esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 2 Materials and Methods
### Apple Feature Extraction
#### 2.4.1 Noise Processing  
When high-definition cameras capture images of apples, they ensure the clarity of apple details but are also susceptible to minor noise from the natural environment, which can hinder the subsequent extraction of apple features. To address this issue, this paper proposes a noise processing method that combines median filtering [19] and bilateral filtering [20]; its name is the MB Filter. The MB Filter algorithm rearranges each pixel and its neighborhood in the image, selects the median pixel for replacement, and combines it with a Gaussian-weighted filtering method in both the spatial and color domains, as described in Equations (7) and (8).  
\[I_{med}\;(p)=med(\{I(q)|q\in N(p)\}) \tag{7}\]  
\[G_{med}\;(p)=\frac{1}{W_{p}}\sum_{q\in N(p)}I_{med}\;(q)\cdot f_{s}\;(\parallel p -q\parallel)\cdot f_{r}\;(\parallel I_{med}\;(p)-I_{med}\;(q)\parallel) \tag{8}\]  
where \(I_{med}\) and \(G_{med}\) represent the median filter and the bilateral filter, \(f_{s}\) and \(f_{r}\) denote the Gaussian functions in the spatial and color domains, \(W_{p}\) is the normalization factor, and \(N(p)\) indicates the neighborhood of pixel p.  
Figure 6: Apple feature extraction scheme.  
The MB Filter algorithm first uses median filtering to suppress noise to the greatest extent possible and then applies bilateral filtering to adaptively smooth the noise regions in the image. The further processes ensure that the apple's contours remain clear and that important detail information is not lost.
# Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
## 5 Computational Studies
### Routing Solutions  
Figure 2 demonstrates the results produced by the RL sampling approach on the first instance on a graph with 10 nodes from [28] with \(\beta=0.6\). As can be seen from the solutions, drones' utilisation for customers is limited due to the small flying range corresponding to \(\beta=0.6\), where drones and trucks travel together to serve customers. In the case involving two trucks, Figure 2b,c show how the RL approach naturally solves the clustering problem of splitting the networks into two groups even though it has not been explicitly outlined in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \(|\mathcal{K}|=\mathbf{1},|\mathcal{D}|=\mathbf{2}\) & \(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{1}\) & \(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{2}\) \\ \cline{3-8} \(\beta\) & **Methods** & **Costs** & **Gaps, \%** & **Costs** & **Gaps, \%** & **Costs** & **Gaps, \%** \\ \hline \multirow{2}{*}{1} & RL Greedy w/t group LSTM & 234.42 & 7.26 & 209.64 & 6.83 & 214.99 & 13.79 \\  & RL Greedy & 232.24 & 6.24 & 205.70 & 4.77 & 198.47 & 5.14 \\ \hline \multirow{2}{*}{2} & RL Greedy w/t group LSTM & 185.81 & 4.75 & 170.71 & 2.40 & 137.63 & 4.71 \\  & RL Greedy & 185.42 & 4.50 & 168.40 & 0.98 & 136.06 & 3.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation studies on \(|\mathcal{N}|=10\) comparing RL Greedy models with and without the LSTM group updates. The averages of 10 instances were reported for costs and gaps. The relative gap is computed for each instance with respect to the best model result among all benchmark models.  
the MDP problem formulation. Instead, based on trial and error with the RL training, the proposed neural network architectures learn implicitly to cluster nodes to be served by two separate groups while ensuring the minimization of costs. Also, Figure 2 illustrates that increasing the number of drones doesn't always improve the total serving time, given the small number of customers to serve and the small flying range of drones.
## 3 Results and Discussion  
### Characteristics of Pre-Ensiled ML  
The characteristics of the pre-ensiled ML are shown in Table 1. The DM content of ML was 374 g/kg fresh matter (FM). The relatively high DM content of ML is comparable with that of tropical legumes, such as alfalfa and stylo [21]. The crude protein content of ML was 139 g/kg DM. It was lower than the 188 g/kg DM reported by [PERSON] et al. [2]; this might be because it was at the mature stage [22]. Neutral detergent fiber is an inferior predictor of digestibility [7]. The low neutral detergent fiber content (440 g/kg DM) indicated that ML probably had better digestive efficiency as a fodder for animals. The water-soluble carbohydrates were a necessary fermentation substrate during the ensiling process and its content of greater than 60 g/kg DM was a guarantee of thorough fermentation [19]. Water-soluble carbohydrate content was 114 g/kg DM in this study, which was enough to support the acid production efficiency of microorganisms. Epiphytic lactic acid bacteria and coiform bacteria counts were 5.72 log10 cfu/g FM and 5.21 log10 cfu/g FM, respectively. Yeasts and molds were below detectable levels (2.00 log10 cfu/g FM). During the ensiling, lactic acid bacteria counts of more than 5.00 log10 cfu/g FM are considered to be a superior predictor of fermentation quality [23]. However, the relatively high coliform bacteria counts might result in poor fermentation, which suggests that fermentation inhibitors were necessary to inhibit undesirable microorganisms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Mulberry Leaves \(\pm\) SEM** \\ \hline Dry matter (g/kg FM) & 374 \(\pm\) 12.8 \\ Crude protein (g/kg DM) & 139 \(\pm\) 1.9 \\ Neutral detergent fiber (g/kg DM) & 440 \(\pm\) 35.7 \\ Acid detergent fiber (g/kg DM) & 353 \(\pm\) 24.8 \\ Water soluble carbohydrates (g/kg DM) & 114 \(\pm\) 3.2 \\ Lactic acid bacteria (log10 cfu/g FM) & 5.72 \(\pm\) 0.1 \\ Coliform bacteria (log10 cfu/g FM) & 5.21 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical composition and microbial population of pre-ensiled mulberry leaves.
## 3 Results and Discussion  
### FT Characterization of Algal Extracts  
The FTIR spectrum (Figure 1) determined the active components' functional groups according to the values of the infrared absorption bands. The absorption peaks at 3346.5464 cm\({}^{-1}\) and 3435.0658 cm\({}^{-1}\) resulted from -OH stretching of the aromatic rings, and the presence of alcohol and phenol groups [45]. A peak at 2933.1506 cm\({}^{-1}\) represented the stretching vibration of C-H of alkenes and ether-me (isothiocyanate) [46]. There were amide I bands at 1659.3297 cm\({}^{-1}\) (C=O stretch of the ester group), amide II bands at 1536.7124 cm\({}^{-1}\) (N-H bending), 1444.9020 cm\({}^{-1}\) (C-H asymmetric bending in CH\({}_{2}\) and CH\({}_{3}\) groups), and bands at 1118.1212 cm\({}^{-1}\) and 852.7263 cm\({}^{-1}\) resulting from aliphatic amines and carboxylic acids, respectively, in the lower wavelength range [47]. The absorption bands at 613.5960-789.6294 cm\({}^{-1}\) (Phaeophyta and Chlorophyta) showed stretching of C=S, indicating the presence of sulfides in the tested algae [48]. Based on these findings, _U. lactuca_ was discovered to contain a wide range of phytochemicals, including phenol, alcohol, lipid, proteins, fatty acids, and other anticancer and antiviral compounds. Furthermore, these phytochemicals may aid in the conversion of sodium selenite to SeNPs [15; 49].  
Figure 1: FTIR spectra of _Ulva lactuca_ aqueous extract.
## 5 Swarm Ne Disturbances during Nighttime of the 2016-2017 Winter and M5+ Earthquakes
The remaining question is if a latitudinally oriented active tectonic plate boundary at higher latitudes can contribute to Ne disturbances at Swarm altitudes. To analyze this issue, the Aleutian Islands are selected additionally, as this region includes a subduction boundary that is directed orthogonally to the Swarm orbit, similarly as in the Papua New Guinea region (Figures 2c and 3c). The winter of 2016/2017 is selected here, as was also the case for Papua New Guinea and Africa. The Swarm A/C and B nighttime tracks are selected and processed in the same way as previously selected data, and the Swarm Ne PSDs at 300 km are summarized in Figure 9. The magnitudes of Swarm Ne PSDs are lower here compared with the equatorial region, probably due to the generally lower electron concentration. The cumulative sum of seismic energy is also lower due to the lower seismic activity at that time. Therefore, both quantities are multiplied here by six in comparison to Figures 7 and 8. After multiplication by six, we can see at least three epochs of correlation of Swarm disturbances with the earthquake record at this tectonic plate boundary (Figure 9). The Swarm Ne PSD, as well as its moving average, is most often increased in Swarm A, B, and C signals between 10 and 20 of November, when the seismic activity is also the largest (Figure 9a-c). The second highest disturbance in Ne signal of all three Swarm spacecrafts occurs in the first week of December, together with the shallow earthquake on 1 st December. The other interesting disturbances are recorded by Swarm A and C on January 18 and coincide exactly with the other shallow earthquake. It is worth noticing that the number of extreme 10-day average ap or Dst values does not correspond to the number of Swarm Ne PSD increases. Instead, the Swarm Ne PSD increases always approximately correspond to the groups of earthquakes or single shallow earthquakes, which is most evident in Figure 9b. Figure 8: The same as in Figure 7, but for the Africa region of more quiet seismic conditions. Subfigures titles (**a–c**) refer directly to individual Swarm satellites. The earthquakes shown along the time axis have M \(\geq\) 5.0.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Areas of Application
#### 2.2.4 Education  
In general, the Metaverse presents a promising opportunity for students to access a wide range of tools and engage in experiments that may be deemed unsafe in the physical world, which is particularly relevant when considering experiments involving hazardous chemical reactions. Furthermore, the Metaverse has the potential to provide a solution for the education of disabled children. Specifically, using avatars to simulate sign language human gestures can facilitate the learning process for children with hearing impairments. By leveraging the Metaverse, we can potentially improve the accessibility and safety of educational environments for a diverse range of students.  
In contrast to the Metaverse, MR offers a unique opportunity to enhance education by blending the physical and virtual worlds, providing students with interactive experiences that bridge both realms. Through MR, students can engage with digital simulations of complex experiments, such as molecular chemistry or engineering processes, while maintaining a connection to their physical environment and ensuring safety without sacrificing the hands-on aspect of learning. For instance, students can manipulate virtual hazardous materials in real-time without any risk, making MR a valuable tool for conducting experiments that would be dangerous or impractical in a traditional classroom.  
Moreover, MR holds significant potential for inclusive education, particularly for students with disabilities. It provides immersive tools like holographic projections and gesture-based interactions that facilitate learning. For example, MR allows hearing-impaired students to engage in sign language lessons through holograms or avatars that accurately replicate human gestures in their real-world environment. This approach not only supports the needs of students with disabilities but also fosters a more dynamic and engaging learning experience for all. By harnessing the power of MR, we can create safer, more accessible, and highly interactive educational environments that accommodate diverse learning needs.
## 7 Outlook and Future Research  
The current research work has shown promising results, and certain topics have been identified which require further research. More specifically, future research is needed in the context of enhancing the obtained results:  
* Use the rendered knowledge for the 3D printing of auxetic structures with metallic alloys by means of powder bed fusion for lightweight energy absorption applications and for human bone implants with adapted stiffness properties;
* Implement material properties in the finite element model derived from the experimental mechanical characterization of samples created by the particular 3D printing process, i.e., FDM or SLS, with specific process parameters, in order to replicate irregularities and possible anisotropy in the real built-up material;
* Enhance the finite element model definition to include contact conditions between and within the auxetic unit cells, plasticity behavior, and fracture criteria;
* Perform experimental testing at higher impact speeds to characterize the auxetic behavior at high strain rates.  
Conceptualization, D.P. and L.P.; methodology, D.P. and L.P.; software, D.P. and L.P.; validation, D.P., S.A., F.S., F.V., O.J. and L.P.; formal analysis, D.P., S.A., F.S., F.V. and O.J.; investigation, D.P., S.A. and L.P.; resources, D.P., S.A., F.S., F.V., O.J. and L.P.; data curation, D.P. and S.A.; writing--original draft preparation, D.P., S.A., F.S., F.V., O.J. and L.P.; writing--review and editing, D.P. and L.P.; visualization, D.P. and S.A.; supervision, L.P.; project administration, D.P., S.A. and L.P.; funding acquisition, D.P., S.A., O.J. and L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission's Horizon 2020 research and innovation program under the grant number 768775.  
This research was funded in the frame of the AMable Project by the European Commission's Horizon 2020 research and innovation program under the Grant agreement 768775. Project website: [[http://www.amable.eu](http://www.amable.eu)]([http://www.amable.eu](http://www.amable.eu)) (accessed on 30 October 2021).  
The authors declare no conflict of interest.
# Seismic Isolation Effect of Non-Water Reacted Two-Component Polymeric Material Coating on Tunnels
## 5 Conclusions  
The main conclusions are as follows:  
An impact resonance test is a reasonable and effective method to gain a damping ratio and dynamic elastic modulus of NRTCPM. When NRTCPM's density is approximately 0.2 g/cm\({}^{3}\), the average damping ratio is 0.0375, and the average dynamic elastic modulus is 359.025 MPa from the results of IRT. In future studies, IRT can be used to gain the damping ratios and dynamic elastic moduli of other densities' NRTCPM.  
The NRTCPM is found to have good capabilities to reduce the seismic demand and attenuate the seismic load. It offers superior capabilities to protect tunnels under seismic load.  
The compressive and tensile strength of NRTCPM increases as its density increases. The seismic response of NRTCPM is highly influenced by its density, elastic modulus, Poisson's ratio, and thickness. To be specific, the tensile stresses and absolute values of the compressive stress on a NRTCPM layer can be reduced by about 90% as the density decreases from 0.54 g/cm\({}^{3}\) to 0.17g/cm\({}^{3}\) and the elastic modulus from 228 MPa to 17.2 MPa, and the tensile stresses and absolute values of the compressive stress on the lining can be reduced by 75%. When the density decreases, the elastic modulus decreases at the same time and the Poisson's ratio increases steadily. By retrofitting NRTCPM layers from 5 cm to 40 cm, the tensile stresses and the absolute values of the compressive stress on the NRTCPM layer and lining can be reduced by 80% on average.  
The NRTCPM layer properties, including the layer density, thickness, dynamic elastic modulus, and Poisson's ratio, are determinative factors for the seismic isolation effects of tunnels. Ultimately, the seismic isolation effect performs better at a lower density, smaller elastic modulus, and higher Poisson's ratio and thicker layer. The influences of these parameters are all significant for seismic isolation. The optimum density is 0.2 g/cm\({}^{3}\) and thickness is 20 cm under the consideration of cost and strength.  
Conceptualization, C.G. and X.M.; data curation, X.M. and B.S.; writing--original draft preparation, X.M.; writing--review and editing, C.G. and X.M.; funding acquisition, C.G. and F.W. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Tensile stress of eight points on the NRTCPM layer.  
**Funding:** This research was funded by University-Study-Research Cooperation Program of Henan Province, grant number (172107000032); Key Research-development and Transformation Project of Tibet Autonomous Region, grant number (XZ201801-GB-07)  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
The COVID-19 pandemic showed that intensive care units in many countries were not prepared to deal with large numbers of patients requiring ventilatory support during such a mass casualty incident [1, 2]. The huge increase in demand for mechanical ventilators at the onset of the pandemic led many teams around the world to respond with the idea of developing a mechanical ventilator that could be easily and quickly manufactured [3]. From the beginning, it was evident that many individuals were eager to contribute; however, a comprehensive understanding of the principles of mechanical ventilation was often lacking. Numerous innovative concepts emerged, but they were not suitable for standard clinical practice, as their use could potentially cause more harm than benefit to patients [4]. The Medtrronic company even released documentation under a permissive license for their ventilator Puritan Bennett 560 [5], but a potential problem was the lack of parts on the market as countries banned the export of critical equipment and pneumatic components [6]. Some research teams have attempted to increase ventilator capacity by using a single ventilator to support multiple patients [7; 8; 9; 10]. Many teams have tried to improve manual resuscitators (bag-value-masks, sometimes referred to as Ambu-bags) [11; 12; 13; 14], based on the so-called MIT design [15].  
Out of hundreds of teams of experts and enthusiasts, only a few dozen succeeded in designing a ventilator that at least met the minimum safety and efficacy requirements of the U.S. Food and Drug Administration (FDA), World Health Organization (WHO), or UK Medicines and Healthcare products Regulatory Agency (MHRA) [16; 17; 18]. Eighty-five ventilators gradually received Emergency Use Authorization (EUA) from the FDA [16], with the time evolution of the number of approved ventilators shown in Figure 1. A significant number of these devices were previously developed ventilators used in clinical practice with only minor modifications to software, hardware, or documentation. Eleven of the eighty-five devices were only approved as emergency resuscitators. Less than ten Emergency Lung Vertilators, which were newly designed and manufactured during the COVID-19 pandemic, received EUA. Examples include the Milano mechanical ventilator [19], authorized on 1 May 2020, or the O2U ventilator Model 100 [20], authorized on 22 March 2021.  
One of these ventilators was also the CoroVent (MICo Medical s.r.o., Trebic, Czech Republic), authorized on 21 August 2020. The CoroVent ventilators were distributed to 27 Czech hospitals for free during the COVID-19 pandemics, but the production was stopped in 2021 when the Czech Republic had handled the crisis and the shortage of ventilators.  
The aim of this study was to describe the CoroVent ventilator and to demonstrate that even such a simple ventilator in principle that is easy to manufacture can meet the requirements for accuracy and safety according to international standards and clinical demands.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 5 Dataset Generation and Neural Network Training  
Given a specific signal-to-noise ratio defined in (14), the dataset over the AWGN channel is generated as follows:  
1. Randomly generate a message \(\mathbf{m}\).
2. Encode \(\mathbf{m}\) to produce the codeword \(\mathbf{c}\).
3. Use BPSK modulation and AWGN to generate the received signal sequence \(\mathbf{y}\).
4. Repeat steps 1-4 one million times to produce the dataset.
5. Divide the dataset into the training and validation sets with a ratio of 9:1.  
This data generation method for training NN or DNN-based decoding framework is commonly used in [13; 14]. However, the total numbers of generated error patterns with different Hamming weights are unbalanced. The error patterns with lower Hamming weight occur more frequently than those with high Hamming weight. Such an uneven distribution of error patterns may lead to a bias in the training of the NN, thereby potentially reducing its performance.  
The probability of the weight-\(j\) error pattern can be calculated via binomial distribution.  
\[P(j)=C_{j}^{n}\times P_{\epsilon}^{j}\times(1-P_{\epsilon})^{n-j},0\leq j\leq n \tag{13}\]  
where \(C_{j}^{n}\) is the number of combinations of \(n\) items taken \(j\) at a time, \(P_{\epsilon}\) is the bit error probability, which depends on the SNR. Assume that the bit error probability and codeword length are \(P_{\epsilon}=0.07\) and \(n=15\), respectively. The error probabilities for \(j=0,1,2,3,4\) can be calculated as follows:  
\[P(0) =C_{0}^{15}\times 0.07^{0}\times(1-0.07)^{15}=0.33\] \[P(1) =C_{1}^{15}\times 0.07^{1}\times(1-0.07)^{14}=0.38\] \[P(2) =C_{2}^{15}\times 0.07^{2}\times(1-0.07)^{13}=0.2\] \[P(3) =C_{3}^{15}\times 0.07^{3}\times(1-0.07)^{12}=0.065\] \[P(4) =C_{4}^{15}\times 0.07^{4}\times(1-0.07)^{11}=0.014\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Numbers of Nodes** & **Activation Function** \\ \hline Input layer & \(n\) & \\ Fully connected layer & \(2^{k}\) & ReLU \\ Output layer & \(2^{k}\) & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for Proposed NN.  
Figure 9: Proposed NN.  
As observed, the generated codewords with no errors account for 33% of the dataset, those with one error account for 38%, those with two errors account for 20%, and those with more than two errors account for 9%. Although this method is simple, it results in an uneven dataset. Therefore, how to produce a balanced dataset becomes an interesting issue in the future.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
###### Abstract  
The sensor-weapon-target assignment (S-WTA) problem is a crucial decision issue in C4 ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent cooperative engagement modes of sensors and weapons. As this problem is a complex combinatorial optimization problem, a novel genetic algorithm is proposed to improve the solution of this formulated S-WTA model. Based on the prior knowledge of this problem, a problem-specific population initialization method and two novel repair operators are introduced. The performances of the proposed algorithm have been validated on the known benchmarks. Extensive experimental studies compared with three state-of-the-art approaches demonstrate that the proposed algorithm can generate better assignment schemes for the most of the benchmarks.  
Keywords:cooperative engagement; sensor-weapon-target assignment (S-WTA); evolutionary algorithm (EA); genetic algorithm (GA) +
Footnote †: journal: Applied