esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### The Influence of the Number of Tenth on the Volume Fraction of Gas in Key Parts  
The cavitation phenomenon in gear pumps most often occurs in the gear meshing area, near the unloading groove and the inlet and outlet area of the pump, where the gear meshing area is prone to bubbles due to severe pressure changes. Although the unloading groove reduces trapped oil, it may cause a pressure drop. The inlet and outlet areas, especially the inlet area, are prone to cavitation due to the increase in the flow rate and the decrease in the pressure. Leakage flow from side faces and radial clearances can also cause pressure drops. Relatively speaking, the phenomenon of trapped oil does not occur in the circular arc gear pump, so it is not necessary to consider the cavitation problem caused by the unloading tank [30; 31].  
As shown in Figure 5, the distribution of the gas volume fraction of the pump at a speed of 10,000 RPM exhibits an order from high to low: gear cavity region, inlet cavity, and outlet cavity. The area of cavitation severity is mainly in the meshing gap and meshing area. The gas content in the gear cavity region decreases significantly with the increase in the number of gear teeth, and the volume fraction of gas in the gear cavity decreases by 54.89% for tooth number nine compared to tooth number six. Compared to the inlet and outlet cavities in the different tooth numbers, the gas content is roughly the same, with an average value of about 3.42% and 0.69%, respectively, and a very small difference. The total gas volume fraction inside gear pumps with six and nine teeth is 12.06% and 7.33%, respectively, and the total gas content inside the pump with tooth number nine is reduced by 39.22% compared to that with tooth number six.  
Because the gas content of the gear pump into and out of the mouth is less affected by the number of teeth, the focus of the discussion is the volume fraction of gas in the gear cavity.
## 3 Results  
### Climate Change  
In order to establish relationships between predicted burned area and climate change meteorological data, Table 5 shows the average values of P, H, T, and V for the 2000-2014 period and 2071-2100 period in both selected scenarios (RCP4.5 and RCP8.5). These scenarios assume pathways to different target radiative forcing at the end of the 21 st century [59]. For example, scenario RCP4.5 figures that an increase in radiative forcing of 4.5 W/m\({}^{2}\) can be produced by the end of the century relative to preindustrial conditions. Both precipitation and humidity remain invariable in the four zones due to their proximity. Future average values for precipitation are always higher than in the last 15 years of historical available data (2000-2014), reaching an average increase around 20% of rainfall, except in the wettest zone of the region (Zone 2) where there is a slight decrease of 3% in the RCP8.5 scenario. Average temperature rises between 2 and 4 \({}^{\circ}\)C in RCP4.5 and RCP8.5, respectively, in all zones, except in Zone 2 where the temperature increase is even higher, more than 6 \({}^{\circ}\)C in the RCP8.5 scenario. As a result of this overall increase in the region, climatic differences between zones are reduced at the expense of global warming. Another important factor of burned area fire size is wind velocity, which is also increased in all regions an average of 15-25%, especially in northwest-southeast direction due to the mountain ridges in this direction [58].  
Considering that fire conditions are very favorable when FWI \(>\) 16 [63; 65], in Table 6 a comparison is shown between the number of fires in the available data series period (2000-2014) and predicted fires for both future scenarios (RCP4.5 and RCP8.5) in the long-term period (2071-2100).  
The results indicate that the number of fires in the future for both scenarios will significantly increase in all zones except Zone 3 and the RCP4.5 scenario, where the number of fires appears to be similar to the number recorded in the 2000-2014 period. However, the RCP8.5 scenario doubles the number of fires per year in all zones, even tripling the number in Zone 4, moving up from an average of 37 fires per year in the period 2000-2014 to 106 fires per year, on average, in the long term (2071-2100).
## 2 System Architecture and Methods
### Safety and Privacy Measures  
Although collaborative robots (cobots) are designed with inherent safety features for human interaction, we implemented additional precautions in our setup. Specifically, we established a designated safety zone for interactions between the robot and children. Moreover, children were always supervised during the process to ensure utmost safety.  
Regarding privacy, we secured written consent from the parents before participation, ensuring that any data collected would be utilized exclusively for academic research purposes and in strict adherence to privacy and data protection laws currently in effect in Chile.
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 4 Markov Decision Process
### Constraint Function  
To guarantee the channel quality of cellular links, the signal-to-interference plus noise ratio (SINR) between the mobile node and relay node must be bigger than the threshold \(k\). We can obtain the constraint function \(C\), which can be defined as  
\[C=\frac{D^{-\alpha}\cdot T\cdot|g|^{2}}{\sigma^{2}+I}, \tag{4}\]  
where \(g\) is the channel response of the mobile node. \(\sigma^{2}\) is the variance of the additive white Gaussian noise (AWGN) and \(T\) is the transmission power of the mobile node. \(I\) is the interference of the mobile node.  
\begin{table}
\begin{tabular}{c l} \hline
**Notation** & **Description** \\ \hline \(S\) & State space \\ \(S_{t}\) & State at the decision epoch \(t\) \\ \(D\) & State for representing the distance between the mobile and the relay node \\ \(M\) & Memory buffer state space of the mobile node \\ \(A\) & Action space \\ \(A_{t}\) & Action chosen at the decision epoch \(t\) \\ \(g\) & Channel response of the mobile node \\ \(\sigma\) & Variance of the AWGN \\ \(T\) & Transmission power of the mobile node \\ \(I\) & Interference of the mobile node \\ \(\gamma\) & Iterations \\ \(B\) & Bandwidth \\ \(\eta_{T}\) & Average data rate \\ \(\eta_{C}\) & Average of the SINR \\ \(\delta_{C}\) & Threshold of the constraint \\ \hline \end{tabular}
\end{table}
Table 1: Summary of notation.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 4 Energy Harvesting for TDS-OFDM-NOMA
With this assumption, the underwater acoustic channel can be given by \(\ Y=\sqrt{hP}X+Z\), where \(Y\) and \(X\) denote the channel input and output, respectively, and \(\ Z\sim\mathcal{CN}\big{(}0,\sigma_{A}^{2}+\sigma_{cov}^{2}\big{)}\). In the energy part of the receiver, we harvest the energy from the received wireless signal. The received signal \(y(t)\) is converted to be a direct current signal \(i_{DC}(t)\) by a rectifier, \(i_{DC}(t)\); this rectifier can be implemented by a Schottky diode and a passive low-pass filter (LPF). The direct current is then used to charge the battery and store the energy.
## 2 Materials and Methods
### Monitoring Methodology  
Monitoring took place during the summer months of 2023. The following equipment was used (Table 3):  
* Temperature loggers, type TERMIO-15;
* Climate loggers, type LB-510 TWPL, recording temperature, humidity, atmospheric pressure, and lighting; \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**External Walls**} & \multicolumn{2}{c}{**Walls between Windows**} & \multicolumn{2}{c}{**Summit Walls**} & \multicolumn{2}{c}{**Windows**} & \multicolumn{2}{c}{**Roofs**} \\ \cline{2-11}  & **U** & \(\mathbf{U_{max}}\) & **U** & \(\mathbf{U_{max}}\) & **U** & \(\mathbf{U_{max}}\) & **U** & \(\mathbf{U_{max}}\) & **U** & \(\mathbf{U_{max}}\) \\ \hline A1 * & 0.29 & 0.20 & 0.21 & 0.20 & 0.28 & 0.20 & 1.09 & 0.90 & 0.56 & 0.15 \\ A2 & 0.33 & 0.20 & 0.33 & 0.20 & - & 0.20 & 1.64 & 0.90 & - & 0.15 \\ A3 & 0.32 & 0.20 & 0.32 & 0.20 & - & 0.20 & 1.16 & 0.90 & - & 0.15 \\ A4 * & 0.29 & 0.20 & 0.21 & 0.20 & 0.38 & 0.20 & 1.16 & 0.90 & 0.56 & 0.15 \\ A5 & 0.29 & 0.20 & 0.21 & 0.20 & 0.38 & 0.20 & 1.15 & 0.90 & 0.56 & 0.15 \\ A6 * & 0.29 & 0.20 & 0.21 & 0.20 & - & 0.20 & 1.14 & 0.90 & - & 0.15 \\ A7 & 0.29 & 0.20 & 0.21 & 0.20 & - & 0.20 & 1.18 & 0.90 & - & 0.15 \\ A8 ** & 0.32 & 0.20 & 0.32 & 0.20 & - & 0.20 & 1.33 & 0.90 & - & 0.15 \\ A9 & 0.32 & 0.20 & 0.32 & 0.20 & 0.20 & 0.20 & 0.89 & 0.90 & - & 0.15 \\ A10 ** & 0.32 & 0.20 & 0.32 & 0.20 & 0.20 & 0.20 & 1.16 & 0.90 & - & 0.15 \\ \hline \end{tabular} * the same floor plan. ** the same floor plan.  
\end{table} 
Table 1: Actual thermal transmittance coefficients U [W/(m\({}^{2}\cdot\)K)] of the apartments’ main partitions and their current acceptable levels U\({}_{\rm{max}}\) [W/(m\({}^{2}\cdot\)K)] according to national regulations.
## Materials and Methods
### Classification Models  
#### 2.4.1 Random Forest  
Random forest (RF) is a non-parametric and nonlinear classification algorithm first proposed by [PERSON] and later improved by [PERSON] [33; 34]. It utilizes the advantages of ensemble learning methods to quickly process high-dimensional data and effectively prevent overfitting. RF has been successfully applied to many neighborhoods, such as image classification, remote sensing image endpoint recognition, and hyperspectral data analysis. The main idea of RF is based on bootstrap-resampling technology, overcoming the weakness of single decision tree classification. Multiple classification decision trees are generated from the training variable set, forming a random forest to complete the regression classification process. The importance of wavelength is measured using the gradient descent of the Gini coefficient and the variable splitting results of all decision trees are jointly analyzed to reduce the correlation between each classification tree. Measuring the importance of classification variables improves the performance of the combined classifier, and ultimately the classification results are determined by the voting method. In this study, the range of the number of decision trees was set to 1-1000; the specific number of decision trees and the corresponding number of split variables were optimized using a 10-fold interactive validation method.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 4 Conclusions and Future Work
### Future Work
#### 4.1.2 RF Noise Impact on Prototype System  
One factor discovered in verification testing of a prototype is the sensitivity of the antenna to noise. For example, minor changes in the exact positioning of the PCB and microcontroller under the ground plane could vary the measured noise by as much as 3 dB. Clearly, the antenna receives RF interference from an unshielded device in the immediate vicinity (e.g., 10 to 20 cm). In fact, it was precisely this interference that played a role in the design of a online learning direct-to-satellite packet scheduling algorithm; since no method could possibly account for the range of possible housing, ground plane, and even power supply designs, as well as varied site conditions and lines of sight, there could be no single ultimate \"good\" pass model, so a learning-based approach is used.  
Conceptualization, G.K. and D.P.; methodology, G.K.; software, G.K.; validation, G.K.; formal analysis, G.K.; investigation, G.K., Z.Z. and D.P.; resources, Z.Z. and D.P.; writing--original draft preparation, G.K. and Z.Z.; writing--review and editing, G.K., Z.Z. and D.P.; visualization, G.K., Z.Z. and D.P.; supervision, Z.Z. and D.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSERC Canada Discovery Grant.  
Not applicable.  
The data used in this study was generated by a simulation program placed in the repository: [[https://github.com/garrettkinman/Self-Learning-Satellite-Pass-Selection](https://github.com/garrettkinman/Self-Learning-Satellite-Pass-Selection)]([https://github.com/garrettkinman/Self-Learning-Satellite-Pass-Selection](https://github.com/garrettkinman/Self-Learning-Satellite-Pass-Selection)) (accessed on 31 May 2023).  
The authors declare no conflict of interest.
## 7 Risk Analysis
### Machine Learning
#### 7.9.1 Tampering  
1. **Component** _Security risk_: The ML component is placed at a remote location and carries out the execution of algorithms to determine the initiation of an emergency response. Tampering with this component can lead to incorrect prediction, which means that some possible emergencies are not correctly detected. Typical attacks consist of data poisoning and other attacks which lead to confusing the model.  
_Security measures_: Data validation must be put in place by regularly validating the data used for training.
2. **Ports** _Security risk_: An attacker can tamper with the connection between the ML and the cloud via network, thus making it a high risk level. The safety of the passengers can be highly affected. For instance, if a crash occurs, the ML program will not be able to communicate with the cloud. In this situation, there will be a big financial impact because if there is a false alarm, sending out the emergency services still costs a lot.  
_Security measures_: The communication between the ML and the cloud can be ensured through encryption. Communication between the cloud and ML units should implement encryption mechanisms to ensure that the communication remains secure and unaltered.
## 5 Discussions  
### Adaptability of CLT System  
The results of the simulation clearly show that the CLT system has great potential for the reduction of heating energy, which indicates that timber is an ideal building material for regions with considerable heating requirements. Previous research has found that space heating energy accounts for approximately 30% to 40% of total energy consumption in North China [61]. When taking the total amount of energy consumption into consideration, buildings in the Severe Cold, Cold, and Hot-Summer Cold-Winter regions consumed more heating energy than those in other regions. However, in the Hot Summer Warm Winter region and the Temperate region, the energy consumption of the CLT system is nearly identical to that of the building with a RC structures. Policy makers in China could reduce office building energy requirements by encouraging the application of the CLT system in the Cold, Severe Cold, and Hot Summer Cold Winter regions.  
The results also indicate that the CLT system may cause overheating in comparison with RC structures. This would mean that CLT buildings would require additional cooling energy in the summer. Previous research has also highlighted that some bio-based materials such as wood may cause overheating due to their lower values of thermal mass [47; 48; 49; 62]. Thermal mass is a property of the mass of a building which enables it to store heat, providing inertia against temperature fluctuations. Higher amounts of thermal mass at the inner side of the thermal insulation appear to be beneficial with regard to improving thermal comfort [63; 64]. Hence, from an energy conservation viewpoint, CLT may not be such a good alternative building material for regions without considerable heating requirements, but this aspect should be verified by further studies.
## 3 Hypotheses and Model Development
### Top Management Support  
Top management support [18] refers to the top management's attitude regarding the concerned technology as well as the extent of support given to the adoption. In terms of a strategic perspective, the successful implementation of CC in HEIs depends on the capabilities of top leadership or management to drive the change from traditional deployment to CC through an official pro-cloud strategy [148]. The decision-makers' awareness and consensus are vital. Their support will ensure what cloud services are needed and what type of cloud deployment is best for HEIs settings. To do that, the decision-makers have to understand the benefits of cloud-based services, the value they can add to the educational services, and how to migrate to the CC environment [149]. Accordingly, it is hypothesized that:  
**Hypothesis 7 (H7).**_Top management support positively influences CC adoption in HEIs._
## 5 Potential 6G Applications in the Maritime Sector  
The unique operational demands of the maritime industry, ranging from long-distance voyages in remote waters to latency-critical port operations, require next-generation communication systems that are intelligent, ultra-reliable, and globally available. In this section, we present five representative 6G-driven maritime use cases, although plenty of use cases and other applications may be considered.
## 3 Materials and Methods
### Analysis of the Results  
Statistical analyses were performed with the IBM SPSS v23 statistical software (IBM Corp., Armonk, NY, USA). First of all, descriptive statistics were analysed, and conclusions were obtained about not only the perception of the importance of usage properties but also about their assessment of each technology.  
As the data from questionnaires could not be considered to follow normal distribution (based on the Kolmogorov-Smirnov test), the following analyses were applied to check if significant differences could be detected:  
* Non-parametric Friedman tests (repeated measures) by taking, as dependent variables, the ratings of perceptions for each usage property and the display technology. Technologies 2D, 360\({}^{\circ}\), and AR were compared by half the sample, and 2D, 360\({}^{\circ}\), ni_VR, and i_VR by the other half (as participants used either AR or VR, but not both). In each group, pairwise comparisons were performed by adjusting significance values using the Bonferroni correction for multiple tests. An a priori power analysis was performed by the Wilcoxon signed-rank test for matched pairs by taking the following as input parameters: power of 80%, error probability \(\alpha\) of 0.05, effect size of 0.5 based on a mean difference of 0.5 and SD of difference = 1. The analysis showed a total sample size of 35 participants set for two tails.  
Figure 5: Experiment flow chart.  
* Independent-sample Mann-Whitney U tests were applied to verify possible differences between the usage properties between AR and ni_VR and also between AR and i_VR.
* Independent-sample Mann-Whitney U tests were also applied to check possible differences in usage properties' ratings depending on product type. The properties' ratings for each technology were taken as dependent variables and product type as the independent variable.
## 8 Conclusions and Future Work  
This paper presents a completely decentralized architecture for safety-critical industrial supply chain tracking based on a secure element and blockchain. The technology forces the honest behavior of the stakeholders and provides a strong guarantee of the quality of the product to final users or third-party observers. To achieve this, NFC tags with asymmetric cryptography capabilities are integrated in the parts manufactured and exchanged in the considered supply chain. With a tag, the part receives a digital identity inside the blockchain infrastructure and can trigger transactions. The blockchain-based supply chain manages all the parts' information and cryptographically certifies its owner at every moment. Using a novel scheme, the smart contract forces the stakeholders to honestly verify the parts along any asset transference, and the blockchain itself is witness of the transfer process. The solution is implemented over the Hyperledger Fabric framework, and it uses the \"private collection\" structures to protect the individual privacy of each stakeholder. As next steps, other functionalities are envisioned, such as the ability to provide the part's physical owner access to certain blockchain privileges or specific data. Additionally, following other tracking solutions, a global navigation satellite system sensor could be integrated in the tag to include information about the position signed by the HSM, though it would present challenges such as the need for an active tag with a power supply.  
Conceptualization, E.G.-M., D.M. and V.S.; methodology, E.G.-M.; software, E.G.-M., D.M. and J.L.T.L.; validation, E.G.-M., D.M. and V.S.; formal analysis, E.G.-M. and D.M.; investigation, E.G.-M., D.M. and J.L.T.L.; writing--original draft preparation, E.G.-M., D.M. and V.S.; writing--review and editing, E.G.-M., D.M., V.S., E.C., L.P. and J.L.T.L.; visualization, E.G.-M., D.M., V.S., E.C., L.P. and J.L.T.L.; project administration, E.G.-M.; supervision E.C. and L.P. All authors have read and agreed to the published version of the manuscript.  
This research has been founded by the European Union's Horizon 2020 Research and Innovation program under grant agreement No. 871518, a project named, A COmprehensive cyber-intelligence framework for resilient coLLABorative manufacturing Systems, COLLABS [55].  
Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Security** & **Elkihyaoui** & **Saikat** & **ULRMAPC** & **LBRAPS** & \\
**Protection to:** & **et al.**[15] & **et al.**[33] & [17] & [16] & **Own** \\ \hline
**Key disclosure** & X & X & X & X & ✓ \\ \hline
**Replay attack** & X & X & ✓ & ✓ & ✓ \\ \hline
**MITM attack** & ✓ & X & ✓ & ✓ & ✓ \\ \hline
**Tracking attack** & ✓ & X & ✓ & ✓ & ✓ \\ \hline
**Prediction attack** & X & NA & ✓ & ✓ & ✓ \\ \hline
**Delay attack** & NA & NA & X & ✓ & ✓ \\ \hline
**Elevation of privileges** & NA & X & X & X & ✓ \\ \hline
**Decentralized** & ✓ & ✓ & ✓ & X & ✓ \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of decentralized-based SCMs.
However, the number of _GGPPS_ paralogs were varied within species, as high as 12 in _Arabidopsis_[16] and as low as one in _Chlamydomonas reinhardtii_[17]. Specifically, three _GGPPS_ genes were found in _H. pluvialis_ based on a RNA-Seq transcriptome analysis (Genbank accessions: KX236181-KX236183). With the advantage of obtaining the full-length of transcripts to identify isoforms, alternative splicing, fusion transcripts, LncRNA, and new homologous genes, the single-molecule real-time (SMRT) long-read isoform sequencing (Iso-Seq) developed by Pacific BioSciences (PaciBio) is more preferred to discover more paralogs over the RNA-Seq technology [18]. As one of successful examples, more than 113,000 novel splice isoforms were discovered in a Iso-Seq data of _Sorghum bicolor_ L. [PERSON] and some of them are functional which confirmed by additional experiments of gene cloning and RT-PCR [19]. Since the whole genome sequence of _H. pluvialis_ is available [20], it is possible to discover more reliable novel _GGPPS_ genes from the Iso-Seq data. The main objectives of this study were to explore more _GGPPS_ paralogs in _H. pluvialis_ using an Iso-Seq data, to determine their evolutionary relationship, and to characterize their performance on astaxanthin biosynthesis. By comparing the productivity of astaxanthin using different _GGPPS_ genes from _H. pluvialis_, it is expected to arrange a more efficient pathway for astaxanthin biosynthesis.