esa-sceva/satcom-chunk-collection huggingface dataset:
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Scheduling Solution Inspection
#### 4.1.1 Example 1  
In this example, we present the features of the scheduling problem included in Figure 9:  
* Four-node network, where Node 1 connects all other nodes.
* Two streams, each one with a single frame. Stream 0 has a period of 5000 ms, while Stream 1 has a period of 2500 ms.
* The fifth red box at the top right of the diagram contains an array of arrays that describes the paths from source to destination obtained using [PERSON]'s algorithm. In this example, Stream 0 traverses from Node 2 to Node 3, passing through Node 1, while Stream 1 traverses from Node 0 to Node 3, also passing through Node 1.  
In the Gantt chart at the bottom of Figure 9, we can see that this solution fulfills all of the conditions of a TSN schedule, since there is no overlap between frames in both the same time slot and the same link; this is clear since no frame shares the same time interval with another frame of the same color. In addition, the example fulfills the usual assumption of any transmission: the streams pass through the links in sequential order. In the case of Stream 1, which has half the period of Stream 0, there exist repetitions in the hyper-period after 2500 ms.
## Glossary  
\begin{tabular}{l l} AIT & Assembly integration and test \\ COPUOS & Committee on the Peaceful Uses of Outer Space \\ EF & Environmental footprint \\ EM & Engineering model \\ EQM & Qualification model \\ ESA & European Space Agency \\ FCC & Federal Communication Commission \\ ISO & International Organization for Standardization \\ LCA & Life cycle assessment \\ LEO & Low Earth orbit \\ LCI & Life cycle inventory \\ \end{tabular}
## Evaluation
### Simulation Experiment  
In this experiment, we aimed to investigate the influence of the SOOA on challenge-based CIDNs under both Scenario 1 and Scenario 2. Following the example given in Figure 2, we set up two scenarios as below:  
* **Scenario 1.** In this condition, node \(A\) had six partner nodes in its list without node \(D\). In this case, node \(D\) behaved normally to some partner nodes of node \(A\), while it behaved untruthfully to the remaining partner nodes.
* **Scenario 2.** In this scenario, node \(A\) had seven partner nodes including node \(D\), which could send truthful answers to several partner nodes of node \(A\), while sending untruthful (or malicious) answers to the rest of the partner nodes.  
Figure 3 depicts the convergence of trust values for different expert nodes: low (\(I=0.1\)), medium (\(I=0.5\)) and high (\(I=0.95\)). The results validated the observations obtained in previous studies [6; 7]: that is, nodes with higher expertise can achieve bigger trust values. The trust values of all nodes became stable after around 16 days in the simulated network.
## 3 Results and Discussion  
### Olive Extract Flavor Characterization  
The GC-MS analysis of OE identified 27 volatile compounds (Table 3). Overall, many of the compounds had fruity or green notes while others had earthy or phenolic descriptors [10]. The profile was characteristic of olive aroma when compared to the literature: 10 of the compounds identified have been associated with olive leaf oil [11], 9 with olive oil [12], 8 with brined table olives [13], and 7 with olive pomace [14,15]. Furthermore, five detected isoprenoid-related compounds, limonee, linalool oxide, linalool, rose oxide, and alpha-terpineol, have also been reported in cathip [16]. These compounds have green and herbal notes commonly associated with plant-derived products and are not limited to olives or cathip [17]. While taste molecules are often not readily volatile and difficult to detect from GC, a previous high-pressure liquid chromatography analysis of the product detected hydroxytyrosol, a bitter compound that may discourage consumption by cats [18]. Another bitter compound associated with olives and reported to be a potential antioxidant and irritant is oleocanthal. Oleocanthal has been identified in the oil portion of olive fruit using Proton Nuclear Magnetic Resonance [19] but was not detectable in olive oil using solid-phase GC-MS [20]. While differences in methodology may explain why oleocanthal was not detected in the current study, the OE used in this study was from a water-based extraction and oleocanthal was not expected to be found in the product.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & **RI (DB5)** & **Odor Descriptors** & **Olive Product Detected in** \\ \hline Furfural & 836 & sweet, woody, almond & Oil \\ Benzaldehyde & 965 & bitter almond, cherry & Leaf, Oil, Fruit, Pomace \\ Phenol & 982 & phenolic, plastic & Pomace \\ Limonene & 1033 & citrus, herbal, terpene & Oil, Fruit, Pomace \\ linalool oxide & 1077 & floral, herbal, earthy & Leaf \\
4-methylbenzaldehyde & 1074 & fruity, cherry, phenolic & Oil, Fruit \\ methyl benzoate & 1099 & phenolic, wintergreen & Fruit \\ \hline \hline \end{tabular}
\end{table}
Table 3: Volatile compounds detected in PhenoFarm(tm) Olive Extract by HS-SPME GC-QTOF.
## 3 Results
### Thermal Storage System  
Based on the models described in Section 2.1 and the parameterized formulation of the effective radiation coefficient according to Section 3.1, systematic investigations of the storage system are performed. Overall aim is to identify storage system configuration with high systemic storage densities complying at the same time restrictions regarding the maximum permitted surface temperature (\(T_{W}\)). For this, the maximum thermal insulation thicknesses required to maintain the permitted surface temperature at the frontal areas (\(\Delta s_{Z}\)) and at the shell surface (\(\Delta s_{r}\)) of the honeycomb structure were iteratively calculated for each design solution. Hereby the extreme case-homogeneously heated honeycomb at the maximum operating heating wire temperature \(T_{P,max}\)-was assumed.  
Application-typical specifications for the storage system with regard to charging duration (\(\tau\)), storage capacity (\(Q\)), maximum permitted surface temperature (\(T_{W}\)) and electrical power supply (\(U\), \(I_{max}\)) are defined and summarized in Table 4.  
Here, values of 1000 \({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C were specified for the maximum permitted heating wire temperature (\(T_{P,max}\)) and for the ambient temperature (\(T_{U}\)). The material properties of the heating wire, the honeycomb structure and the thermal insulation were assumed to be constant and can be found in Table 1.  
In order to examine the storage system for its performance and systemic storage densities, variation studies were performed on the relevant influencing variables. These include the heating wire assignment (\(x\)), the specific surface (\(a_{V}\)) and the void fraction (\(\epsilon\)). Hereby, the minimum storage mass (\(m_{S}\)) of the honeycomb structure was iteratively determined for each design solution fulfilling the required specifications as shown in Table 4. Due to neglectable influences of the length-to-diameter ratio on the results within the simulation studies a constant value of \(L/D=2\) was set.  
For explanation of the central effects with regard to systemic storage density and heating wire dimensions, results on specific surface and heating wire assignment are presented firstly in Section 3.2.1. Based on these, storage-optimized results are derived in Section 3.2.2 and the impact of void fractions are discussed. Finally, central characteristics of the transient heating process for a favored design option and the resulting storage dimensions are presented in Section 3.2.3.
# Helmholtz Coils Based WPT Coupling Analysis of Temporal Interference Electrical Stimulation System
## 2 Principles and Methods Analysis
### Wireless Power Transfer System Based on Helmholtz Coils
#### 2.2.1 Magnetic Fields Generated by Helmholtz Coils
\right| \tag{3}\]  
From the superposition theorem, the x, y, and z directional components of the magnetic flux density vector \(\mathbf{B}\) produced by the Helmholtz coil at point \(P\) are:  
\[\mathbf{B_{PX}}=\frac{n\mu_{0}R}{4\pi}\int_{0}^{2\pi}\;(\frac{(0.5R-z)I_{1}}{r_{1} }^{3}+\frac{(-0.5R-z)I_{2}}{r_{2}}^{3})\cdot\cos\theta\mathrm{d}\theta \tag{4}\]  
\[\mathbf{B_{PY}}=\frac{n\mu_{0}R}{4\pi}\int_{0}^{2\pi}\;(\frac{(0.5R-z)I_{1}}{r_{1} }^{3}+\frac{(-0.5R-z)I_{2}}{r_{2}}^{3})\cdot\sin\theta\mathrm{d}\theta \tag{5}\]  
\[\mathbf{B_{PZ}}=\frac{n\mu_{0}R}{4\pi}\int_{0}^{2\pi}\;(\frac{I_{1}}{r_{1}^{3}}+ \frac{I_{2}}{r_{2}^{3}})\cdot(-R+y\sin\theta+x\cos\theta)\mathrm{d}\theta \tag{6}\]  
Based on the derivation of the above equations, the magnetic field distribution of the Helmholtz coils at each point in space can be calculated by the analytical method.
# Radiometric Identification of Signals by Matched Whitening Transform
## 3 Reversing Phase and Frequency Offsets
### Signal Model  
An MPSK signal is modeled as follows  
\[s(t) =\sqrt{p}e^{i(2\pi f_{c}t+\phi_{m})}+n(t),m=0,1,\ldots,M-1 \tag{7}\] \[\phi_{m} \in\{\frac{2\pi m}{M},m=0,1,\ldots,M-1\} \tag{8}\]  
where \(P\) is the received carrier power, \(f_{c}\) is the carrier frequency and \(\phi_{m}\) is the original constellation vertices. The local oscillator offset frequency \(f_{d}\) creates a time varying phase offset \(\theta(t)=2\pi f_{d}t\). Therefore, the offset frequency is the the slope of the phase trajectory. The basebanded signal for the \(k\)th symbol is  
\[r_{k}=i_{k}+jq_{k}=\sqrt{p}e^{i(\phi_{k}+\phi_{k})}+noise \tag{9}\]  
The discrete model for the phase offset is \(\{\theta_{k}=2\pi f_{d}t,t=kT_{s},k=1,2,\ldots K\}\) where \(T_{s}\) is the symbol length and \(K\) is the number of symbols in the block used to estimate phase rotation. Successive symbols rotate by \(2\pi f_{d}T_{s}\) radians away from their nominal positions. This movement forms an arc over time thus causing a smearing effect shown in Figure 4. To correct for this rotation, an estimate of \(\theta_{k}\), \(\hat{\theta}_{k}\), must be found and used to recover \(f_{d}\) and derotate the block of symbols. The maximum symbol rotation over a block is \(T=KT_{s}\).  
Offset frequency estimation can be achieved by first estimating the phase trajectory. The estimation of \(\theta(t)\) is performed over short blocks of length \(T\) to assure phase stationarity, i.e., \(\{\theta(t)\approx\theta_{k},t\in T\}\). Therefore, there is one phase estimate per block of data. The quantity \(f_{d}T\) is the fractional rotation of the constellation over \(2\pi\) for the block length \(T\). This quantity must be kept small for two reasons. One, smaller \(f_{d}T\) means a finer sampling of the phase curve. This is important in capturing phase nonlinearity by piece-wise linear modeling. Two, large \(f_{d}T\) pushes the symbols beyond their original symbol quadrant. This effect can be seen in Figure 4 where symbols in the first quadrant have been pushed to the second quadrant. What constitutes short or long segments is explained in the following section.
## Materials and Methods
### Satellite Data Pre-Processing and Predictors Derived
The biophysical variables were also calculated in SNAP from its biophysical processor, which uses a neural network algorithm based on the PROSPECT+SAIL (PROSAIL) radiative transfer model [59]. Except for serving as a predictor, the elevation data from SRTM digital elevation model (DEM) were supplemented with Sentinel imagery processing to improve the accuracy (Figure 2).
## 2 Related Work
For many years, fuzzy logic has been the best solution that has been widely used in rapidly changing unpredictable environments. [PERSON] et al. [51] have used fuzzy logic for offloading mobile data because of its simplicity, which produces more efficient results compared to its competitors. For measuring the sender's vehicle trust level, [PERSON] et al. [52] used fuzzy -logic in vehicular ad hoc networks environment which was based on experience and plausibility. To ensure the authenticity of the received information from the authorized vehicles, this model performed a series of security checks. Furthermore, in case of a security service selection, a soft hesitant fuzzy rough set (SHFRS) approach was used to choose the appropriate decision from multi-criteria decision-making problem [53]. Moreover, [PERSON] et al. [54] proposed a workload orchestration based decision engine for task offloading by using fuzzy logic to determine the offloading location for executing the tasks which will be either edge or remote cloud, whereas we used fuzzy logic for collaborative task offloading scheme, which will decide where to offload the incoming task among SBS-MEC servers. Our proposed fuzzy-logic based system is employed to determine the location of the offloaded task which will either be the local or neighboring SBS-MEC servers. The novelty of our work is to utilize the neighboring SBS-MEC server which has spare computing resources to reduce the number of task failures and application response time. To the best of our knowledge, an FCTO scheme for DDSCNs has not yet been studied for this domain.
# Analysis of Student Academic Performance Using Human-in-the-Loop Cyber-Physical Systems
## 4 Results and Discussion
In the case of evaluating the number of sleep hours (\(NSH\)) against the amount of study (\(StudyQ\)), we can see that these have a negative relation (\(R\) = \(-\)0.371). While, the \(StudyQ\) is correlated positively with the percentage of sociability (\(\%S\)) (\(R\) = 0.513). In addition, there is a positive relation (\(R\) = 0.588) between the Sleep Quality (\(SQ\)) and the physical activity reported in the questionnaire (\(\%AAQ\)). As such, we can conclude that when the students sleep better, they feel more energetic and tend to do more physical activity. The average percentage of sociability state obtained from the questionnaire (\(\%SSQ\)) is also correlated with the sleeping quality (\(R\) = 0.565). Additionally, we performed an analysis dividing the dataset into two groups based on the grade obtained in the first bimester in the subject where this study was applied _GPx1B_ (value out of 10 points): the first group (\(G_{1}\)) was made up of students that obtained 6 points or less, and the second group (\(G_{2}\)) was made up of the students that obtained 6.1 or more. Where \(G_{1}\) had eight students and \(G_{2}\) had 18 students. In this case, the average values of the variables for each day and for each group were calculated. Figure 5: Grade Point Average of 1 st bimester (_GPA1B_) and \(\Delta t_{P}\) by student. In the case of evaluating the number of sleep hours against the amount of study, we found that these variables have a negative relation for both groups (\(R_{G1}=-0.578\); \(R_{G2}=-0.642\)). This correlation can be better seen in Figure 6, where the number of hours slept has an inverted y-axis, and we can see that both curves align almost perfectly. Additionally, the groups have different patterns of sleep. Students from \(G_{2}\) start to sleep less 4 days before the exam, which corresponds to the same date in which they increase their study levels; while students from \(G_{1}\) drastically decrease their sleep duration the day before the exam. For both groups, it is also visible that students sleep more at the weekends. As Figure 7 shows, the amount of study also negatively influences the sleeping quality, as happens with the number of sleeping hours. This was already expected since the sleep quality and the amount of sleeping hours are strongly related. It is also worth noting that the levels of sleep quality for students in \(G_{2}\) are lower than the ones from students in \(G_{1}\), especially in the week of the exam and the one before, which correspond to the weeks in which the study levels increase for \(G_{2}\). Figure 6: _StudyQ_ and the Number of sleep hours (_NSH_) per day of G2.
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
## 2 Materials and Methods
### Evaluation of Purity of Sodium Alginate by Qualitative Phytochemical Analysis  
To estimate the purity, an analysis for the existence of flavonoids, alkaloids, tannins, terpenoids, steroids, saponins, glycosides, phenols, and phlorotannins was done conforming to Trease and Evans [46] and Sofowora [47], which are rapid qualitative assays to verify the presence of possible impurities (other seaweed compounds in addition to the targeted compound).
## 3 Proposed Framework
### PSI-CNN Model  
PSI-CNN first passes input image through \"_Conv stage 1_\", which comprises two ordinary convolutional layers followed by a standard pooling layer. Each convolutional layer extracts \(d_{1}\) feature maps. Similar to the VGG face model, \(d_{1}\) is set to 64 and each feature map has size of \(224\times 224\). After pooling, we yield 64 reduced feature maps of size \(112\times 112\). Now, \"_Conv stage 2_\" is where the PSI-CNN model differs from other CNN-based approaches. The 64 feature maps generated from \"_Conv stage 1_\" are passed through two additional convolutional layers with 128 filters in \"_Conv stage 2_\", generating \(d_{2}(=128)\) feature maps in total. At the same time, the factor-of-two-downsampled input image is used to generate \(d_{2}\)_untrained_ feature maps of size \(112\times 112\). Thus, the total number of feature maps in \"_Conv stage 2_\" becomes 256 (=\(2\times 128\)). If the above feature maps were to propagate directly to the next stage, the overall size of the network would increase, consequently increasing the computational cost. To circumvent this issue, we combine the feature maps resulted from \"_Conv stage 1_\" and the untrained feature maps from the downsampled image by adopting a feature-level fusion process, maintaining a constant number of feature maps. More specifically, each level of feature maps obtained by propagating through the network is paired with its same-level _untrained_ counterpart (i.e., feature maps derived from image downsampling), and a combined feature map is generated by averaging each of these pairs. By adopting this fusion procedure, it is possible to utilize a conventional CNN architecture without modifying its foundational structure (e.g., number of feature maps and stride of convolution filters). The subsequent stages are extended in the same way as in \"_Conv stage 2_\". After each convolution step, we apply the rectified linear unit (ReLU), which is an efficient activation function that requires lower computation cost than other activations such as sigmoid or hyperbolic tangent and avoids the problem of vanishing gradients during back-propagation [18]. Given a scalar input \(x\), the ReLU function simply outputs  
Figure 2: An overview of our PSI-CNN architecture\[y=\max(0,x). \tag{1}\]  
After the last _\"Conv stage\"_, 512 feature maps of size \(7\times 7\) are generated and converted to a 1024-dimensional vector by applying a fully connected layer. Finally, a softmax layer is added as a classifier. Note that this layer is only utilized at test time. The feature vector from the fully connected layer is used to calculate the similarity between two images to identify whether they are from the same person or not. To prevent over-fitting, we apply dropouts with 50% dropout probability to edges between the fully connected layer and the classifier. More details on the PSI-CNN architecture can be found in Table 2. Note that the PSI-CNN framework is generic and may therefore be applied to other conventional convolutional networks without modifying its basis structure.
# Improvement of Porous GaN-Based UV Photodetector with Graphene Cladding
###### Abstract  
This work presents the role of graphene in improving the performance of a porous GaN-based UV photodetector. The porous GaN-based photodetector, with a mean pore diameter of 35 nm, possessed higher UV sensitivity, about 95% better compared to that of the as-received (non-porous) photodetector. In addition, it exhibits a lower magnitude of leakage current at dark ambient, about 70.9 uA, compared to that of the as-received photodetector with 13.7 mA. However, it is also highly resistive in nature due to the corresponding electrochemical process selectively dissolute doped regions. Herein, two types of graphene, derived from CVD and the electrochemical exfoliation (EC) process, were cladded onto the porous GaN region. The formation of a graphene/porous GaN interface, as evident from the decrease in average distance between defects as determined from Raman spectroscopy, infers better charge accumulation and conductance, which significantly improved UV sensing. While the leakage current shows little improvement, the UV sensitivity was greatly enhanced, by about 460% and 420% for CVD and EC cladded samples. The slight difference between types of graphene was attributed to the coverage area on porous GaN, where CVD-grown graphene tends to be continuous while EC-graphene relies on aggregation to form films.  
graphene cladding; porous GaN; high sensitivity; UV photodetector +
Footnote †: journal: Applied Sciences
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 1 Introduction
In recent years, the development of artificial intelligence technology has provided a new way for state recognition, which can be realized through multi-source information fusion. According to the different fusion levels, information fusion technology can be divided into data-level fusion, feature-level fusion and decision-level fusion. [PERSON] et al. [10] achieved bearing fault diagnosis by correlating multiple acoustic emission sensor signals and then performing data-level fusion. [PERSON] et al. [11] used Variational Mode Decomposition (VMD) and Hilbert Transform (HT) methods to process the vibration signal into a time-frequency image, and through data-level fusion with the infrared image of the same dimension, the accurate diagnosis of the rotor system failure is realized. [PERSON] et al. [12] constructed a deep learning model for dynamic feature-level fusion of speech and facial images, and realized the recognition of depression tendency with an accuracy rate of 71%. [PERSON] et al. [13] used a combination of feature-level and decision-level fusion to realize the diagnosis of bearing fault types, where the cross-diagnosis of various eigenvectors was performed at the feature level, and the evidence theory method was used for fusion diagnosis at the decision level. [PERSON] et al. [14] achieved the classification of depression by performing decision-level fusion of models trained from speech features and video features. [PERSON] [15] realized depression recognition through decision-level fusion of speech, facial images and text with an accuracy rate of 86.73%. In view of the shortcomings of the existing method in contact fatigue state identification of the specimen, such as the problems of unstable image quality and influenced by the test environment in visual detection, as well as the difficulties of identifying critical features and poor robustness in vibration detection, combined with the powerful advantages of artificial intelligence technology in pattern recognition, it is proposed to integrate two heterogeneous data, vibration and image, to realize the contact fatigue state identification of the specimen. Relying on the vibration signals and monitoring images obtained by the rolling contact fatigue test equipment independently developed by the team, and comprehensively using theories and methods such as multi-source information fusion, pattern recognition and deep learning, a state recognition network model based on vibration and image spatiotemporal heterogeneous data is constructed to realize the contact fatigue state identification of the specimen accurately.