temporal problem is also taken into account in our model, and from the experimental results, the recognition accuracy of both models is approximately the same on the same dataset. [PERSON] et al. [15] proposed an improved bidirectional long and short-term memory deep neural network for the analysis of head acceleration data in three time series. They designed a new classifier called asda, where all fatigue features were determined by the head pose. Although the authors achieved a high accuracy in their experiments, we believe that better detection would be achieved if eye and mouth features were added to the model. It is worth mentioning that the RCAN proposed by [PERSON] et al. [16], a model that also includes an attention mechanism module (CBAM), uses the residual network to extract features of the human eye and mouth and the attention mechanism module to extract semantic information of the image, achieving good classification results. The attention mechanism allows the network to adaptively learn 'Which part is more important?' In our model, the 3D-SE-Net with the attention mechanism module also performs well, achieving an accuracy of 99.03% in the recognition of fatigue states.
# Recent Advances of MXene-Based Electrochemical Immunosensors
## 1 Introduction
[21] summarized MXenes-based electrochemical sensors, which included four immunosensors. [PERSON] et al. [22] summarized the recent progress of 2D MXenes nanomaterials in the construction of electrochemical enzyme-based biosensors, immunosensors and nucleic acid biosensors in 2021, which briefly discussed two immunosensors. However, the above-mentioned review articles only cover a small part of the work on MXenes-based electrochemical immunosensors, and do not provide a good generalization of the roles of MXenes in sensors, thus affecting the readers' comprehensive understanding of the field. Herein, we present the first comprehensive and systematic review on MXenes-based electrochemical immunosensors. First, we summarize the advances in the composition and structure of MXenes, as well as the properties of MXenes related to electrochemical biosensors. Then, we demonstrate different strategies of immunoassays and the corresponding roles that MXenes play in them. Finally, we also elucidate the current problems and propose suggestions for the future development of MXenes-based electrochemical immunosensors. We aim to inform readers of the recent advances of MXenes-based electrochemical immunosensors, and expect to provide helpful information for promoting the development of this field, so as to fabricate high-performance and practical electrochemical immunosensing devices for real-world human health, food safety and environmental monitoring.
## 3 Road-Based Multi-Metric Forwarder Evaluation Scheme  
The design of the Road-based Multi-metric Forwarder evaluation scheme for Multipath Video Streaming (RMF-MVS), which is based on multiple metrics including Vehicle Density of the Road (VDR), Vehicle Trajectory (VTJ), Hello Message Receive Rate (HmRr), Accessible Bandwidth Estimation (ABE) and Signal to Interference plus Noise Ratio (SINR) has been presented in this section. These metrics improve the selection of Next Forwarding Vehicle (NFV) for attaining quality video streaming delivery. The RMF-MVS scheme comprises of two stages, namely, information collection and video data forwarding stage. The information collection stage involves the exchange of information among neighboring vehicles via hello message. The video data forwarding stage performs video packet transmission from Source Vehicle Node (SVN) to the Destination Vehicle Node (DVN) based on the evaluated multi-metrics and dynamic self-weight score mechanism. The detailed discussion of the two stages is presented in Sections 3.1 and 3.2. Figure 1 represents the concept of the whole road-based multi-metric video streaming transmission in a multipath setting.  
Figure 1: Video Streaming Forwarding in Multipath Settings.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 3 AntOR-Based Protocol
### AntOR Phases
#### 3.2.2 Data Stochastic Routing  
The first route setup creates a unique path between the source and destination, as shown in the routing table. Other route discoveries and the route exploration, which is explained in the following section, originate multiple paths between the source and destination. This is carried out such that the data can be forwarded in mode multi-hop according to a probabilistic technique based on the routing tables. The strategy consists of making the data load expand through load balancing. This is important in mobile ad hoc networks because the wireless channel bandwidth is very limited.  
The data routing is given by the following equation:  
\[P_{in}^{d}=\frac{(\tau_{in}^{d})^{\beta_{2}}}{\sum_{j\in N_{i}^{d}}(\tau_{ij}^ {d})^{\beta_{2}}}\qquad\qquad\beta_{2}\geq 1 \tag{9}\]  
Equation (9) is similar to (1). The difference is in the exponential parameters \(\beta_{1}\) and \(\beta_{2}\).
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 5 Experiments
### Convergence Study  
The objective function of LGOMSC has multiple variables and constraints. We have developed an effective iterative optimization algorithm to solve the proposed method. We conduct the convergence experiment in Figure 5, which provides the convergence curves of LGOMSC on nine multi-view data sets. The x-axis displays the number of iterations,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & & **3 sources** & **100 leaves** & **BBC** & **Caltech101 COIL-20** & **NottingHill Webkb** & **Cornell** & **Wikipedia** \\ \hline \multirow{6}{*}{MVSCLG} & ACC & 78.70 & 73.38 & 80.88 & 82.56 & 78.61 & - & 91.82 & 70.77 & 63.49 \\  & NMI & 65.13 & 88.54 & 58.14 & 58.06 & 90.28 & - & 56.49 & 49.88 & 59.09 \\  & F-score & 76.13 & 66.10 & 69.95 & 79.07 & 72.94 & - & 88.04 & 65.40 & 56.16 \\  & ARI & 68.10 & 65.74 & 61.18 & 61.76 & 71.34 & - & 67.89 & 51.35 & 49.69 \\  & Time (s) & 59.77 & 947.71 & 209.86 & 764.76 & 434.21 & 4744.49 & 163.05 & 40.27 & 107.10 \\ \hline \multirow{6}{*}{Ours} & ACC & 83.43 & 94.31 & 88.47 & 79.85 & 92.08 & 100.00 & 98.38 & 64.62 & 62.63 \\  & NMI & 77.74 & 97.43 & 75.20 & 54.23 & 97.44 & 100.00 & 84.84 & 39.40 & 56.06 \\ \cline{1-1}  & F-score & 78.34 & 88.91 & 82.83 & 75.72 & 91.86 & 100.00 & 97.58 & 57.32 & 52.48 \\ \cline{1-1}  & ARI & 70.63 & 88.79 & 77.06 & 57.54 & 91.42 & 100.00 & 92.92 & 35.08 & 46.13 \\ \cline{1-1}  & Time (s) & 0.53 & 17.03 & 3.91 & 27.87 & 7.79 & 102.3 & 4.54 & 0.56 & 4.15 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparing LGOMSC with MVSCLG on nine multi-view data sets.  
Figure 4: The memory usage representation of compared methods on 3 sources.  
and the y-axis displays the corresponding objective function value. From these results, one can see that the proposed method is well convergent and converges quickly. Within \(10\) iterations, the objective function value can converge to a stable value.
## 6 Results
### Comparison of MC-M and MLMC-M  
Once we have a promising meta-model, it can be utilized by Monte Carlo methods. Based on the approach proposed in Section 5, we provide MC-M and two-level MLMC-M in comparison with the standard MC. Both are compared in terms of computational cost and the quality of estimations of moments. The number of samples \(N_{l}\) were calculated by formulas presented in Section 4.1 with \(V_{t}=10^{-5}\), \(R=25\). The efficiency of Monte Carlo methods with a meta-model compared to Monte Carlo methods without a meta-model is expressed as computational cost savings CS[%] calculated as CS = 100(1 - \(\frac{C_{M}}{M}\)).  
Figure 4 shows the computational cost (\(Cost(sec)\)) and moments error (\(J(\hat{\mathbf{\mu}},\hat{\mathbf{\mu}})\)). The cost is measured as the CPU time of simulations, including auxiliary processing and time needed for meta-model learning. The moments error has the form of MSE: \(J(\hat{\mathbf{\mu}},\hat{\mathbf{\mu}})=\frac{1}{S}\sum_{s=1}^{S}\|\hat{\mathbf{\mu}}^{s} -\hat{\mathbf{\mu}}^{s}\|_{2}^{2}\), where \(\hat{\mathbf{\mu}}\) represents the moments estimated by the standard MC, whereas \(\hat{\mathbf{\mu}}\) are moments calculated by either MC-M or MLMC-M. As expected, the computational cost increases with the precision of a model expressed as a number of mesh elements. As you can see, the cost can be greatly reduced (CS \(\approx\) 87%) by using MC-M instead of MC. However,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Mesh Size**} & \multicolumn{2}{c}{**Accuracy of Meta-Model (\(J_{\mathcal{L}}\))**} \\ \cline{2-3}  & **Deep Meta-Model** & **Shallow Meta-Model** \\ \hline
53 & 0.779 & 0.945 \\
115 & 0.799 & 0.949 \\
474 & 0.763 & 0.885 \\
2714 & 0.773 & 0.896 \\
10,481 & 0.799 & 0.898 \\
18,397 & 0.792 & 0.954 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of accuracy of meta-models for models on different mesh sizes.  
the meta-model error causes poor estimates of moments: \(J(\hat{\mu},\bar{\mu})\approx 10^{-3}\). On the contrary, MLMC-M suppresses the effect of the meta-model error, and estimates of moments are obtained with \(J(\hat{\mu},\bar{\mu})\approx 10^{-5}\). The cost savings are not so substantial but still significant: CS \(\approx 25\%\). Generally, using MC-M can be reasonable if we have an exceptionally accurate meta-model or we intend to obtain just a basic idea of the moments and PDF in a short time. Otherwise, using the MLMC-M is recommended.
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.2 LAI Field Measurements  
The LAI observations were collected with the LAI-2200 vegetation canopy analyzer from LI-COR, USA, and the collection was conducted mainly on cloudy days or the collection times were concentrated between 07:00 and 10:00 a.m. and 04:00 and 07:00 p.m. to avoid errors due to direct sunlight. In this study, data were collected between 24 July 2019 and 24 August 2019, and 139 random sample plots of plantediferous pure forests with a sample plot size of 30 \(\times\) 30 m were selected for measurement. In order to facilitate accurate matching between the actual measurement points and remote sensing images, real time kinematic (RTK) was used to locate each random sample location in the experiment. Four points were evenly selected for observation at each sample site location and within 10 m of the surrounding area, and five LAI data were obtained and averaged as the observation results of one sample site. Between 10 August 2020 and 22 August 2020, 47 of the 139 samples measured in 2019 were randomly selected for repeated measurement, and the measurement method was consistent with that in 2019.  
Field LAI time-series measurements of the study area were conducted in 2019 using LAI-2200, with four sample plots. The size of each sample plot was 30 \(\times\) 30 m, and the data were collected between May and October, 2019. Field measurements of the sample plots are shown in Figure 3. Coordinates of the time-series LAI sample plots are shown in Table 2.  
Figure 3: Field measurements of the sample plots. (**a**) The location of the sample plots; (**b**) LAI field measuring method.
## 3 Information-Centric Networking
### Named Data Networking: Basics  
NDN is the most prominent architecture within the broader ICN paradigm [30]. It relies on two primary communication primitives, namely Interest packets and Data packets. Clients, e.g., data/service consumers, send an Interest packet specifying the name of the desired content or service. This packet propagates through the network until it either (i) reaches a provider node that can satisfy the request (e.g., a content source, a service executor, or a caching node) or (ii) times out or (iii) an intermediate receiver sends back a negative acknowledgment. When Interests reach the provider node, Data packets are returned along the reverse path established by the Interest. Data packets can carry static contents or service results and they are cryptographically signed by the provider to ensure authenticity and integrity. This request-response mechanism eliminates the need for end-to-end connections and allows for dynamic retrieval of content from multiple sources.
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
## 2 Materials and Methods
### Instrumentation  
**Eddy covariance measurements**  
Two eddy covariance systems provided data to this study. The above canopy system was installed initially at a height of 44 m and on 5 th December 2018 moved to 48 m above ground. The second system was installed below the canopy at a height of 3.5 m above  
Figure 1: Map of Czech Republic (**left**), the study site Lanžhot with the location of the EC tower (**middle**) and the view of the floodplain forest ecosystem from above (**right**).  
ground. Both systems consisted of a Gill HS-50 sonic anemometer (Gill Instruments Limited, Hampshire, UK) for measuring the horizontal (\(u\), \(v\)) and vertical (\(w\)) wind components as well as sonic temperature (T\({}_{\mathrm{s}}\)), and an LI-7200 (LI-COR Environmental, Lincoln, USA) gas analyzer for detecting H\({}_{2}\)O and CO\({}_{2}\) mixing ratios (dry mole fraction). The sampling frequency of both systems was 20 Hz.  
**Environmental measurements**  
The instrumentation used in our study (Table 1) follows the standards of the Integrated Carbon Observation System (ICOS) which provides high-quality long-term observations of greenhouse gases (GHG) and greenhouse gas exchange [[https://www.icos-cp.eu/](https://www.icos-cp.eu/)]([https://www.icos-cp.eu/](https://www.icos-cp.eu/)) (accessed on 5 March 2022) [37; 38; 39; 40; 41]. Vapor pressure deficit (VPD), later on used in the course of this study, was calculated using T\({}_{\mathrm{air}}\) and RH measurements.
# Nanomechanical, Structural and Antioxidant Characterization of Nixtamalized Popcorn Pericarp
## 3 Results and Discussion
### Antioxidant Activity
#### 3.4.3 Hydroxyl Radical  
The results of OH radicals are shown in Figure 4. The activity of the pericarp is compared with the activity shown by Trolox, caffeic acid, ferulic acid, and coumaric acid (0.1M). The pericarp exerts OH radical scavenging activity of 51% compared to the Trolox (0.1 M), 36% compared to caffeic acid (0.1 M), 53% compared to coumaric acid (0.1 M), and 55% compared to ferulic acid. The activities showed by Trolox, caffeic acid, ferulic acid and coumaric acid are significantly different (\(p\) < 0.05). A high positive correlation (0.809) between total phenolic content and hydroxyl radical (OH) indicates that phenolic compounds catch radicals free [35]. [PERSON] et al. [39] reported OH radical scavenging activity values in cereal brands, wheat bran with 79.2% and oat bran with 41.3%, while in another study, protein hydrolyzates from corn gluten meal reported hydroxyl radicals of around 20 to 100% [40]. A study on bonito (kasuto) from Japan reported similar values of around \(54\pm 1\) with ascorbic acid and garlic (88 \(\pm\) 4), and onion (89 \(\pm\) 4) had higher values than popcorn pericarp [41]. These show that the pericarp has a significant OH radical scavenging activity and can be applied in different industries (food, nutraceutical and cosmetic) as a good source of antioxidant compounds.  
Figure 3: Percentage of reducing power of Pericarp comparison with Trolox, caffeic acid, ferulic acid, coumaric acid (0.1 M). Values reported as percentage (%). Values presented are the average of three replicates \(\pm\) standard deviation. Different letters indicate that the values are significantly different (\(p\) < 0.05).
## 3 Results and Discussion
### Morphology Analysis  
#### 3.2.1 SEM  
The surface morphology and particle size of both the MIP and NIP were examined using SEM, as shown in Figure 5. The surface of the control polymer (NIP) was found to be smoother than that of the MIP, as it did not have the target template to form the desired cavities. Smaller pores are observed in the NIP images due to the polymerization process, which can create minor defects that are non-binding specific to create this porosity. Other factors, such as the solvent and cross-linking effect density, can contribute to the porosity of the NIP, as when these solvents are being evaporated, they might leave a void. The MIP, on the other hand, had a rough surface once the templates were removed. These rough surfaces can be attributed to the formation of cavities during the synthesis process and washing out of the template process; when these templates are removed, they form their desired specific binding pores (cavities) [36; 37]. A prior study found that the roughness of MIP particles might result in a higher surface area than the control polymer [38]. As a result, the MIP outperforms the control polymer in adsorbing analytes of interest.  
Figure 4: FTIR spectrum of the MIP, NIP, and the functional monomer 2-vinylpyridine (2-VP).