esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices
## 3 Results and Discussion
### Tools for Determining Spectral Indices Useful for Polymers' Separation  
The results above clearly show that each polymer presents a characteristic spectral signature with absorption peaks positioned at distinctive wavelengths. Then, the ratio (or difference) of the reflectance values at two well-defined wavelengths may be remarkably different from polymer to polymer. Reflectance values may then be combined as ratios or differences to yield hyperspectral indices. The chance of a successful identification and separation of different materials increases with the difference of their hyperspectral index values.  
In the following paragraphs, tools employed to determine key wavelengths for hyperspectral index definition will be described.
## 4 Influence of Magnetic Load and Current Density on Temperature
### Influence of Current Density on Motor Temperature  
The effect of current density on temperature is explored in this section. The relationship between copper loss of winding and current density can be obtained using the following equations [17]:  
\[P_{Cu}=m{I_{ph}}^{2}R_{ph} \tag{3}\]  
Figure 10: Temperature comparisons under different stator magnetic flux densities: (**a**) temperature comparisons of stator yoke, rotor yoke, and PM; (**b**) temperature comparisons of stator teeth and winding.  
Figure 9: Temperature distributions under 1.2 T and 1.6 T: (**a**) whole temperature distribution under 1.2 T; (**b**) partial temperature distribution under 1.2 T; (**c**) whole temperature distribution under 1.6 T; (**d**) partial temperature distribution under 1.6 T.  
\[R_{hf}=\rho\frac{l}{A_{o}} \tag{4}\]  
\[J=\frac{I_{ph}}{A_{o}} \tag{5}\]  
\[P_{Cu}=mplI_{ph}I \tag{6}\]  
where \(P_{Cu}\) is the copper loss of the valid part, \(m\) is the phase number, \(I_{ph}\) is the phase current, \(R_{ph}\) is the effective partial resistance of each phase conductor, \(A_{o}\) is the conductor's effective cross-sectional area, \(I\) is the core length, \(\rho\) is the resistivity of conductor materials, \(J\) is the current density.  
The copper losses at different current densities are obtained via Equation (6), as shown in Figure 11. From the figure, it can be seen that the copper loss of winding increases by 12,127 W when the current density increases from 2 A/mm\({}^{2}\) to 6 A/mm\({}^{2}\).  
Figures 12 and 13 show the temperature distributions and comparisons, respectively, at different current densities. As shown, when the current density is 6 A/mm\({}^{2}\), the temperature of each part of the motor is the highest, and the temperature of the stator winding is 160.8 \({}^{\circ}\)C. Compared with the current density of 2 A/mm\({}^{2}\), the temperature of the stator winding at the current density of 6 A/mm\({}^{2}\) increased by 80.1 \({}^{\circ}\)C. Therefore, the temperature is greatly affected by the current density.  
Figure 11: Copper loss under different current densities.  
Figure 12: _Cont._
# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 2 Related Works  
### Real-World Image Datasets  
With the advancement of computer vision, object detection techniques have become increasingly comprehensive and sophisticated. It is widely recognized that the size of the dataset has a significant impact on the performance of object detection. A larger dataset allows for a higher upper limit of object detection accuracy, making large datasets critical for object detection tasks. Several corporations and challenges have released their own datasets to facilitate research and development in this field. For instance, Stanford's [PERSON] team created ImageNet, which consists of over 14 million images spanning 20,000 categories. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) utilizes this dataset. PASCAL Visual Object Classes has also released image datasets with labeled data and conducted competitions over multiple years, including notable examples such as VOC 2007 and VOC 2012. Microsoft has contributed the Microsoft Common Objects in Context (COCO) dataset, which includes 91 categories and a vast number of images for each category. Additionally, Google Inc. released Google Open Images, which comprises 1.9 million images, 600 categories, and 15.4 million annotated instances.  
In the domain of aerial imagery, the availability of rich datasets is also crucial to improve the practical performance of object detection. However, due to the inherent characteristics of aerial imagery, commonly used datasets in remote sensing are often small in size. This can be attributed to factors such as the high cost of satellite imagery, the susceptibility of aerial photographic tools to signal interference, interference from urban structures, expensive cameras that are suitable for aerial imaging, low resolution, and other challenges. Some well-known remote sensing datasets include DOTA [5], UCAS-AOD [6], NWPU VHR-10 [7], TGRS-HRRSD-Dataset [9], DIOR [8], VEDAI [20], and LEVIR [21]. A comparison of various prominent remote sensing datasets is presented in Table 1.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 3 Related Work
### ADS-B and ADS-B Vulnerabilities
They showed that ADS-B messages transmitted through this channel are at high risk of radio frequency attacks; these attacks could affect an aircraft's collision avoidance. Another outcome was that they reported a huge loss of messages due to the vast traffic on that specific communication channel. They recommended addressing these security issues before fully relying on ADS-B within the aviation industry. Moreover, [PERSON] and [PERSON] [20] showcased a relatively recent type of attack called teleporting ghost aircraft; they achieved it using Sentry and HackRF, where reports of an aircraft position were signaled at different locations and were moving around in an unstable way. Thus, that aircraft movement seemed to break the fundamental laws of physics. They pointed out that the ADS-B receiver trusted the protocol without verification or validation. They advised that if ADS-B is used more within the aviation industry with its lack of security, an attacker could take control of any aircraft because of the amount of control they would gain. All in all, these studies have shown that even though ADS-B is an evolution in the industry and brings advantages, it still needs to be more reliable and secure; more work is needed to secure and utilize it fully. We summarize the most relevant studies about ADS-B and its problems in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Approach** & **Contributions** & **Limitations** \\ \hline \multirow{4}{*}{[11]} & \multirow{2}{*}{—} & ADS-B injection using & — & The first ADS-B injection attack. & — & It provided no suggestions for dealing with those security threats and vulnerabilities. \\  & & MATLAB and SDR to & — & An insight into the threats and vulnerabilities that exist within ADS-B. & — & It provided no suggestions for dealing with those security threats and vulnerabilities. \\  & & inject ADS-B publicly. & — & Attacked an ADS-B network by jamming signals and interrupting communications. & — & Attacked an ADS-B network by jamming signals and interrupting communications. \\  & & & — & Network-based mitigation technique & — & The solutions are very costly since they require installing more hardware on a large geographic space. \\  & & & — & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the most relevant studies about ADS-B and its problems.
# Thermal and Mechanical Behavior of Elastomers Incorporated with Thermoregulating Microcapsules
## 4 Conclusions  
Elastomeric materials with thermoregulating properties due to incorporating mSD-(LDPE:EVA-RT27) within 0-20 wt.% and isocyanates/hydroxyl [NCO]/[OH] molar ratios of 1.05 and 1.1 were produced.  
The presence of urethane and isocyanate groups was clearly identified by FT-IR analyses, allowing the confirmation of an excess of isocyanate groups for the formulated [NCO]/[OH] molar ratio 1.1.  
All the synthesized elastomers were formed by a net of spherical microparticles with an average particle size within 2.640-1.617 mm.  
Thermogravimetric analyses allowed for distinguish between weight losses attributed to paraffin, the microcapsule shells and the hard segments and soft segments. Nevertheless, the weight loss of paraffin in the elastomers was overlayed with the hard segment, indicating that the elastomers were able to build form-stable materials, enlarging the paraffin thermal stability since its vaporization was delayed.  
In general, the density and mechanical properties decreased with the microcapsule content as result of the lower density of the microcapsules and their worse distribution into the elastomeric matrix. Elastomer E-1.05 exhibited the better structural and stability properties for MC contents up to 15 wt.%, whereas the E-1.1 elastomer containing 20 wt.% MC revealed mechanical and thermal synergy effects, demonstrating good structural stability and the largest latent heat (8.7 J/g).  
Conceptualization, I.I., A.S., M.C. and J.F.R.; methodology, P.J.T., A.M.B., J.D., M.C. and I.G.; validation, A.S., P.J.T., J.D. and I.G.; formal analysis, A.M.B., I.I., A.S., M.C. and J.F.R.; investigation, A.S., I.I. and P.J.T.; resources, M.C. and J.F.R.; data curation, A.M.B. and I.I.; writing--original draft preparation, I.I. and A.M.B.; writing--review and editing, I.G. and M.C.; visualization, I.I. and A.M.B.; supervision, J.F.R. and M.C.; project administration, M.C.; funding acquisition, M.C., and J.F.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Science, Innovation and Universities under project TRANSENERGY (RTI2018-100745-B-100).  
Figure 9: Resilience (**a**) and Shore A hardness (**b**) of the elastomers with mSD-(LDPE:EVA-RT27).
## 3 Results
### Health Assessment  
The following parameters were measured: body temperature, weight, height, BMI, blood pressure, BPM, oxygen saturation, blood sugar levels.  
Average body temperature was 35.96 \({}^{\circ}\)C (range: 34-37.1); weight was 70.71 kg (11-142), height was 1.62 mt (0.75-1.92), and average BMI was 26.3 (range: 13.78-44.92). Of these, 11 were underweight (BMI < 18.5), 42 were overweight (BMI between 25 and 29.9), and 36 were obese, with 3 having third-degree obesity (BMI > 40).  
Blood pressure measurements were performed on 340 individuals (77.98%). Average systolic pressure was 131.51 mmHg (range: 85-200); average diastolic pressure was 78.65 (range: 40-110). During the visit, 8 (2.35%) patients exhibited signs of grade 2 hypertension (160-179/100-109) and 4 (1.17%) displayed values compatible with grade 3 hypertension (\(\geq\)180/\(\geq\)110).  
Heart rate measurements were conducted on 351 of the 436 patients (80.5%) enrolled in the study; of these, 327 (93.16%) had values of beats per minute ranging from 50 to 100, 18 (5.12%) had values > 101, and 14 (3.98%) had values < 60.  
Oxygen saturation data were obtained from 345 individuals (79.12%); 322 (93.34%) had a SpO2 \(\geq\) 95, with 84 (24.34%) exhibiting values equal to 99 or 100.  
Blood glucose levels were measured in 286 subjects (65.59%). An amount of 53 (18.53%) patients displayed values between 60 and 80 mg/dL, 123 (43%) between 81 and 100 mg/dL, 71 (24.82%) between 101 and 120 mg/dL, 19 (6.64%) between 121 and 140 mg/dL, and 20 (7%) >141 mg/dL; of them, 4 patients displayed blood glucose levels between 280 and 408 mg/dL.  
The main health parameters evaluated in our sample are summarized in Figure 1.
# Biologically Derived Gels for the Cleaning of Historical and Artistic Metal Heritage
## 3 Bio-Based Hydrogels for Metal Preservation
### Physical Hydrogels
#### 3.1.4 Chitosan  
Chitosan is the N-deacetylated form of chitin, which is, in turn, the most abundant animal polysaccharide present in the exoskeleton of crustaceans and the cell wall of fungi [61]. Currently, chitosan is exploited mostly for drug delivery [61]. In general, it can gel in presence of both alkaline and acidic solutions [74].  
In art conservation, it is rather uncommon, but it was used to develop chitosan-based coatings for corrosion mitigation, of copper alloys [25]. The research aimed to design a non-toxic, sustainable, easily-applicable, and, whenever necessary, removable matrix to incorporate corrosion inhibitors for copper--mercaptoethanolobenzothiazazole (MBT) and BTA [25]. Remarkably, the chitosan polymer allowed the presence of a reduced quantity of toxic inhibitors and diminished their leaching in the environment. The transparent and colorless appearance of the chitosan-based lacquer meets the aesthetic standards in metal care. In addition, it showed a great adhesion on metals [25]. Furthermore, the development of chitosan-based active protective coatings has been carried out for aluminum alloys as well, by doping the matrix with either Ce\({}^{3+}\) ions or MBT [75;76]. In CH, this could be of particular interest for aeronautical heritage, considering for instance relics from World War II [77].  
Finally, chitosan-hydrogels have been investigated for their capacity to be loaded, and thereafter release BTA on metallic surfaces [78].
###### Abstract  
With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.  
cloud-network integration; satellite network; resource allocation; reinforcement learning; virtual network mapping +
Footnote †: journal: _electronics_  
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Footnote †: journal: _electronics_  
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# A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem
## 3 Simplified Discrete Harmony Search Algorithm for the 0-1 KP
### Solution-Level Pitch Adjustment Operator  
In the field of 0-1 KP, the greedy optimization method has been widely used to enhance the feasible solution. Most of the existing metaheuristics, such as [13; 21; 23; 24; 44; 45], use only the profit-weight ratio to guide the greedy optimization method. Our studies in using trajectory-based metaheuristics for the 0-1 KP [47; 48] have found that profit information can provide an extra intensification ability for the greedy optimization method. In the SDHS algorithm, we use a profit-guided greedy optimization method, where only profit is used to rank the items. The profit-guided greedy optimization method can be regarded as a solution-level pitch adjustment operator which is used to replace the classical dimension-level pitch adjustment operator of the HS algorithm. Algorithm 4 is the pseudo code of the solution-level pitch adjustment operator.  
```
0:\(x,v,c\) //A feasible solution
1:\(h\leftarrow\) the sorted items in non-ascending order of profit
2:for\(k=1\) to \(n\)do
3:\(j=h_{k}\)
4:if\(x_{j}==0\) and \(c+w_{j}\leq W\)then
5:\(x_{j}=1\)
6:\(v=v+p_{j}\)
7:\(c=c+w_{j}\)
8:endif
9:endfor
10:return\(x,v,c\)
```  
**Algorithm 4** Solution-level pitch adjustment operator.  
In Algorithm 4, parameters \(x\), \(v\), and \(c\) represent a feasible solution, the total profit of the selected items in solution \(x\), and the total weight of the selected items in solution \(x\), respectively. Variable \(h\) is a vector which stores the items in non-ascending order of profit. The solution-level pitch adjustment operator examines each item \(j\) in non-ascending order of profit and changes \(x_{j}\) from zero to one if the selection of item \(j\) does not violate the capacity constraint. Because each item must be checked for whether it can be put into the knapsack to enhance the solution, the time complexity of Algorithm 4 is \(O(n)\).  
Figure 2: Flowchart of the memory consideration operator.
## 4 Simulation Results
### Scenario 1
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Method** & **Peak Value** & **Lowest Value** & **Restoration Time (ms)** \\ \hline \multirow{2}{*}{P (kW)} & DCch-based Tree & 284.63 & 25.49 & 152.55 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 260.03 & 74.95 & 130.3 \\ \hline \multirow{2}{*}{DC-link voltage Vdc (V)} & DCch-based Three & 495.32 & 380.85 & 127.27 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 484.37 & 427.96 & 108.55 \\ \hline \multirow{2}{*}{DC current (p.u.)} & DCch-based Tree & 527.58 & 481.8 & 152.56 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 524.40 & 498.37 & 131.17 \\ \hline \multirow{2}{*}{Diode current (A)} & DCch-based Tree & 42.67 & 2.01 & 136.33 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 26.39 & 0.23 & 116.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of two protection strategies’ tracking effectiveness for a 90% voltage dip in time t = 1 s.
###### Abstract  
The aim of this paper was to obtain different extracts from the aerial parts of _Mesembryanthemum nodiform_, comparing traditional extraction (using solvents with increasing polarity such as hexane, ethanol 80%, acetone 70% and water) with an eco-friendly technique (supercritical fluid extraction (SFE)); to evaluate which extract showed a higher amount of antioxidants and then evaluate the bioactive properties in vitro, in human fibroblast (HS68). From the six extracts obtained it was observed that the solvent with the highest extraction efficiency was water, but the extracts in ethanol, N-hexane and SFE are those that showed the highest antioxidant activity (polyphenols, 1,1-Diphenyl-2-picrylhydrazyl (DPPH) and reducing power). On the basis of these results, the SFE extract was chosen to evaluate the antioxidant and anti-aging activity in vitro, by assessing cell vitality and molecular markers (MTT and immunoblotting assays). The results showed that the SFE extract exerted antioxidant activity in vitro, protecting cells from mortality induced by oxidative stress; this protection was also confirmed at the molecular level, by the levels of the protein integrin \(\alpha\)-1, that is able to prevent the negative effects of a stress situation, such as oxidative stress, that could promote aging and related diseases. This extract, obtained with an eco-friendly technique, given its beneficial properties, could be used for application in nutraceuticalals and cosmeceuticals.  
ice plant; SFE; antioxidant; cell culture; Integrin \(\alpha\)1; Sirt1; oxidative stress
## 2 Related Works
### Hypergraph Model  
The hypergraph can represent the complex relations between high-dimensional data, and every hyperedge connects multiple vertexes. A hypergraph \(\mathbf{G}=(\mathbf{V}_{H},\mathbf{E}_{H},\mathbf{W}_{H})\) can be constructed to formulate the relationships among data samples, where \(\mathbf{V}_{H}\) is a set of vertices, \(\mathbf{E}_{H}\) is a set of hyperedges, and \(\mathbf{W}_{H}\) is a diagonal matrix where each diagonal element denotes the weight of hyperedge. Each hyperedge \(e_{i}\in\mathbf{E}_{H}\) is vested by a weight \(w(e_{i})\in\mathbf{W}_{H}\).  
To represent the relationship of \(\mathbf{G}\), an incidence matrix \(\mathbf{H}=[H_{mn}:h(e_{m},v_{n})]\in R^{|\mathbf{E}_{H}|\times|\mathbf{V}_{H}|}\) is denoted as follows:  
\[H_{mn}=h(e_{m},v_{n})=\left\{\begin{array}{rcl}1,&\quad if\quad\quad v_{n} \in e_{m}\\ 0,&\quad if\quad\quad v_{n}\
otin e_{m}\end{array}\right. \tag{2}\]Furthermore, the degree of hyper-edge \(e_{m}\) and the degree of vertex \(\mathbf{v}_{n}\) can be defined as  
\[d(e_{m})=|e_{m}|=\sum_{\mathbf{v}_{n}\in\mathbf{V}_{H}}H_{mn} \tag{3}\]  
\[d(\mathbf{v}_{n})=\sum_{e_{m}\in\mathbf{E}_{H}}w(e_{m})H_{mn} \tag{4}\]  
There is an example to illustrate the hypergraph in Figure 1. A vertex is denoted as a circle (such as \(\mathbf{v}_{1}\), \(\mathbf{v}_{2}\), \(\mathbf{v}_{5}\)). As shown in Figure 1a, the simple graph only holds two vertices per edge, which just describes a single one-to-one relationship (such as \(\mathbf{v}_{1}\) and \(\mathbf{v}_{2}\), and \(\mathbf{v}_{1}\) and \(\mathbf{v}_{3}\)). In Figure 1b, each hyper-edge is represented by a curve (such as a hypergraph \(e_{1}\) is constructed by \(\mathbf{v}_{1}\), \(\mathbf{v}_{2}\), \(\mathbf{v}_{3}\)), which represents complex multiple relations among pixels. That is, a hypergraph can describe the local neighborhood structure well and preserve the complex relationships within the neighborhood. This hypergraph consists of six vertices and four hyper-edges, and the corresponding incidence matrix is shown in Figure 1c. The incidence matrix intuitively represents affinity relationships between vertices and hyper-edges, the non-zero element in each row indicates that a hyper-edge is associated with the vertex, otherwise the vertex and the hyper-edge are not interrelated.  
However, the hypergraph model only represents complex higher-order relations between pixels in spectral domain, and it fails to consider spatial information in hyperspectral image that limits the discriminant ability of embedding features for land cover classification.
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 5 Experimental Results
### Training Details  
All experiments were run on a computer with one NVIDIA GeForce GTX 1080 Ti and an Intel(R) Core (TM) i7-7700 running at 3.60 GHz. The PyTorch deep learning framework was built with Ubuntu 16.04 LTS architecture. The program code was written using Python language and called CUDA, CUDNN, and other required libraries to achieve the training and testing of human motion recognition models. The improved ST-GCN was trained by stochastic gradient descent (SGD) with a momentum of 0.9 and weight decay of 0.0001. There were 50 epochs in our training process, with 13 steps per epoch, and the complete training took about 600 min. The batch size was set to 16 and the learning rate was set to 0.1. When epochs achieved 10 and 50, the learning rate was reduced by a factor of 0.1.