esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 4 Results
### Far Field Acoustic Data  
Far field acoustic emissions are important to UAS operating near populated areas to avoid being a nuisance. Methods to reduce acoustic emissions may include baffling, tuning engine exhaust, and employing multiple large propulsors to reduce the exhaust velocities of the main engine and propulsors. In addition to sound pressure level, it is important to manage the acoustic frequency of emissions to avoid irritation and increase dissipation. Acoustic emissions from VTOL flight tests were collected using the four half-inch microphones placed about 60 m from the vehicle to capture far-field acoustic measurements. Figure 11 shows the microphone arrangement.  
During flight tests, acoustic measurements were collected for takeoff, hover, transition flight, and landing. Data were recorded in the time domain and postprocessed with a Fast Fourier Transform to present sound pressure level (SPL) in the frequency domain, as shown in Figure 12. The lower blue line shows ambient noise recorded at the field prior to flight tests. Ambient noise below 0 dB was due to the acoustic measurements captured below the threshold of the microphones. Peaks in the acoustic data of the microphones are due to the blade passing frequencies. However, the observed SPL throughout the flight test did not exceed the expected noise levels of similar class unmanned aircraft. The peak sound pressure level was at the low end of the human auditory range at close to 20 Hz with a 58 dB SPL. The peak auditory noise was at around 1 kHz with a 46 dB SPL.  
The compressor and turbine operate at such high rotational speeds that the resulting high frequencies are greater than 70 kHz, which is outside of human hearing range. The broadband noise captured during the flight test is attributed to the jet exhaust, ambient noise, propeller, and propeller slipstream interactions with the airframe. Acoustic emissions could be reduced by lowering propeller and rotor tip speeds, and by including acoustic dampening materials in the fuselage; however, these may affect system weight and performance. The aircraft currently has the largest VTOL rotor size allowable for sufficient clearance.  
Figure 11: Microphone configuration for VTOL flight acoustic emissions measurements.
# Security Attack Behavioural Pattern Analysis for Critical Service Providers
## 4 Cyber Attacks
### Vulnerability  
The classifier of attack vector was used to categorise cyberattacks according to the abstract vulnerability exploited, or that the threat actor attempted to exploit, to compromise the perimeter of the organisation's cyber infrastructure. Among the total number of cyberattacks reported, the most common category, based on the analysis, was concluded to be **social engineering**. In total, 32 percent of the IT-incident reports that described cyberattacks, or a total of 81 cases, were categorised as having utilised social engineering techniques to gain initial access. This category includes cases of **phishing, spear phishing**, and other forms of manipulation attempts utilised with the aim of trying to deceive the recipient into responding or performing certain actions. The prevalence of social engineering incidents suggests that various deceptive tactics are commonly employed to gain unauthorised access to critical service providers. The second most prevalent method of attack was **insufficient capacity**, which was found to be responsible for 28 percent of all reported cyberattacks, totaling 72 cases. This category refers to incidents in which the attacker attempted to take advantage of the limitations in the capacity of cyber infrastructure resources, as illustrated by distributed denial of service attacks. Afterwards, the insufficient category accounts for a low percentage of cyberattacks.  
**Insufficient authentication** involves techniques for bypassing authentication procedures, such as employing brute-force attacks. Meanwhile, the term \"unknown\" refers to incidents in which the reporting organisations did not provide sufficient details in their description of the event to determine the specific attack method used. This was later accompanied by **inadequate input validation** and **misconfiguration** at 9%, 5%, and 5%, respectively. The other category covers reports that described an attack vector that did not align with any other specified categories. These primarily describe common vulnerabilities in systems and code. Based on attack vector usage, social engineering cases increased in April 2019-2020 and April 2020-2021. Social engineering techniques were used in 44% and 49% of reported malicious incidents during these periods. After that, social engineering decreased in frequency, accounting for only 15% of reported cyberattacks in 2022-2023.  
This could indicate that threat actors are increasingly making use of other types of attack vectors. Alternatively, the organisations targeted are becoming better at protecting themselves against these types of attacks and, therefore, do not report them to the same extent. Meanwhile, using capacity limits as an attack vector saw a sharp increase in the April 2022-2023 period, going from representing approximately 20 percent of cyberattacks in April 2019-2022 to 52 percent.
## 4 Case Study
### Impact of Multiple Slices on User Satisfaction
#### 4.5.3 Average Latency  
Figure 7 delves into the realm of latency satisfaction, shedding light on the influence of the number of slices on the user experience, depending on the varying number of VUs. The results portray enhanced performance in terms of latency satisfaction as the number of slices increases. This improvement can be attributed to the increased bandwidth available for communication, resulting in reduced communication and transmission delays. Additionally, the increased resources in each slice and layer contribute to serving VU demands with reduced computation delay.  
The observed decrease in total latency, resulting from optimized communication and computation resources, underscores the efficacy of the proposed DLaaS structure. Interestingly, the figure reveals that even with a doubling of the number of VUs, the latency KPI does not undergo a proportional decrease. This phenomenon signifies the strategic utilization of additional resources made available through multiple slices, showcasing the system's resilience to scalability challenges.
## 6 Results and Discussion
### Static Analysis
Overall, the preload effects of the C-GFB are smaller than the mechanical preload effects of the shimmed GFB, in particular, at the loaded region \(\theta=180^{\circ}\). This result implies that the gas-film pressure can reduce the piezoelectric preload of the C-GFB owing to the low structural stiffness, particularly in the case of high-speed and high-driving voltage operation. In this scenario, the C-GFB performance may become significantly different from that of the shimmed GFB. For the C-GFB case in this study, the base piezoelectric preload under no-load condition of 72.92 \(\upmu\)m under driving voltage of 150 V is reduced differently at each preload location of 60\({}^{\circ}\), 180\({}^{\circ}\), and 300\({}^{\circ}\), and eventually becomes 65.36, 54.06, and 64.62 \(\upmu\)m, respectively, at the rotational speed of 90 krypm. As a result, the mechanical deflection reduces the preload effects of the PZT actuators and hinders the pressure generation locally during bearing operation. This is because it alleviates the wedge effect of the bearing surface. This is a significant discrepancy between the C-GFB and traditional shimmed GFBs during operation, in terms of the generation of preload. The result indicates that the PZT stack stiffness should be carefully designed and selected in the bearing design process, to maximize the control effects of the PZT actuators. Figure 12 illustrates the predicted journal eccentricity and attitude angle versus rotational speeds. The journal eccentricity is normalized with the relation \(\epsilon_{0}=\sqrt{\varepsilon_{\chi}{}^{2}+\varepsilon_{\chi}{}^{2}}\)  
Figure 11: Predicted piezoelectric preloads versus rotational speeds with respect to preload locations of 60\({}^{\circ}\), 180\({}^{\circ}\), and 300\({}^{\circ}\) for the C-GFB. Figure 10: Predicted local gas-film pressure versus rotational speeds with respect to circumferential locations \(\theta=60^{\circ}\), 180\({}^{\circ}\), and 300\({}^{\circ}\) for C-GFB and shimmed GFB. and the journal attitude angle is defined as \(\phi_{0}=tan^{-1}(\varepsilon_{Y}/\varepsilon_{X})\). The eccentricity decreases overall, and the attitude angle displays a decreasing trend with increasing rotational speeds. Notably, the C-GFB exhibits higher eccentric steady-state operation from the bearing center with greater eccentricities than the shimmed GFB due to decreases in the piezoelectric preload of the C-GFB, in particular at \(\theta\) = 180\({}^{\circ}\). This is because it is a key location for determining the journal eccentricity, from among the presented three PZT actuator locations. Meanwhile, the C-GFBs have larger attitude angles than the shimmed GFBs, particularly at the high speed region aside from the case of 50 V, where the lubricant begins to stiffen and the dominance of the foil structures increases. Note that small journal attitude angles generally imply reduced cross-coupled effects in the gas film, thereby exhibiting higher rotordynamic stability.
## 3 Results
### Characterization of Physical Properties
#### 3.1.3 Energy Dispersive X-Ray Spectroscopy, EDS and Elemental Composition  
The characteristic EDS spectra for the layers obtained in different deposition conditions are represented in Figure 4. From the analysis of these spectra, it can be observed that Ag is present in all types of processes, with comparable intensities in the spectrum for processes without adding oxygen. In the process of adding oxygen, there is an increase in the intensity of Ag and a decrease in the intensity of Ti, indicating a significant change in the ratio between their concentrations in the layer composition.  
The inset of Figure 4 indicates a different mode to visualize the evolution of the deposited layers composition, representing the evolution of the ratio between Ag and the main element in the oxide matrix, Si or Ti, as well as the ratio between Ag and O. From this representation, an increase in these ratios can be observed when moving from SiO\({}_{2}\) to the TiO\({}_{2}\) matrix, as well as when moving from the process without oxygen to the one with addition of oxygen. In the same figure, the evolution of Ag concentration is represented, being also observed that it is higher for the process in which a TiO\({}_{2}\) target is used compared to those with a SiO\({}_{2}\) target, respectively, for the process in which an Ar+O\({}_{2}\) mixture is used compared to the one using only Ar. In conclusion, the layer with the largest amount of incorporated Ag, at the same total thickness, is obtained in the optimized process with the oxygen content of 2.5% of the total gas flow.
# Energy Productivity Potential of Offshore Wind in Poland and Cooperation with Onshore Wind Farm
## 3 Results and Discussion
On 5 March 2022, during the evening, the peak energy consumption (hours 18-22) and the potential energy production from offshore wind would be zero. At the same time, the actual energy production from solar PV was zero, and the hourly energy production from onshore wind was between 81 and 342 MWh. In contrast, during the morning peak consumption (hours 7-10, 6 March 2022), the potential energy production from offshore wind (despite the lower installed capacity) would exceed the total production from onshore wind. For hour 8 (6 March), there would be 321 MWh of energy from offshore wind and 49 MWh of energy from onshore wind. The results of the simulation for 23 September 2022, the day on which the capacity market emergency alert was announce by the Transmission System Operator in Poland [37], are presented in Figure 6.0  
Figure 6.1 Energy generation and wind speed data for 23 September 2022. Source: own calculation based on [14; 36]. Figure 6.2 Energy generation and wind speed data for 1 August 2022 and for 5/6 March 2022. Source: own calculation based on [14; 36]. During real emergency hours on the capacity market, i.e., 19:00-21:00, on 23 September 2022, energy generation from onshore wind was approximately 100 MWh (i.e., less than 2% of the _TNP_ nominal capacity). Energy production from solar PV was zero, and the calculated potential production from offshore wind was zero MWh/MW as well. Based on the formulae shown in Section 2.4, the capacity factor was 25.5% in 2021 (CF, the value of energy production, was 15.2 TWh, with an average installed capacity of 6.8 GW); for 2022, details on a monthly scale are presented in Figure 6.3  
The highest value of the Capacity Factor for onshore wind was observed in February 2022 (55%). The simulated value of the Capacity Factor for offshore wind was also the highest in February (almost 82%). The lowest value was observed in August: 15% for onshore wind and 35% for offshore wind. The highest electricity demand was observed in January, and the lowest was observed in September. There is an observable dependence that in the months with higher demand, the capacity factor value is also higher. The total amount of generated electricity from PV and wind onshore and wind offshore compared to energy demand is shown in Figure 6.4  
Total energy production from 6 GW offshore wind can produced 29.2 TWh/year, based on the 2022 ERA5 wind speed data. This means that based on the [PERSON] et al. methodology [38], CO\({}_{2}\) emissions are reduced by 22.4 million Mg, and use of hard coal is reduced by 13.4 million Mg.  
Figure 6.5 Capacity Factor and monthly electricity demand values in monthly scale in 2022 (Poland). Source: own calculation based on [14; 36]. Figure 6.6 Monthly energy production values for existing technologies in 2022 (onshore wind and photovoltaic) and also potential energy production for 6 GW offshore wind.
## 4 Results and Discussion
### Estimation Performance Comparison between OSVR and SVR  
In order to quantify the estimation performance of OSVR, with the same training data as those selected in Section 4.3, RMSE of OSVR and SVR (parameters selection based on rule of thumb) were calculated, shown in Table 9.  
We can see from Table 9, through the optimization of the parameters of the SVR, the estimation performance has been significantly improved.
## 3 Results and Discussion
### SEM Images  
SEM images were acquired at five distinct scales: 2 mm, 100 \(\upmu\)m, 30 \(\upmu\)m, and 5 \(\upmu\)m. The images were captured using a 10.0 kV accelerating voltage with corresponding magnifications of 29\(\times\), 500\(\times\), 2000\(\times\), and 1000\(\times\), respectively. A large-area detector (SE(L)) was utilized to detect secondary electrons, providing detailed surface morphology. The microstructures within the samples were examined in detail and are presented in Figure 8.  
In the context of injection molding, the polymer exhibits a more fluidic structural behavior, primarily influenced by temperature and pressure. Achieving homogeneous cooling within the mold results in the formation of a smooth surface with minimal void formation during material compression. This phenomenon is clearly discernible in images captured at scales of 2 mm, 100 \(\upmu\)m, and 30 \(\upmu\)m. At the finer 5 \(\upmu\)m scale, the presence of  
Figure 7: Illustration of CMM measurement points.  
distinct gaps and borders becomes readily apparent. This observation indicates that the polymer is fully compressed, leading to a low surface roughness.  
A thorough examination of the 3D-printed parts using the FDM method reveals the presence of layers in 2 mm scale images. A closer inspection of 100 \(\upmu\)m scale images reveals  
Figure 8: SEM images of ABS 3D-printed and ABS-Injection-molded parts at different scales.  
layer differences in the dark top, along with fluctuations and gaps in the microstructure. The FDM method involves the non-complete melting of the material, leading to a homogeneous fluid structure. This results in the formation of a porous and heterogeneous surface. The observed heterogeneity and fluctuations are believed to be attributable to the three distinct monomers, namely, acrylonitrile butadiene styrene (ABS), which constitute the polymeric material. It has been established that acrylonitrile contributes to the enhancement of hardness and chemical resistance, while butadiene confers impact resistance and styrene ensures enhanced brightness and ease of machinability.
## 8 Conclusions  
The evaluation of the SKATEBOARD system, as presented in this study, provided a holistic understanding of its performance, user satisfaction, and areas for improvement. Our analysis encompassed both quantitative Likert scale responses and qualitative open-ended feedback, offering a comprehensive view of user perspectives.  
Quantitatively, we observed a diverse range of opinions among users across various key factors. User satisfaction, system effectiveness, support quality, usefulness, and learnability were assessed, and responses spanned the spectrum from highly satisfied to neutral to less satisfied. This diversity of opinions underscored the multifaceted nature of user experiences with the SKATEBOARD system.  
Qualitatively, we delved deeper into user feedback, employing sentiment analysis and topic modelling. This analysis revealed nuanced insights into user sentiments and key themes within their responses. Word clouds and topic distribution charts visually represented the most-prominent themes and sentiments associated with each key factor, providing valuable context to the quantitative findings.  
In conclusion, the evaluation of the SKATEBOARD system yielded valuable insights that can guide its future development and refinement. The diverse perspectives and feedback from users serve as a foundation for making informed decisions and enhancing the system to better meet the varying needs and preferences of its user base.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Query Phase
#### 5.4.3 Updating Key-Value Pairs  
Using the method of creating composite keys, we can effectively avoid the complex data filtering processes typically involved in conditional queries. When the \(getState\) method is executed, it retrieves a result set that meets all the criteria, significantly enhancing the efficiency of data queries. However, there may be challenges associated with updating key values when storing data records using composite keys. Specifically, when new records are added to the ledger, the \(k_{j}\) created for the new record might coincide with one of the existing keys in the ledger, where none of the values corresponding to \(k_{j}\) match any existing records. In such cases, it becomes necessary to update the existing key-value pairs.  
In the method proposed in this scheme, the process for updating key-value pairs involves the following steps. First, query the existing value \(N_{j}=\{N_{EMR_{\textit{hash}_{i}}}{}_{1},N_{EMR_{\textit{hash}_{i}}}{}_{2},\ldots,\)\(N_{EMR_{\textit{hash}_{j}}}\}(t\geq 1)\) associated with the \(k_{j}\). Next, combine the new \(N_{EMR_{\textit{hash}_{i+1}}}{}_{1}\) with \(N_{j}\) to form a new set \(N_{new}\), which will serve as the updated value linked with the \(k_{j}\). Therefore, Algorithm 2 can be enhanced by introducing a key-value pair updating process, as shown in Algorithm 3. Finally, invoke the \(putState(k_{j},N_{new})\) function to submit the new key-value pair to the blockchain for persistent storage. \(putState\) is an API provided by Hyperledger Fabric for submitting the latest state. In using the \(putState\) function, the new record value will overwrite the old value in the state database, thereby achieving the objective of updating the data.  
```
0: a set of records \(R=\{r_{1},r_{2},\ldots,r_{t}\}(t\geq 1)\)
0: a set of key-value pairs \(KVP\)
1: Declare an empty set \(KVP\)
2:// \(r_{i}=\{N_{EMR_{hash}},A_{i}\}\), where \(A_{i}\) is a set of attributes with elements that can be used as a query condition
3:for all\(r_{i}\in R\)do
4: Compute \(k_{i}\) based on Algorithm 1
5:// Define \(ls\) as the latest state of \(k_{i}\) in the blockchain
6:\(ls\gets getState(k_{i})\)
7:if\(ls\) is not null then
8:// Define \(N\) as the old set of \(N_{EMR_{hash}}\)
9: Add \(N_{EMR_{hash}_{i}}\) into \(N\)
10:\(kvp_{i}=(k_{i},N)\)
11:else
12:\(kvp_{i}=(k_{i},N_{EMR_{hash}_{i}})\)
13:endif
14: Add \(kvp_{i}\) into \(KVP\)
15:endfor
16:return\(KVP\)
```  
**Algorithm 3** Update key-value pairs
## 2 Experiment  
### Test Setup and Instrumentation  
The slug flow experimental investigation is examined in a pipeline situated in hilly terrain, as represented in Figure 2. The experimental arrangement consists of a test section, an air supply pipeline, an image acquisition device, and a data acquisition system, as displayed in the figure. The test pipe is constructed from organic glass and possesses dimensions of 26 mm inner diameter, 32 mm outer diameter, and a length of 6600 mm. Aluminum pipe clamps are employed to support the horizontally and inclined pipes, which are connected with organic elbows at angles of \(5^{\circ},10^{\circ},15^{\circ}\), and \(20^{\circ}\) within the test section. This configuration simulates a section of low-lying resembling a hilly terrain.  
For the test, the compressed air flow is furnished by an Atlas Copco air compressor and tank with a 1.5 m\({}^{3}\) capacity. Monitoring of the gas flow rate is done using the FESTO-565406 sensor, which possesses \(\pm\)0.3% accuracy. To regulate the inlet velocity in the pipeline, a pressure-reducing valve (IR3020-03) is employed. This valve offers a range of 0.01 to 0.8 MPa and a sensitivity of 0.2%. Moreover, pressure is measured using the MPM489 sensor, with an accuracy of \(\pm\)0.5%, installed at P1, P2, P3, and P4, as demonstrated in Figure 2.  
The sensors in this study are linked to a data acquisition instrument and a computer system consisting of an Advantech industrial computer IPC-610L and an acquisition card PCI-1710U. For visualizing the movement of slug flow under optimal lighting conditions, an imaging system is employed, which incorporates a high-speed camera with a 50 mm f/1.8D lens. This camera possesses a resolution of 1920 \(\times\) 1020 pixels and a capture rate of 1500 frames per second. The ambient temperature of the laboratory is maintained at approximately 25 \({}^{\circ}\)C, and the physical properties of the experimental fluid, including density and viscosity, are unaffected by the prevailing temperature and humidity. A summary of the fluid parameters is provided in Table 1.