esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### DIALux(r) Analysis Results
Table 2 includes the average and maximum illumination on the desks and blackboards in the selected classrooms for the cases studied, along with the maximum UGR glare values obtained in each case:  
* Current Case (represented by the experimental measures)
* Current Case Simulation
* Case 1 (change of paint on the walls)
* Case 2 (replacement of the lamps using the same models)
* and Case 3 (replacement of the lamps with LED models)  
In addition and due to their special importance, Figure 9 shows the percentage distributions of the desks in the selected classrooms, classified according to their illuminance in cases 1, 2, and 3 and with regard to the initial case (current case simulation). The results of this study clearly indicate the need for a change in the lighting of the ETSIAE classrooms, as a real and true approach to the minimum conditions (500 lx) is not achieved unless the luminaires and lamps are replaced. More specifically, it can be said that changing the color of the walls and replacing the existing luminaires would both have positive effects but are not sufficient to provide adequate lighting for the classrooms. Therefore, for the purposes of the case study, changing the lighting technology is considered the best option in order to meet the standard [43] in the building A and B classrooms. As it can be observed in Table 2, for the option chosen as the appropriate improvement action (case 3), the UGR or glare values are below the limits established by the UNE 12464-1, which means no further action is needed to comply with the standards in terms of glare. However, if other options were to be implemented (as in case 2 for the B-306 classroom), technologies for glare reduction should be considered and tested via simulations in order to reduce the maximum UGR value to comply with the standards (maximum UGR value of 19). Such technologies include translucent or polarizing covers or reflectors to prevent light spreading horizontally. In addition, focusing on case 2 for the B-306 classroom, this particular case could be evaluated with some of the luminaires turned off, as there is a wide margin for illuminance reduction that could lead to meeting the glare standards. Figure 9: Distribution of desks by lighting levels and improvement cases (case 1, case 2, and case 3) from the initial illuminance levels in each of the selected classrooms.
# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 3 Results and Discussions
### Sensitivity Analysis  
Since the proposed ANN model in this study has only one hidden layer, the Garson factor [34] can be used to evaluate the relative importance of various input parameters to the output. The importance factor of each input parameter is computed using the following equation:  
\[U_{io}=\frac{\sum_{h=1}^{L}\left(\frac{w_{ab}}{\sum_{t=1}^{L}w_{n}}v_{ho} \right)}{\sum_{l=1}^{E}\left(\sum_{h=1}^{L}\left(\frac{w_{h}}{\sum_{t=1}^{L} w_{n}}v_{ho}\right)\right)} \tag{7}\]  
Figure 14: Activation functions of ANN model: (**a**) MAE (**b**) and RMSE.  
where \(\sum\limits_{r=1}^{E}w_{rh}\) is the weights summation within the hidden neuron \(h\) and the input neuron \(E\); and \(v_{ho}\) is the connection weight for the hidden neuron \(h\) and the output neuron \(o\).  
Figure 16 shows the relative importance of the different input parameters through sensitivity analysis. In general, it can be seen that these input parameters all have a significant effect on the cantilever wall deflection \(\delta\). In particular, the excavation depth  
Figure 15: Influence of input parameters on the cantilever wall deflection. (**a**) influence of the excavation width \(B\); **(b**) influence of the excavation depth \(H_{c}\); (c) influence of the wall thickness \(t\); (d) influence of the at-rest lateral earth pressure coefficient \(K_{0}\); (e) influence of the soil shear strength ratio at mid-depth of the wall \(s_{u}/\sigma_{v}{{}^{\prime}}\); (f) and influence of the soil stiffness ratio at mid-depth of the wall \(E_{ur}/s_{u}\).  
is the most important parameter, followed by the soil stiffness ratio at mid-depth of the wall \(E_{\text{tur}}/\text{s}_{\text{u}}\), while the at-rest lateral earth pressure coefficient \(K_{0}\) has the least importance. Obviously, the parameter of \(H_{c}\) is demonstrated to be the most sensitive parameter, which means that it plays a key role in predicting the cantilever wall deflection.
###### Abstract  
This study focuses on a 4 FSK-modulated ultraviolet (UV) communication system, introducing an innovative symbol-level maximum likelihood decoding approach based on Poisson statistics. A forward error correction (FEC) coding mechanism is integrated to enhance system robustness. Through Monte Carlo simulations, the proposed decoding scheme and the error correction performances of Reed-Solomon (RS) and Low-Density Parity-Check (LDPC) codes are evaluated in UV channels. Both RS and LDPC codes significantly improve the Bit Error Rate (BER), with LDPC codes achieving superior gains under low SNR conditions. Hardware implementation and field tests validate the decoding algorithm and LDPC-optimized 4 FSK system. Under non-line-of-sight (NLOS) conditions (10-45\({}^{\circ}\) transmit elevation angle), stable 60 m communication with BER < 10\({}^{-3}\) is achieved. In line-of-sight (LOS) scenarios, the system demonstrates 900 m range with BER < 10\({}^{-3}\), highlighting practical applicability in challenging atmospheric environments.  
UV communication; 4 FSK; LDPC; Poisson maximum likelihood decoding
## 7 Results and Discussions  
The complexity of the encoder and decoder of the proposed EDFC is analyzed in this section. For the simplicity of analysis, we assume that each source has the same block length \(k\). The complexity of the encoder mainly depends on the average degree of the distribution. According to [41; 42], the encoding and BP decoding complexity of point-to-point LT codes are \(O(\ln(k))\) and \(O\big{(}\sqrt{k}\ln^{2}(k)\big{)}\), respectively. For EDFC with \(n\) sources, the encoding complexity of sources, relay \(R_{1}\), and relay \(R_{2}\) are \(O(n\ln(k))\), \(O(\ln(n))\), and \(O(\ln(t))\), respectively. The complexity of the BP decoder at the destination can be represented by \(O\big{(}\sqrt{nk}\ln^{2}(nk)\big{)}\).  
In Table 2, we briefly compare the advantages and disadvantages of the proposed EDFC with three existing methods: Decode-and-forward (DF), DLT, and GDFC. In contrast to the existing methods, EDFC innovatively introduces an extra cooperative relay in a distributed network to solve the problem of connection interruptions. Simulation results show that EDFC has better BER performance on both lossless and lossy channels. It is able to provide an enhanced UEP property and improve the reliability of the networks. The disadvantages of EDFC are the hardware costs due to the additional relay and the increased complexity due to optimization.  
The novel multiple-access network with a cooperative relay proposed in this paper is suitable to be used in wireless sensor networks. When some of the source-relay channels are in poor conditions and the sensor data cannot be collected, a drone can be used as a mobile cooperative relay and flies to a specified location to collect sensor data. Since both sensors and drones are energy-restricted devices that cannot perform high-complexity computational tasks for long periods of time, it is necessary to study how to reduce the complexity of EDFC in the future.  
Figure 8: BER performance versus relay-destination erasure rate \(p_{rd}\) for EDFC with two sources.
## Appendix A Robustness Test Considering the Mismatch of \(R_{s}\) or J  
Figures 11-13 show the test results when the parameters \(R_{s}\) are not matched at rated speed. Figures 14-16 show the test results when the coefficient \(J\) is not matched.  
Figure A1: The speed response of VPDPI regulator when \(R_{s}\) = 0.001 \(R_{s}\) = 0.011. (**a**) Observed speed and actual speed. (**b**) Estimation error curve  
Figure A2: The speed response of VPDPI regulator when \(R_{s}\) = 0.021 and \(J\) = 0.0008. (**a**) Observed speed and actual speed. (**b**) Estimation error curve  
According to the test results of the appeal, it can be seen that when the parameters fluctuate, the adjustment performance of the proposed VPDPI regulator changes little, which ensures that the system without position sensor can still run stably when the parameters change, and further verifies the robustness of the proposed regulator.
## 2 Related Work
### Homomorphic Encryption
In healthcare, it facilitates the analysis of encrypted medical data, such as tumor detection and drug recommendation, safeguarding patient confidentiality while improving diagnostic accuracy. In autonomous driving, homomorphic encryption supports vehicle-to-cloud collaborative computations, including real-time path optimization and environmental awareness, ensuring data privacy for sensitive information like driving behaviors and location data. Despite its theoretical and practical advantages, the high computational complexity of homomorphic encryption and its limited application scenarios remain significant challenges. In particular, the bootstrapping process and the accumulation of noise in FHE impose substantial computational overhead, negatively impacting system performance. Research efforts are increasingly focused on optimizing computational efficiency while maintaining encryption strength. Techniques such as integrating attention mechanisms, multimodal data processing, and parallel computing are promising directions for enhancing the practicality and computational efficiency of homomorphic encryption in complex scenarios. Overall, homomorphic encryption stands as a pivotal technology for privacy-preserving computation, with its potential and applicability expanding through advancements in algorithms and hardware support, providing robust security solutions for sensitive data processing.
## 4 Conclusions  
The key findings and conclusions of this study can be summarized as follows:  
1. The lowest cutting tool flank wear is observed during hot turning at a temperature of 300 \({}^{\circ}\)C. Applying this preheating temperature reduces tool flank wear by up to 33%.
2. In hot turning at 300 \({}^{\circ}\)C, the temperature at the tool/chip interface is higher than in conventional turning. As a result, the hot hardness of the workpiece carbides  
Figure 12: Predicted cutting force by FEM in hot turning and conventional turning for V\({}_{\text{c}}\) = 96 m/min and f = 0.175 mm/rev.  
Figure 13: (**a**) cutting force estimated by using FEM in conventional and hot turning, (**b**) fluctuation amplitude of the predicted cutting force at a feed of 0.102 mm/rev.  
decreases further compared to the hardness of the tool components. This, combined with a substantial reduction in cutting force during hot turning, typically leads to less tool abrasive wear compared to conventional turning operations.
3. With an increase in the workpiece's initial temperature from 300 \({}^{\circ}\)C to 400 \({}^{\circ}\)C, there is a slight rise in the ratio of the hardness of the carbides in the workpiece to the hardness of the tool components. Consequently, the tool flank wear in turning with a preheating temperature of 400 \({}^{\circ}\)C is marginally higher than that in turning with the preheating temperature of 300 \({}^{\circ}\)C.
4. SEM micrographs of the tool rake face show the BUL layer formation during conventional turning operation, while this layer is not observed in hot turning and the worn area of the tool coating is less.
5. The predominant mechanisms of tool wear in conventional and hot turning of AISI630 stainless steel are abrasive and adhesion wears phenomena.
6. The application of hot turning at 300 \({}^{\circ}\)C can lead to a reduction in machined surface roughness of up to 23% when compared to conventional turning. Furthermore, surface defects like side flow and tool marks on the workpiece are significantly reduced during hot turning compared to conventional turning.  
Conceptualization S.M.E. and M.H.; Methodology: S.M.E. and M.H.; Software: S.M.E.; Visualization: S.M.E. and M.H.; Validation: S.M.E. and M.H.; Formal analysis: S.M.E.; Investigation: S.M.E.; Resources: S.M.E.; Data curation: S.M.E., Writing-original draft preparation: S.M.E. and M.H.; Writing-review and editing: S.M.E. and M.H.; Supervision: M.H.; Project administration: M.H.; Funding acquisition: M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
Open Access funding provided by the Qatar National Library.  
The authors declare no conflict of interest.
## 6 Discussion
### Nature and Evolution of Ore-Forming Fluids  
Fluid inclusions provide direct evidence for identifying the composition, pressure, temperature, and redox conditions of the mineralization environment [75; 76]. Although the post-mineralization deformation and metamorphism can obliterate most of the evidence of the original ore-forming fluids [77], the primary fluid inclusions in the quartz from all types of massive ore in the studied deposit are well-preserved.  
The fluid inclusions in the quartz from the three types of massive ores exhibit distinct homogenization temperatures and salinity characteristics, which may represent different stages of ore-forming fluid evolution and correspond to the formation of the different types of massive ores. Type-1 fluids, characterized by higher homogenization temperatures (avg. 222.9 \({}^{\circ}\)C) and lower salinities (avg. 6.74 wt.% NaCl eqv.), likely represent the early stage high-temperature fluids responsible for the formation of Type-1 massive ores. The Type-2 and Type-3 fluid inclusions have relatively low homogenization temperatures (avg. 185.7 \({}^{\circ}\)C and 184.81 \({}^{\circ}\)C, respectively) and similarly high salinities (avg. 16.56 and 16.22 wt.% NaCl eqv., respectively), indicating that they share most characteristics. However, the Type-2 fluid inclusions are distinguished by the presence of a small number of halite-bearing high-salinity fluid inclusions (salinity range from 30.27 to 31.09 wt.% NaCl eqv.), which are absent in the Type-3 fluid inclusions. We infer that the high salinity of the Type-2 fluids, particularly the presence of halite-bearing inclusions, is more likely caused by the mixing of magmatic fluids, which is consistent with the magmatic-hydrothermal origin of the Type-1 and Type-2 massive ores, as indicated by the sulfur isotope and trace element characteristics. However, the relatively low homogenization temperatures of Type-2 fluid inclusions suggest that they were possibly trapped during a later stage of the hydrothermal system, after the fluids had undergone cooling from the peak metamorphic conditions (600-800 \({}^{\circ}\)C) associated with the formation of the Type-2 massive ores. In contrast, the absence of halite-bearing inclusions in the Type-3 fluid inclusions, combined with their sulfur isotope and trace element characteristics, points to a greater contribution from seawater in the formation of the Type-3 massive ores. The generally low Th/La and Nb/La ratios (<1) in pyrite and pyrrhotite across all of the ore types indicate that the hydrothermal fluids were enriched in Cl, facilitating the complexation and transportation of REE and HFSE. These observations suggest a trend of cooling and dilution from Type-1 to Type-2 and Type-3 fluids, with an increasing involvement of seawater in the hydrothermal system.
## 7 Equation Calculation Results and Analysis
### Results of the Sensitivity Analysis  
Changing the air void content of the mixture could affect the mortar voids, and a commonly used asphalt aggregate ratio and air void content range were studied. The mortar void sensitivity analysis of the AC-13 mortar is shown in Figure 9.  
AC-13 had a designed air void content of 4% and a designed asphalt aggregate ratio of 5%. Several changes were applied to the air voids and asphalt aggregate ratio. From Figure 9, it is revealed that the mortar with different NMPS had different sensitivities. For the mortar with a 0.6 mm NMPS, a 1% parameter value change in the air content or asphalt aggregate ratio in the corresponding asphalt mixture could lead to an approximately 0.1% change in the 0.6 mm mortar's air void content and a 1% change in the 4.75 mm mortar's air void content. The air void content of the mortar with a larger NMPS was more sensitive than that of a smaller NMPS. With same mortar NMPS, the air voids of the mixture were more sensitive to the mortar air voids than the asphalt aggregate ratio of the mixture. Because this result was calculated by the given design, the mortar air void content versus the different AC-13 air void contents had a significant linear correlation, and each coefficient of determination, R\({}^{2}\), was almost 1. These results were calculated by their scanned mixture samples; therefore, they had good correlation.  
When changing the mixture air void content and asphalt aggregate, the mortar air void content of AC-20 and AC-25 are shown in Figures 8-10. The mortar air void content versus the different AC-20 and AC-25 air void contents had a significant linear correlation, and each coefficient of determination, R\({}^{2}\), was close to 1, similar to AC-13. The AC-13 slope absolute value of the mortar air void content versus the mixture air void content was bigger than that of AC-20, and AC-20's was bigger than AC-25's. This indicates that with the same mixture air void content as in AC-13, it would have a larger mortar air void content whether the mortar NMPS and the mixture with a lower NMPS had a higher mortar air void content sensitivity. The AC-13 slope absolute value of the mortar air void content versus the asphalt aggregate ratio was bigger than that of AC-20 and lower than that of AC-25, indicating that this slope absolute value did not have a clear rule with NMPS. The Figure 10 showed AC-20 mortar void content with different mixture's air void content and with different asphalt aggregate ratios. The Figure 11 showed AC-25 mortar void content with different mixture's air void content and with different asphalt aggregate ratios.  
The sensitivity analysis of the mortar air void content was more sensitive with the air void content change and less sensitive with the common asphalt aggregate ratio change.  
Figure 9: AC-13 mortar voids: (**a**) with different mixtures’ air void content; (**b**) with different asphalt aggregate ratios.
## 2 Literature Review
### Hypotheses Development  
Five hypotheses that reflect the macro-level factors influencing cloud computing adoption in EU member states are put out in this study based on the examined literature. First, a number of studies highlight how education and human capital support digital transformation. The ability to adopt and use cutting-edge technology, such as cloud computing, is more likely in nations where a larger proportion of the population has completed postsecondary education [11; 21]. Thus,  
**H1:**: _Cloud adoption rates are higher in nations with higher university education levels._  
Second, there is a constant correlation between increased digital investment and macroeconomic strength. Cloud adoption is facilitated by a larger GDP per capita, which usually indicates both more budgetary space and a bigger demand for digital services [10; 24]. Thus,  
**H2:**: _Higher GDP per capita is positively related to cloud adoption._  
Third, digital transformation relies heavily on the availability of skilled personnel. The presence of ICT specialists within the workforce improves an economy's technical capacity to adopt, maintain, and innovate in cloud-based environments [8; 23]. Therefore,  
**H3:**: _A higher share of ICT specialists leads to cloud computing adoption._Fourth, efficient use of cloud requires a strong digital infrastructure, especially broadband access. According to empirical data, nations with higher rates of broadband adoption are better equipped to implement and grow cloud services [7,28]. Thus,  
**H4:**: _Cloud adoption is facilitated by increased broadband penetration_.  
Lastly, cloud preparedness may be indirectly impacted by more general macroeconomic factors like unemployment. High unemployment rates have the potential to discourage investment and impede efforts at digital transformation, especially in the public and small business sectors where resources are limited [25]. In this case,  
**H5:**: _Cloud adoption is moderated by unemployment rates, which may impede digital investments_.
# Noiseless Attenuation for Continuous-Variable Quantum Key Distribution over Ground-Satellite Uplink
## 5 Conclusions  
We have proposed an NA-based scheme for improving the uplink CV-QKD protocol. Specifically, the NA process is realized by ZPC operation, which happens to be a zero-energy-input attenuation that does not cause the consumption of external energy. We find that this scheme shows better performance when the zenith angle is above 26\({}^{\circ}\) and 3\({}^{\circ}\) for 100 km and 120 km, respectively. Numerical simulations show that the ZPC operation improves the performance by relatively reducing the eavesdropper's information. In addition, the ZPC-involved scheme performs better than the scheme with SPC operation, which is considered as another NA process. The research confirms the role of the NA operation on the uplink CV-QKD protocol and is conducive to making the CV-QKD protocol more practical. The limit of the proposed approach is that the effective time within  
Figure 8: Secret key rate vs. zenith angle with finite-size effect for the satellite altitudes: (**a**) \(H\) = 100 km; (**b**) \(H\) = 120 km.  
a single communication cycle is still below 30 s. The next step of our work is to reach a longer effective time.  
Conceptualization, Y.W. and Y.G.; methodology, Z.Z.; validation, S.X., Y.M. and Z.Z.; formal analysis, Y.M.; investigation, Y.L.; writing--original draft preparation, S.X.; writing--review and editing, Y.L.; visualization, X.R.; supervision, Y.G. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (Grant No. 61871407), and Natural Science Foundation of Hunan Province, China (Grant No. 2021 JJ30878).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} QKD & Quantum key distribution \\ DV & Discrete-variable \\ CV & Continuous-variable \\ NA & Noiseless attenuation \\ ZPC & Zero-photon catalysis \\ EB & Entanglement-based \\ TMSV & Two-mode squeezed vacuum \\ BS & Beam splitter \\ PDT & Probability distribution of transmittance \\ ABS & Asymmetrical beam splitter \\ PNRD & Photon number resolving detector \\ CF & Characteristic function \\ \end{tabular}