esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Performance Indicators for Traffic Flow Prediction
## 4 Results
### Comparison Between Different Traffic Prediction Methods
Even though they had similar average RMSE values, the spatial clustering of high-value nodes on STSGCN was less preferable than that of ASTGCN, which had no tendencies. If we assume a similar number of star and line clusters, the insignificant \(p_{G}\)-value of ASTGCN suggests a more uniform performance across the network. The APAM univariate analysis comparison between different methods is summarized in Figure 10. We calculate APAM with \(\alpha=0.5\), assuming that the importance between the topology of the network and the traffic flow data is equal. The higher APAM value of a node indicates a higher importance, considering its centrality and node value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \(G\) & \(E[G]\) & \(G-E[G]\) & \(V[G]\) & \(\sqrt{V[G]}\) \\ \hline STSGCN & 0.0205 & 0.01907 & \(1.43\times 10^{-3}\) & \(5.49\times 10^{-7}\) & \(7.41\times 10^{-4}\) \\ ASTGCN & 0.0198 & 0.01907 & \(7.26\times 10^{-4}\) & \(5.41\times 10^{-7}\) & \(7.35\times 10^{-4}\) \\ DDGCRN & 0.0209 & 0.01907 & \(1.83\times 10^{-3}\) & \(9.20\times 10^{-7}\) & \(9.59\times 10^{-4}\) \\ STAEformer & 0.0216 & 0.01907 & \(2.53\times 10^{-3}\) & \(1.37\times 10^{-6}\) & \(1.17\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(G\), \(E[G]\), and \(V[G]\) values for all traffic flow prediction methods. Figure 10: Comparison of APAM moment analysis for different traffic flow prediction methods. Figure 9: Comparison of Getis-Ord General G for different traffic flow prediction methods. All methods have positive skewness and kurtosis, indicating a distribution where most nodes have low importance, with some high-importance nodes. Similar to the clustering scenario, the variance of the distribution showed an insignificant difference and a skewed distribution with high kurtosis, rendering the variance of the distribution uninformative. The ranks of skewness and kurtosis are identical, but kurtosis shows a more pronounced difference compared to skewness. The differences in the skewness and kurtosis of each method were small and less informative compared to the results in the clustering scenario. ASTGCN had a comparably higher kurtosis and STSGCN had a comparably lower kurtosis. STSGCN with the lowest kurtosis indicates a more uniform distribution and less pronounced extreme values, whereas ASTGCN with the highest kurtosis indicates a higher probability of extreme values. This finding is interesting because both STSGCN and ASTGCN use the same adjacency matrix as prior knowledge with similar average RMSE. Based on the analysis of clustering scenarios, the higher skewness and kurtosis of ASTGCN suggest that there are more nodes with higher APAM values, even though the majority of the nodes have lower APAM values. These nodes can be considered critical nodes and require more attention because of their high importance.
## 2 Results and Discussion
Unfortunately, Hall measurements for samples with rather low conductivity or electrical non-uniformity are experimentally challenging and consequently the few data obtained for the SiOC/C samples with low contents of segregated carbon (i.e., <8 vol%) do not allow any clear statement. Figure 10 plots the Hall coefficient (R\({}_{\rm H}\) = 1/Ne = \(\mu\)/\(\sigma\)) of SiOC/C samples as a function of their sp\({}^{2}\)-hybridized carbon content. It is shown that SiOC/C samples possessing carbon content in the range from 8 to 43 vol% show a Hall coefficient of 5 \(\times\) 10\({}^{-2}\) cm\({}^{3}\)/C, which does not depend on the amount of carbon in SiOC/C and is similar to that of glassy carbon (2.8 \(\times\) 10\({}^{-2}\) cm\({}^{3}\)/C) [77]. The positive sign of the Hall coefficient indicates p-type carrier transport. This may be related to vacancies (missing carbon atoms within the lattice of sp\({}^{2}\) carbon) or to the removal of dangling bonds creating holes in the filled \(\pi\)-valence band [81]. Figure 1:3 Plot of the Hall coefficient in SiOC/C samples synthesized at 1600 \({}^{\circ}\)C as a function of their sp\({}^{2}\)-hybridized carbon content. Glassy carbon is depicted as reference [75]. Figure 1:4 Charge carrier density (N) and mobility (\(\mu\)) in SiOC/C materials prepared at T = 1600 \({}^{\circ}\)C; the empty symbols represent data from a case study published by [PERSON] et al. [76]. Positive Hall coefficients comparable to those measured for SiOC/C samples in the present study were also reported for (nano)crystalline graphite [82; 83; 84]. It is shown that upon decreasing the sp\({}^{2}\) carbon content in the studied SiOC/C samples (i.e., \(\phi_{\mathrm{C}}<\) 8 vol%), the Hall coefficient strongly increased, indicating less ordering of the carbon phase in SiOC/C materials containing low amounts of sp\({}^{2}\) carbon. This result is supported by the Raman data showing a change of the lateral crystal size L\({}_{\mathrm{a}}\) and the average defect distance L\({}_{\mathrm{D}}\) in the same concentration region. Furthermore, the activation energy for the conductivity is shown to increase from 0.03 to 0.3 eV as the amount of free carbon decreased below 8 vol.%, as discussed above.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 4 Challenges of Predictive Maintenance
### Machine Repair Activity Limits  
By being able to predict the remaining life of a component, maintenance times can be determined, but the actual maintenance of a component still faces challenges related to the dependence on human interactions and the lack of self-maintenance [53]. In fact, the effectiveness of maintenance depends on the quality of human management and skills, given that machine components currently depend on human operators for controland maintenance. Industrial machines particularly work by executing commands in a reactive manner and do not question the plan for them. However, human task planning is based on data and experience, which the machine might also be able to retrieve. Thus, an intelligent component could autonomously propose or even initiate actions that are beneficial to system health, asset throughput, or product quality. A further step toward asset autonomy relies on asset awareness and autonomous maintenance. A self-aware asset can assess its conditions based on data that is currently extracted and stored in a predictive maintenance system and can recognize critical conditions up to the point of autonomous definition of maintenance decisions. Unlike a central system controlling one or more assets, all the information needed to make predictive maintenance decisions, as well as the degradation and prediction model, would be distributed and available at a component level. Machines can then plan maintenance programs for themselves. However, currently, industrial machines do not have this level of self-awareness and self-maintenance.
## 3 Results
### Ascorbic Acid Content in Analysed Vegetable Juices
#### 3.2.2 Ascorbic Acid Content in Analysed Vegetable Juices in Plastic Containers
The vitamin C content of vegetable juices stored in plastic containers decreased by 0.85% to 20.00% in 24 h when the temperature was raised to t\({}_{2}=23\)\({}^{\circ}\)C. After 48 h of storage in these containers, differences in vitamin C concentration were found to range from 5.12% to 30.44% and after 3 days from 8.80% to 39.13% of the initial value. The decomposition of vitamin C is associated with several chemical changes, most of which involve enzymes (present in natural, unpasteurised foods) which grow activated as the temperature rises [78]. In addition, vitamin C was found to be more stable in glass containers than in polyethylene ones. At t\({}_{2}=23\)\({}^{\circ}\)C, the lowest losses were recorded in the red pepper juice and the highest in the white cabbage juice (21 days). Vegetables with a firm texture, such as red and white peppers and cucumber, preserve vitamin C better. The rapid decrease in vitamin C content in the cabbage juice may have been influenced by the larger surface area and higher iron content, which play an important role in oxidation-reduction reactions. After lowering the temperature of the storage to t\({}_{3}=-18\)\({}^{\circ}\)C in the vegetable juices in containers of plastic, the spectrum of decline in vitamin C contents within 24 h was between 0.98 and 36.33%, within 48 h to the extent of 6.64-40.08%, and within 3 days in the range of 7.60-43.82% of the beginning value. Refrigeration does not always guarantee the safety and integrity of food that is susceptible to damage. Freezing vegetable juices can cause chemical changes in oxidative and enzymatic processes that lead to loss of vitamin C. Freezing is popular among consumers because it has minimal effect on the nutritional value of foods and does not require the addition of additives. Due to consumer habits and the long-term storage of vegetable juices, they are often frozen (t\({}_{3}=-18\)\({}^{\circ}\)C) for up to three weeks before consumption. Ascorbicacid is better preserved at low and stable temperatures throughout the storage period, but this is not consistent with the results of this analysis. Vitamin C depletion was also observed in vegetable juices stored at t3 = \(-\)18 \({}^{\circ}\)C in plastic containers. The highest losses were observed for the red pepper juice and the lowest for the cucumber juice. The loss of ascorbic acid is probably due to ascorbate oxidase activity, which is pH-sensitive.
## 4 Evaluation of Biological Properties
### Neuroprotective Activity
[41] investigated the neuroprotective effects of daucosterol on H\({}_{2}\)O\({}_{2}\)-induced cell death of human brain neuroblastoma SK-N-SH cells using MTT and lactate dehydrogenase (LDH) assays, annexin-V/PI double-staining, and flow cytometry. Therefore, daucosterol exhibited neuroprotective activity against H\({}_{2}\)O\({}_{2}\)-induced oxidative stress through downregulation of MAPK pathways, minimizing ROS, and upregulation of antioxidant genes (HO-1, CAT, and SOD2) (Figure 3). Moreover, oral administration of daucosterol ameliorated amyloid beta-induced learning and memory impairment in rats by inhibiting beta-amyloid-induced hippocampal ROS production, and prevented beta-amyloid-induced hippocampal neuronal damage and restored hippocampal synaptophysin expression level [29]. Recently, [PERSON] et al. [30] investigated the neuroprotective effects of daucosterol in a cerebral ischemia/reperfusion (I/R) rat model. Accordingly, daucosterol attenuated brain damage and neuronal cell apoptosis caused by I/R injury by upregulating the PI3K/Akt/mTOR signaling pathway and suppressing iNOS expression in the ischemic zone. Figure 3: Neuroprotective effects of Daucosterol. The arrow indicates increasing or decreasing. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Approaches** & **Key Results** & **References** \\ \hline \multicolumn{3}{c}{Reduced neuronal loss and apoptotic rate} \\ Oxygen-glucose & Suppressed caspase-3 activity & \\ deprivation/reperfusion-mediated injury in & Upresgulated the expression of IGF1 protein & \\ OGD/R model & Activated the AKT signal pathway & \\ Diminished the downregulation of the Mcl-1 and Bcl-2 & & \\  & Decreased the expression level of protein Bax & \\ \hline \multicolumn{3}{c}{Inhibited cell death and LDH activity} \\ H\({}_{2}\)O\({}_{2}\)-induced cell death of human brain & Reduced intracellular ROS levels by 37.7\% & \\ neuroblastoma SK-N-SH cells & Reduced H\({}_{2}\)O\({}_{2}\)-mediated fragmented DNA & [41] \\  & Increased CAT and SOD2 mRNA levels & \\  & Attenuated H\({}_{2}\)O\({}_{2}\)-induced phosphorylation of p38 and JNK & \\ \hline \multicolumn{3}{c}{Decreased apoptotic cell death} \\ Cerebral ischemia/reperfusion (I/R) rat model & Suppressed iNOS expression in ischemic zone & [30] \\  & Upresgulated the PI3K/Akt/mTOR signaling pathway & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Neuroprotective effects of Daucosterol.
## 2 Motivation  
Our aim was to design special antennas able to convey the electromagnetic signal along patterns characterized by extremely high directivity, ameliorated in comparison to existing commercial solutions. The basic structure we sought is that of biconic antennas, although other geometries might be taken into account. The goal was to predict the possibility of generating peculiar waves that can be employed in P2P communications or WPT applications in a much more energy efficient way. The cones, supplied with an oscillating source of the proper frequency, are embedded in a dielectric material. The shape of this medium is designed in order to drive the signal in a specified direction by respecting the classical rules of geometrical optics. Recent similar results on this subject are provided for instance in [13], although the functioning principles of those devices are rather different from the ones considered here.
## 6 Proposed MAC Protocol for Wireless Healthcare Environments--WhMAC
### whMAC Power Consumption Model
The idea is to have a function \(P_{total}=f(n_{wup},n_{stat},T_{stream},T_{on})\), where the input parameters would be application dependent, and everything else would be hardware dependent (dependent on data rate, hardware power consumption, sample rates, _etc._)  
For the power consumption, we will consider an application with real components, and discuss the power consumption. The architecture of the sensor and MN is shown in Figure 2. We will look at the sensor modes of operation. The power consumption for each mode is calculated as:  
* _Disconnected and Stand by_--The sensor is off, waiting for a command. \(P_{sb}=P_{ws}\)
* _Monitoring_--The sensor is waiting for a command, and woken up to send measured data with the frequency \(f_{m}\): \[P_{mon}=P_{ws}+f_{m}(E_{wup}+E_{stat})\]
* _Stream_--The sensor is woken up to stream data for the duration of \(T_{stream}\). \[P_{str}=P_{ws}+E_{wup}/T_{stream}+E_{stream}/T_{frame}\]  
For example, we will consider ADF7020 transceiver for data transfer, MSP430 microcontroller, and our added wake up circuit. For our wake up receiver, the relevant values are given in Table 1.  
Timing values for the presented packet formats are: \(T_{wup}=8.7 ms\), \(T_{cmd}=1.08\) ms, \(T_{stat}=1.08\) ms, \(T_{beac}=0.44\) ms and \(T_{ack}=0.44\) ms. We will consider for streaming a signal sampled with stream data rate of \(f_{s}\), which would give various \(T_{data}\) for a \(T_{frame}=1\) s. For this example, the power used by the sensor for communication is given in Table 2. \begin{table}
\begin{tabular}{l r} \hline \hline
**Communication Parameters** & **Measured Values** \\ \hline _ADF7020 transceiver_ & \\ Communication band—ISM & \(433\) MHz \\ Communication data rate & \(200\)\(\mathrm{kb/s}\) \\ RF output power & \(-10\) dBm \\ Low Power Sleep Mode & \(230\) nW \\ Consumption in receive mode & \(\mathrm{P_{R}}=57\) mW \\ Consumption in transmit mode & \(\mathrm{P_{T}}=47.7\) mW \\ \hline _Wake up receiver (WUR) [2]_ & \\ Data rate & \(5.5\)\(\mathrm{kb/s}\) \\ Quiescent power & \(\mathrm{P_{ws}}=270\) nW \\ Receiving-Dynamic power & \(\mathrm{P_{wd}}=3\)\(\mu\)W \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ADF7020 and Wake Up Receiver characteristics. In a typical application, depending on the type of the sensor used, the sensor can go through various states, and using the derived model for the hardware, the energy consumption can be predicted. Also, some sensors can be in two modes, streaming and monitoring, where additional power must be added.
## 3 Results and Discussions
### Water Absorption  
The sorption characteristics were investigated by a gravimetric analysis according to the standards provided by ASTM D570. The percentage of water absorbed by the designed materials after 11 days of immersion at room temperature is represented graphically in Figure 8. Based on the literature studies [53; 54; 55; 56; 57; 58], it is known that the moisture absorption of polymeric materials is influenced by numerous factors, the principal ones being the presence of nanovoids and microcracks characteristic for epoxy resins and the presence of hydroxyl or other polar groups in the chemical network. As can be seen in Figure 8, the water retention of the neat EDI resin is very low, with the WA% being about 0.7% at saturation level. If we analyse the water absorption depending on the nature of the filler, as both HC and SB present hydroxyl groups along the chain, they will increase the affinity of composites for water. Thereafter, in the case of untreated wood filler SB, the concentration of polar groups is higher than that of HC, leading to higher water absorption.  
Investigating the water retention behaviour depending on the amount of filler, the WA% gradually increases with the increase of the filler quantity. In addition to the presence of polar groups that favour water absorption by hydrogen bonding, the manufacturing protocol of the polymeric materials, as well as the presence of some chemically unbound filler particles, can lead to the formation of microcracks that favour the penetration of water through the network. In the case of both HC and SB composites, the maximum WA% at saturation level is reached by the systems with 20 wt.% filler, being around 1.7% for HC materials and 6.3% for SB materials. For the materials with 30 wt.% filler, a decrease of the WA% can be observed, which may be due to the supra-saturation of the network.  
In conclusion, the designed materials reveal a very good WA% at the saturation level, ranged between 0.7 and 1.7% for the ELO/ HC composites and between 0.7% and 6.3% for those with SB.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques
### Secure Multi-Party Computation  
Secure multi-party computation (SMPC) is a cryptographic technique that allows two or more parties to securely compute a function over their private data without any of the parties being able to access the data of the other parties [86; 87]. In other words, SMPC ensures that the private data of each party remain secure while still allowing the parties to obtain the result of the computation over all the data without ever having to share the data themselves.  
SMPC has been used in FRSs to protect user data and enable collaborative recommendations across multiple parties. For example, in [88], the study used SMPC to create a privacy-preserving collaborative filtering system that allows multiple parties to generate personalized recommendations without sharing the user data. Similarly, in [89], the study used SMPC in blockchain for privacy preservation.  
The benefits of using SMPC in FRSs include the ability to protect user data privacy, the ability to generate personalized recommendations from multiple parties collaboratively, and the ability to create a decentralized recommendation system [90; 91]. However, the use of SMPC also has some limitations, such as the high computational cost associated with secure computation, as well as the complexity of designing and implementing secure multi-party protocols.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Reflector Antenna
#### 3.6.2 Parabolic Reflector (Dish Antenna)  
The parabolic reflector antenna has two types. The first is the right cylinder while the second one is paraboloid. The cylinder type uses linear array, linear dipole, and slotted wavelength to feed. The paraboloid, on the other hand, uses pyramidal or paraboloid conical to feed. The radiation pattern of this antenna is strongly determined by the radiation pattern of the feeder used as well as the reflector measurements and material [65; 66]. An evaluation study on the desirability of defocusing a large-sized multi-beam hybrid parabolic antenna (MBHPA) has been performed [67]. Defocusing means displacement of the AA from the focus, in the interests of rarefaction of the AA. Results indicate that the idea of defocusing, which is tempting at first glance, does not benefit in the gain factor of the beams in comparison with un-displaced AA with the same spacing of the array elements.
## 5 Example of GSI Assessment: Graphical GSI Chart and V-GSI Chart (Without and with Scale)
The takeaways from this example that can have design implication are as follows:  
* With reference to Figure 9, judging scale based on structure caricatures alone can lead to strength and deformability estimates that are conservative; e.g., the tensile strength estimate using the scaled V-GSI rating is a factor of around 4.5 times stronger than the estimate using the graphic chart estimate. * The modulus of the rock mass from the scaled V-GSI rating (Figure 9) is around a factor of 1.5 times greater than the graphic GSI modulus estimate. Depending on the project, use of the graphic GSI chart strength and deformability estimates could lead to significantly higher support level requirements to ensure global stability than if  
Figure 9: Strength and deformability estimates for the same rock mass with and without scale consideration (see Figures 7 and 8 for GSI estimates). Note the UCS strength estimate for this example is 100 MPa. the V-GSI estimates are adopted. The input and output generated with RSDData are shown in Figure 9 (RHS), while the strength curves with increasing stress are plotted on the LHS. While a conservative design is better than the opposite, the reverse is also possible if scale is not considered when assessing GSI ratings without considering scale:  
* For example, if the graphically estimated GSI for a rock mass is considered to be Blocky or Massive with very good to good joint surface conditions (a GSI of around 70-75) without considering scale, this too can result in overly optimistic strength and deformability estimates compared to a very blocky rock mass with a VFC of say 15-20 fractures/m3 with a V-GSI of 60 to 65. * If the graphical non-scaled GSI range is used for design, this can result in deformation and/or rock mass failures compared to the scaled V-GSI estimates. This does not mean the purpose of including scale improves accuracy, rather it can provide a better-calibrated assessment of the GSI ratings on the chart and therefore more reliable strength and deformability estimates that consider scale. Readers interested in other examples that compare GSI ratings using graphical charts and quantified charts are referred to [PERSON], [PERSON], and [PERSON] (2016) [28].