esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Scalability Study
### Impact of Data Volume and Number of Clients  
A series of experiments was conducted to assess the impact of increasing data volume on the Smart Money DLT. A three-level scale of data volume with reference to the number of items included within an invoice was adopted, i.e., 10 items per invoice, 50 items per invoice, and 100 items per invoice. The total number of invoices was fixed to the value of 1000. The experiments were repeated for various numbers of clients [10, 20,..., 100], and the transaction load, i.e., the total number of invoices, was spread out amongst the clients so that the following Formula was satisfied:  
\[cl\times tx\text{ per client}=1000.\]  
This design made it possible to evaluate the impact of increasing numbers of clients on the network in addition to the effects of increasing data volume. Furthermore, all tests were executed twice: one set of runs with split payments enabled and one set of runs without split payments, where the full amount was paid directly to the seller with no involvement from HMRC.  
The results in Figure 8 show that the Smart Money DLT achieves similar performance for all data volume levels. By inspecting the families of curves (issue--split, issue--non-split, pay--split, and pay--non-split) there is no clear within-family separation, which shows that the number of items per invoice has no effect on performance when invoices are issued or paid. Additional findings can be made with regard to the number of clients in transactions. In Figure 8a, the curves follow almost a straight line, which shows that throughput is not affected by increasing the number of clients. In the case of payments, Figure 8c, throughput appears to slightly increase when the transaction load is spread out amongst more clients, which demonstrates high scalability. Transaction latency, Figure 8b,d, increases with the number of clients, albeit linearly. Overall, the results show that the Smart Money DLT demonstrates scalability when the data volume of transactions increases. Similar results are observed when increasing the number of clients in the network. The results also show that with split payments enabled, performance (in terms of transaction throughput and latency) drops by a factor of 1.5 to 2. This is caused by the extra computational burden required for calculating and transferring the VAT of a payment to HMRC for either issuing or paying an invoice.
## 2 Failure Rate Modeling in Ice Disaster Scenarios
### Fast Newman Algorithm-Based Grouping Model  
In disaster emergency resource dispatch, rational regional partitioning is crucial for improving resource allocation efficiency and reducing post-disaster dispatch costs. To achieve this, we adopt the fast Newman algorithm, a community detection algorithm based on [PERSON]'s modularity optimization approach. It is an accelerated community aggregation algorithm proposed by [PERSON] et al. [26], which builds upon the GN algorithm [27] to efficiently partition network structures. Compared with traditional community detection methods, the fast Newman algorithm offers significant computational efficiency advantages, making it well suited for complex and dynamic emergency resource dispatch tasks.  
The fundamental principle of the fast Newman algorithm is to initially treat each network node as an independent community and then iteratively merge nodes. Each merging operation is determined by the principle of maximizing the modularity increment \(\Delta Q\), prioritizing the combination of nodes that strengthen intra-community connections while minimizing inter-community links. The algorithm iterates by selecting merges that maximize the modularity increment until all nodes are merged into a single community or the modularity \(Q\) reaches its peak, after which it terminates and outputs the optimal community partitioning result. The detailed algorithmic steps can be found in [28].  
Figure 3 illustrates the community partitions generated by the modified fast Newman algorithm, which divides the network into four distinct zones, each delineated with a different colored boundary (Zone 1 to Zone 4). These partitions correspond to groups of nodes with strong internal connectivity and limited external links, facilitating efficient and localized resource scheduling while minimizing cross-regional complexity and costs. The key steps of the modified fast Newman algorithm are summarized in Algorithm 1, which provides the computational basis for the network partitioning adopted in this study.  
```
0: Network data, DERs allocation
0: Partitioned result
1: Calculate the entire PFS matrix;
2: Separate nodes into clusters and calculate \(Q_{0}\);
3:while Number of clusters \(\
eq 1\)do
4:if\(\exists(c_{i},c_{j})=1\)then
5: Group two clusters randomly;
6:endif
7: Calculate the increment of \(\Delta Q\);
8: Select the partitioning with maximum \(\Delta Q\);
9: Update \(Q\) according to the result of partitioning;
10: Conserve the number of communities;
11:endwhile
```  
**Algorithm 1** Modified fast Newman algorithm for finding max \(Q\)
## 3 Research Methodology and Empirical Analysis
### Structural Equation Modeling and Hypothesis Validation

Table 6: Initial hypothetical model: standardized path coefficients between latent variables and their significance levels. Figure 2: Standardized path estimates for the structural model. The structural equation modeling analysis supported seven hypotheses, as illustrated in Figure 2 and detailed in Table 7. Key findings include the significant influence of interface transparency on dispatchers' trust in the ATS system (Hypothesis 1) and the role of dispatchers' knowledge mastery in shaping their understanding of interface transparency (Hypothesis 2b). Additionally, the capability redundancy of the ATS system was found to directly enhance dispatchers' trust (Hypothesis 3). Trust, in turn, primarily influenced reliance behavior during routine monitoring operations (Hypothesis 4a), which further affected reliance behavior during emergency responses (Hypothesis 4c). Finally, the capability redundancy of the ATS system was confirmed to impact reliance behavior in both routine monitoring and emergency scenarios (Hypotheses 5a,b). These results highlight the interconnected roles of interface design, system knowledge, and capability redundancy in fostering trust and shaping reliance behavior in automation systems.
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
## 3 Results and Discussion
### Results from Vibrational Spectral Analyses of Lenses
The Raman spectra of the analyzed lenses are presented in Figure 11c,d; and are analogous to the standard spectra of silica glass (fused quartz) [27] depicted in Figure 11a, and of borosilicate glass [28] depicted in Figure 11b. The band assignments of the Raman spectra can be based on previous works on SiO\({}_{2}\) glass and SiO\({}_{2}\)-rich glasses as borosilicate glasses [29; 30; 31; 32]. The spectra of the silica lenses are dominated by a \(\sim\)430 nm band assigned to symmetrical stretching (SS) vibrational modes of Si-O-Si. The sharp peak observed at 490 cm\({}^{-1}\) and the small at \(\sim\)600 cm\({}^{-1}\) have been attributed to four and three-membered silicate rings, respectively. The 450 cm\({}^{-1}\) Raman band is assigned to the bending vibration mode of the Si-O-Si bond in six-membered rings. The 480 cm\({}^{-1}\) band has been assigned to a vibrationally isolated Si-O-Si, Si-O-B mode of four-membered rings which has small inter-tetrahedral angles. The weak band at around 800 cm\({}^{-1}\) is assigned to the O-Si-O symmetric bond stretching, associated with motions of Si (possibly also B) against its O cage [33]. Figure 10: UV–visible spectra: (**a**) standard spectra for natural silica glass and synthetic silica glass; (**b**) Standard spectra for borosilicate glasses and soda-lime glass; (**c**) obtained UV–visible spectra in silica glass lenses of different focal length; (**d**) obtained UV–visible spectra in borosilicate glass lenses of different focal length.
## 3 Results and Discussion  
### CO\({}_{2}\) Broadening Coefficient  
In order to derive the HCl-CO\({}_{2}\) broadening coefficient, a customized Linde HCl/CO\({}_{2}\) gas mixture (110 \(\pm\) 5 umol/mol HCl in CO\({}_{2}\)) was employed. The gas pressure in the cell was regulated by the gas cell outlet valve and the exhaust pump. The previously evacuated cell was filled with the HCl/CO\({}_{2}\) gas mixture at a steady flow rate of 200 sccm set via a mass flow controller.  
Figure 2a shows a subset of the measured line profiles for the HCl/CO\({}_{2}\) gas mixture at different pressures. The measured laser intensity was averaged over 10 scans and fitted with a third order polynomial for the baseline and the Voigt model using a non-linear Levenberg-Marquart algorithm for the absorption profile. Note that, within the spectral fitting window, we analyzed HCl neighboring lines effects on this P5 line at 700 mbar, the overall contribution of HCl neighboring lines is around 0.01% as P5 line center, and up to 0.2% at the edges of the fitting window with the absorbance of about 5 \(\times\) 10\({}^{-5}\), so that in this paper, only one HCl line was used to fit the measured spectra. CO\({}_{2}\) (pure, 100%) lines absorption (OD, optical density) within the fitting range is less than 5 \(\times\) 10\({}^{-5}\). The residual between measured and fitted data shown below in Figure 2a is depicted for pressure of 943.5 mbar. Typical average residuals were around (1\(\sigma\)) 5 \(\times\) 10\({}^{-4}\) optical density, which corresponds to a good signal-to-noise-ratio (SNR) of 509. To clearly discriminate the pressure-broadening effect, the Gaussian width was calculated in all our experiments using the measured temperature in the gas cell according to Equation (3). The line width progression at different pressures was used to extract the pressure broadening coefficient \(\gamma_{\rm foreign}\). The pressure measurements were associated with an 0.1-0.15 mbar uncertainty (k = 1) according to PTB's national pressure standard. The pressure uncertainty was taken into account for the linear regression in Figure 2b. The standard deviation (1\(\sigma\)) of the line width results (more than 200 measurements) at each pressure point together with the wavenumber scale (laser tuning) uncertainty was used as the y- error bar. The respective Lorentzian widths shows very good linearity with pressures as required by Equation (4) (second term only, as the self-broadening contribution is less than 0.04%) with an intercept of (1.71 \(\pm\) 6.19) \(\times\) 10\({}^{-4}\) cm\({}^{-1}\). Thus, we determined a HCl-CO\({}_{2}\) broadening coefficient of \(\gamma_{\rm CO2}\) = 76.42(83) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm at 295 K.  
Figure 1: Experimental setup of the spectrometer for measuring the pressure broadening coefficients, P-sensor: pressure sensor; PD: photodetector; FG: function generator; DAQ: data acquisition card.
# Integration of Microstrip Slot Array Antenna with Dye-Sensitized Solar Cells
## 3 Results and Discussion
The axial ratio radiation patterns in Figure 8b show that the working solar cells have almost no effect on the antenna axis ratio. The results of normalized radiation patterns of the array antenna integrated with dye-sensitized solar cells are shown in Figure 9. It can be seen that the existence of dye-sensitized solar cells has little influence on the directivity of the microstrip slot array antenna. The above results show that, when the antenna is integrated with dye-sensitized solar cells, the measurement results in the figures will show slight changes, which are mainly caused by the interference between the solar cells and the mutual coupling between the wires. To sum up, the dye-sensitized solar cells and the microstrip slot array antenna are perfectly combined; the interference between the solar cells and the antenna is minimal. When solar cells with higher cost but better photoelectric conversion efficiency are used, the antenna size can be reduced to further improve the performance. Figure 6: The experimental setup and the measurement environment. Figure 8: \(Cont\). Figure 7: (a) Reflection coefficient; (b) gain of the microstrip slot antenna integrated with dye-sensitized solar cells varies with frequency.
## 4 Fuzzy Logic Controller Design  
Figure 8 shows a block diagram of the proposed FLC. The FLC is composed of fuzzification, a membership function, a rule base, a fuzzy inference, and defuzzification, as shown in Figure 9.  
Figure 8: Proposed fuzzy logic controller (FLC).  
Figure 7: Grid-side controller.  
Figure 9: Internal structure of the FLC.  
In order to design the proposed FLC, the error of the rotor d-axis current (\(el_{rd}\)) and rate of change of the \(el_{rd}\) (\(d[el_{rd}]/dt\)) are considered as the controller inputs. The reference rotor d-axis voltage (\(V_{rd}\)*) is chosen as the controller output. In Figure 8, 1/z is one sampling time delay.  
The triangular membership functions with overlap used for the input and output fuzzy sets are shown in Figure 10, where linguistic variables are indicated as NB (Negative Big), NM (Negative Medium), NS (Negative Small), ZO (Zero), PS (Positive Small), PM (Positive Medium), and PB (Positive Big).  
The rules of fuzzy mapping of the input variables to the output are given in the following form:  
IF \(<\)\(el_{nd}\) is PB- and \(<\)\(d(elrd)\)/\(dt\) is NS- THEN \(<\)\(V_{rd}\)* is PS-  
IF \(<\)\(el_{rd}\) is NM\(>\) and \(<\)\(d(elrd)\)/\(dt\) is NS- THEN \(<\)\(V_{rd}\)* is NB-  
The entire rule base is listed in Table 1, which includes a total of 49 rules.  
In the present study, [PERSON]'s max-min method is used as the inference mechanism [27]. The center of gravity method is used for defuzzification in order to obtain \(V_{rd}\)* [28].
## 2 Materials and Methods
### Methodology
#### 2.1.2 Data Collection  
The next phase, after identifying and defining the environmental aspects to be analysed, is the compilation of all the information relating to them and their standardisation for subsequent processing. For this study, the data were collected, for 2019 and 2020, for each of the environmental aspects defined (see Table 1).  
During the pandemic, the consumption of natural resources decreased. This is because standard hospital activity was affected during 2020. All scheduled surgical activities were suspended; consultations, rehabilitation activities and all non-urgent healthcare activities were cancelled.  
In contrast, the consumption of medical gases and materials and the production of waste increased considerably in 2020. During 2020, healthcare mainly focused on patients affected by COVID-19. Medical gases, and more specifically medical oxygen, have been the most significant medicine for severe and critical COVID-19 patients.  
Figure 3: Environmental aspects considered in the study.  
Waste generated during the care of a person infected or with symptoms compatible with COVID-19, such as gloves, masks, gowns, protective clothing and the like, are considered hazardous health care waste. In addition, all the waste generated during the cleaning and disinfection of potentially contaminated surfaces or because of the application of other hygienic measures in any place in contact with COVID-19 is managed as hazardous health care waste, according to the provisions of the regional regulations on health care waste.  
The data obtained show an increase in the production of hazardous healthcare waste, with 92% of the waste generated in this hospital in 2020 coming from materials in contact or possible contact with COVID-19.  
In addition, during 2020, the number of total patient trips to the hospital decreased compared to the previous year. In cases not affected by COVID-19, telemedicine measures were implemented to diagnose and provide care to patients in order to avoid travel and possible contacts.  
With regard to the consumption of materials, it was the use of disinfectants that increased by an unprecedented amount. Environmental cleaning and disinfection procedures, together with the application of commonly used hospital disinfectants, have become essential and indispensable procedures to combat the spread of COVID-19.
## 2 Materials and Methods
### CMC Samples Characterization
#### 2.3.4 Average Molecular Weight  
Chromotographic analyses were performed on the liquid chromatograph Agilent 1260 Infinity II LC System apparatus equipped with the Agilent 1290 Infinity II Evaporative Light Scattering Detector (N2 Flow: 1.2 SLM; Evaporator temperature: 70 \({}^{\circ}\)C and Nebulizer Temperature: 50 \({}^{\circ}\)C). PL aquagel-OH MIXED-M (PN: PL1149-6801--4.6 \(\times\) 250 mm, 8 \(\upmu\)m) and Aquagel-OH 20 (PN: PL1120-6520 300 \(\times\) 7.5 mm, 5 \(\upmu\)m) and a pre-column (PL aquagel-OH PN: PL1149-1240 PL aquagel-OH, 7.5 \(\times\) 50 mm, 15 \(\upmu\)m) were used. The analyses performed in triplicate for each CMC sample were carried out using ammonium acetate (10 mM) as solvent and eluent with a flow rate of 0.6 mL min\({}^{-1}\). CMC solutions at 1% (_w/v_) in ammonium acetate (10 mM) were previously filtered through a 0.2 \(\upmu\)m filter. All chromatograms were analyzed using the OpenLab ChemStation program. The Shodex standards (P-82 kit; Cat. #: WAT034207: P-800 (MW = 736 kDa); P-400 (MW = 348 kDa); P-200 (MW = 200 kDa); P-100 (MW = 113 kDa); P-50 (MW = 49 kDa); P-20 (MW = 23 kDa); P-10 (MW = 9.9 kDa) and P-5 (MW = 6.6 kDa)) used for the calibration curve in order to obtain the molar mass distribution were obtained from Waters Chromatography (Madrid, Spain). The transformation of the chromatographic data into a molar mass distribution implies the determination of the following parameters: (i) weight-average molar mass (\(M_{W}\)); number-average molar mass (\(Mn\)); and polydispersity index (PI) [20; 21; 22]. The average molecular weights and I based on the peak area of each fraction (\(Ai\)) selected in each chromatogram are defined according to Equations (4)-(6):  
\[M_{w}=\left(\Sigma A_{i}M_{i}^{2}\right)/(\Sigma A_{i}M_{i}) \tag{4}\]  
\[M_{n}=\left(\Sigma A_{i}M_{i}\right)/(\Sigma A_{i}) \tag{5}\]  
\[I=M_{w}/M_{n} \tag{6}\]
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 5 Performance Evaluation  
In this section, we evaluate the performance of our proposed algorithms through experimental simulations. The experiments are implemented with the MATLAB platform and run on a personal computer with Intel Core CPU i7-9750H @ 2.60 GHz, 24 GB RAM.  
We use the real-world EUA ([[https://github.com/swinedge/edu-dataset](https://github.com/swinedge/edu-dataset)]([https://github.com/swinedge/edu-dataset](https://github.com/swinedge/edu-dataset)), accessed on 16 June 2023) dataset to simulate the MEC network environment. In our experiments, the locations of base stations and users are from the EUA dataset. There is one edge server deployed alongside each base station. The resource capacity of each edge server is randomly between 50 and 150 units. The data size, delay, reliability requirements, and maximum number of sub-flows of each user request \(r_{i}\) are randomly between 100 and 300 KB, 100 and 300 ms, 0.7 and 0.99 and 1 and 4, respectively. The number of VNF types is 20. The resource demand, processing capacity, and reliability of each VNF are randomly between 1 and 10 units, \(10^{4}\) and \(10^{5}\) KB/s and 0.6 and 0.9, respectively. We conducted experiments with the above settings. To further understand the effectiveness of our proposed algorithm, we compared it with two benchmark algorithms. We also define our proposed algorithm for the joint deployment of P-SFCs and BVNFs with dynamic reliability as DRPD:  
1. The Joint Deployment algorithm of P-SFCs and BVNFs with Static Reliability (SRPD): this algorithm deploys parallel VNF instances only to decrease delay. Then, it deploys BVNF instances to satisfy the reliability requirement. The reliability of parallel VNF instances is the product of the reliability of all VNF instances;
2. The Joint Deployment algorithm of P-SFCs and BVNFs for Decreasing BVNFs (DBPD): this algorithm deploys parallel VNF instances only to decrease delay and enhance reliability for decreasing BVNFs. When deploying parallel instances to decrease delay, it calculates delay-reduction priority and finds the target edge server by comparing the communication distances from the previous edge server traversed by the target sub-flow to all feasible edge servers.  
We also investigate the impact of important parameters on the performance of our proposed algorithm. The important parameter settings in our experiments are shown in Table 2.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Human-Machine Interfaces  
Generally, human-machine interfaces are an important factor supporting proper decision making in automated systems, introduced by the industry 4.0 concept to improve work conditions and enhance productivity. Hence, human-machine interfaces should provide collected information to the operators in the most user-friendly way possible [59]. In SCCs,human-machine interfaces mostly refer to ergonomics. The organization of SCCs significantly imperils good ergonomics. The size and number of screens and design of automated and remote systems directly affect operators' performance. [PERSON] et al. [35] proposed four subcategories of ergonomically acceptable collaborative work systems that exist in SCCs: installed systems, input devices, furniture, and visual displays. Indeed, during the remote operation of UAVs, the operator interface is defined as a preliminary facilitator of human-machine interactions and coordination, in addition to human-human communication and coordination. Smooth UAV operation requires an interface which provides an adequate level of awareness, aimed at reducing information overload, miscommunication, distractions, and coordination breakdown [39].  
A study comparing various human-machine interfaces to onboard ship-handling equipment found that participants considered SCC simulators to be ideal human-machine interfaces because they use a joystick handle rather than a mouse. Participants suggest that the use of simulators as a human machine interface in SCCs would provide a 3D visualized environment in order to improve ship sense. However, researchers found that such a visualization would not provide adequate SA [22]. [PERSON] et al. [23] discuss various solutions which could be applied in SCCs, such as a \"_large shared display_\" and \"_shared screen_\" that could be utilized in order to maintain shared awareness.  
The 58 th Sub-Committee on Safety of Navigation (NAV) session [27] prioritized various solutions to improve e-Navigation processes, such as improving and harmonizing bridge design to make it more user-friendly and the presentation and integration of available information in graphical displays which were received from communication facilities. [PERSON] et al. [10] and international maritime organization (IMO) [28] highlight the same human-machine interfaces issues. These solutions are also critical in the design of SCCs because when humans start to interact with machines, an inefficient human-machine interface may lead to various human factor challenges such as a higher level of fatigue, greater communication challenges, and distraction from navigational and monitoring tasks.