esa-sceva/satcom-chunk-collection huggingface dataset:
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 4 Case Study
### Optimization Result
#### 4.2.3 Analysis of the Maximum Thrust-to-Weight Ratio  
Previous research has usually used the hover figure of merit (FM)/power and cruising efficiency/power as the objectives for VTOL propeller optimization. However, the hover FM or power cannot characterize the maneuverability during takeoff. The relationship between the hover power and the maximum thrust-to-weight ratio at hover are shown in Figure 25. Pareto front 1 contained the optimal design using \(E_{mission}\) and \(\kappa\) as the two objectives. Pareto front 2 contained the optimal design using \(E_{mission}\) and the propeller hover power as the two objectives. It was clear that, with certain maximum power and torque constraints, the propeller with the lowest hover power was not always the one with the maximum thrust-to-weight ratio, especially for the VPP. This result validated the necessity of taking the maximum thrust-to-weight ratio at hover as the objective  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **VPP** & **FPP** & **Advantage of VPP** \\ \hline \(E_{mission}\) (kWh) & 4.399 & 5.303 & \(-\)17.05\% \\ \hline \(\kappa\) & 1.577 & 1.505 & 4.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between VPP and FPP.  
Figure 23: Performance of the selected optimal FPP. (**a**) \(C_{T}\); (**b**) \(\eta\); (**c**) \(C_{P}\); (**d**) \(\eta_{hover}\).  
Figure 24: Average relative blade width and average twist amplitude of different optimal FPPs.  
instead of the hover power to characterize the maneuverability during vertical takeoff and landing. From another perspective, the maximum thrust-to-weight ratio at hover was a more practical factor than the hover power for the design of a VTOL propeller.
## 5 Experimental Results
### Dataset  
The HAICon2021 [31; 32] dataset was used in the present study. The ICS dataset was analyzed to identify essential characteristics, such as sensor data, control data, and configuration files. The HAICon2021 dataset is designed to detect anomalies in industrial control systems. It includes data collected from various sensors and actuators and covers the average operating data and data with injected anomalies. The dataset contains time series data collected from multiple sensors, which can be used to establish machine learning models to detect abnormal system behaviors.
## 6 Conclusions  
The investigations of tufa accumulations at SRB and GRB led to the following conclusions:  
(1) Four tufa facies are recognized, ordered by their abundance: moss tufa, phytoplankton tufa, algal tufa, and stromatolitic laminated tufa. Different facies display different environmental conditions.  
(2) Stable isotope (\(\delta^{13}\)C and \(\delta^{18}\)O) data from the tufa of SRB and GRB are similar in all tufa facies. Oxygen isotope composition suggests that the tufa precipitated from cold, freshwaters enriched in calcium bicarbonate. Carbon isotope composition suggests that CO\({}_{2}\) is dominantly of atmospheric origin, with various amount of soil (organic) CO\({}_{2}\).  
(3) Differences in the \(\delta^{13}\)C values between the tufa samples from Serbia and from the other sites in Europe are negligible. Lower \(\delta^{18}\)O values in the tufa from SRB and GRB than in other sites in Europe indicate that these tufa deposits in Serbia precipitated in a slightly different condition. Lower \(\delta^{13}\)C values in tufa from SRB and GRB than in other sites in Europe indicate that in tufa from Serbia, while the influence of isotopically heavier \({}^{13}\)C from dissolved limestones was less.  
(4) Well-developed sylvite crystals in two samples from GRB suggest a strong anthropogenic influence.  
Conceptualization, N.B.; Methodology, N.B. and V.G.; Software, U.D. and A.V.; Investigation, V.G.; Data curation, I.C. and V.G.; Writing--review & editing, N.B. and I.C.; Visualization, N.B., W.W., I.C. and U.D.; Supervision, W.W. All authors have read and agreed to the published version of the manuscript.  
The study was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract numbers 451-03-68/2022-14/200126 and 451-03-68/2022-14/200091).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 7: Comparison of stable isotope data in this study with other deposits in Dinaric Karst. \(1-\)Gostijie River Basin; \(2-\)Sopotnica River Basin; \(3-\)Zrmanja River Basin; \(4-\)Kra River Basin; \(5-\)Krupa River Basin; \(6-\)Plitrice Lakes.
## 4 Discussion
Adapting the system to new regions requires careful consideration of these environmental factors and may necessitate the retraining or fine-tuning of the models to account for local conditions. Regulatory frameworks and socioeconomic conditions vary across regions, influencing pollution sources, reporting mechanisms, and community engagement practices. Adapting the system to new regions requires collaboration with local stakeholders, including government agencies, environmental organizations, and community groups, to ensure alignment with regulatory requirements and to address community needs and concerns. Cultural attitudes towards environmental monitoring and data sharing may differ across regions, impacting the acceptance and adoption of the pollution detection system. Ethical considerations, such as privacy rights and data ownership, must be addressed when deploying monitoring systems in diverse socio-cultural contexts. By discussing these potential challenges, stakeholders can gain a more rounded view of the system's applicability and develop strategies to overcome barriers to scaling and adaptation in diverse geographic and socioeconomic settings.
## 5 Results
### Fluid Inclusions
#### 5.3.1 Microthermometric Results  
Type-1 ore fluid inclusions have homogenization temperatures (T\({}_{h}\)) ranging from 142.7 \({}^{\circ}\)C to 291 \({}^{\circ}\)C, with salinities varying from 0.18 to 12.39 wt.% NaCl eqv. The majority of these inclusions have T\({}_{h}\) values between 200 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, and salinities lower than 10 wt.% NaCl eqv. (Figure 6B,C).  
Type-2 ore fluid inclusions exhibit T\({}_{h}\) values from 172.8 \({}^{\circ}\)C to 199.0 \({}^{\circ}\)C, and higher salinities ranging from 10.86 to 22.1 wt.% NaCl eqv. The majority of these inclusions (63%) have T\({}_{h}\) values between 150 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, and salinities between 10 and 20 wt.% NaCl eqv. (Figure 6E,F). Two halite-bearing fluid inclusions have halite dissolution temperatures (T\({}_{NaCl}\)) of 165 \({}^{\circ}\)C and 183.6 \({}^{\circ}\)C with salinities of 30.27 and 31.09 wt.% NaCl eqv.  
Type-3 ore fluid inclusions show a wide range of Th values from 127.4 \({}^{\circ}\)C to 254.9 \({}^{\circ}\)C, with salinities varying from 6.16 to 22.85 wt.% NaCl eqv. The majority of these inclusions  
Figure 6: (**A,D,G**) Histograms showing pressure; (**B,E,H**) histograms of homogenisation temperatures (T\({}_{h}\)); (**C,F,I**) histograms of salinity for fluid inclusion in the three types of massive ores from the Hongtoushan deposit.  
have T\({}_{h}\) values between 150 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, and salinities between 10 and 20 wt.% NaCl eqv. (Figure 6H,I).  
The densities of the fluid inclusions from all the types of ores range from 0.72 to 1.08 g/cm\({}^{3}\), with corresponding trapping pressures from 11.67 to 33.53 MPa (Figure 6A,D,G).
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### NSSIM Index  
Similar to the definitions of luminance, contrast and structure in SSIM, we define _blurriness comparison function_ as  
\[h(x,y)=\frac{2d_{x}d_{y}+C_{4}}{d_{x}^{2}+d_{y}^{2}+C_{4}} \tag{9}\]  
where \(d_{x}\) and \(d_{y}\) respectively represent blurriness of distorted image \(x\) and its re-blurred image \(y\). \(C_{4}\) is to avoid instability when \(d_{x}^{2}+d_{y}^{2}\) closes to zero. Thus, we can calculate luminance, contrast, structure and blurriness to get a new metrics SSIM\({}_{r}\) as  
\[SSIM_{r}(x,y)=\left[l(x,y)\right]^{\alpha}\left[c(x,y)\right]^{\beta}\left[s( x,y)\right]^{\gamma}\left[h(x,y)\right]^{\lambda} \tag{10}\]  
where \(\lambda\) is the exponent coefficient of \(h(x,y)\). To better capture the blurriness in local areas of an image, we partition an image into \(P\times P\) patches of the same size and compute the mean SSIM\({}_{r}\) as  
\[MSSIM_{r}(x,y)=\frac{1}{M}\sum_{i=1}^{M}SSIM_{r}\left(x^{i},y^{i}\right) \tag{11}\]  
where \(M=P\times P\), and \(x^{i}\) and \(y^{i}\) are the \(i\)-th patches in \(x\) and \(y\) respectively. Finally, to make the quality score in accordance with the subjective impression, i.e., high-quality image gets high IQA score, we define our proposed _NSSIM index_ as  
\[NSSIM(x,y)=1-MSSIM_{r}(x,y) \tag{12}\]be noted that DMOS scores provided by CSIQ [25] and LIVE II [26] have positive correlation with blurriness scores, while DMOS scores provided by IVC [27] and TID2013 [28] change in the opposite direction. As shown in Figure 6, the datasets used for performance evaluation contain blur images with different kinds of nature scenes which are generally distributed, thus are suitable for statistical analysis of evaluation results.
[PERSON] et al., in their paper, discussed the recycling possibilities of PVB waste from vehicle windshields and building glass for reuse [7]. A recent study by [PERSON] et al. contributed to the filament production area of research by offering a detailed description of the extrusion process and how selected parameters can affect optimal resulting filament quality [8]. [PERSON] et al. have employed the Taguchi method to optimize the filament production process, intending to reduce irregularities and improve the filament quality; applying the Taguchi method can help ensure better performance in diameter homogeneity [9]. [PERSON] et al. have investigated several mechanical properties of fabricated filament from acrylonitrile butadiene (ABS) and polyvinyl chloride (PVC). The obtained results from the measurements (glass transition temperature, tensile properties, and pyrolysis scenario) were evaluated. The results show differences in virgin and recycled materials' melt flow indices (MFI). The MFI of recycled ABS is significantly lower than that of virgin ABS material [10]. [PERSON] et al., in their study, have provided a methodology for the recycling process of laminated glass, which consists of PVB interlayer film. The recycling methodology consists of several steps, such as mechanical separation, vibration, and chemical cleaning of the PVB film. Additionally, the study proposed the equipment design needed for the separation method [11]. Although the related studies peripherally address this specific area of research, no investigation deals with filament production using the extrusion method from recycled PVB.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 3 Ground Testing
### Wing Mass and Actuation Energy Estimates  
Table 4 shows the wing's mass breakdown. One can notice that compared to the assumed MTOM the wing mass is about 44% and thus excessively high. The actuation and support components comprised 9.4% of the MTOM while the wing skin and internals comprised 34.6%.  
One could argue that either the wing may allow a high operational load factor or that the MTOM could be increased in order to justify this wing system mass. Although the load  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Mass (g)** & **\% of MTOW** \\ \hline Wing Skin & 1094 & 27.4 \\ Internals & 286 & 7.2 \\ Rotational servos & 186 & 4.7 \\ Linear servos & 116 & 2.9 \\ LE support & 38 & 1.0 \\ TE support & 20 & 0.5 \\ \hline Total & 1740 & 43.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Morphing wing mass breakdown.  
Figure 12: Estimated L/D variation with speed for the main configurations.  
tests were performed as shown in Figure 13, the maximum load applied to the wing with an approximately elliptical distribution was equivalent to a load factor of 2.2 g without signs of structural failure but with visible airfoil shape deformation in the second quarter of the wing.  
The wing was also actuated with and without this load and the power required for the same actuation requests was recorded in both cases, as shown in Table 5.  
As expected, actuation power increases with the configuration camber. The same trend was seen with maneuvering power. As for the effect of load on actuation power, it shows an increase that varied with the configuration up 129%.  
The power consumption naturally implied a mass penalty as the actuation energy must be provided by some energy source. As is frequent with morphing solutions, the penalties might outweigh the benefits if the mission does not favor the potential of the concept. For example, maintaining cambered configurations for long periods of time may jeopardize potential structural mass reduction benefits from the load alleviation capability.  
Both deprejective factors (excess mass and actuation energy consumption) could be mitigated with a more careful design. It seems reasonable to expect wing skin mass reductions of about 33% with replacement of the paper and resin material between fiber plies with foam as previously mentioned. Internal parts supporting servos and the \"comb-like\" parts could also be improved. As for the actuation energy, extending the use of linear actuators as much as possible would reduce the energy requirements to maintain the configuration shape, making the energetic expenditures dependent only on the amount of configuration transitions and maneuvers required in a flight mission.
# BiPAD: Binomial Point Process Based Energy-Aware Data Dissemination in Opportunistic D2D Networks
## 2 BiPAD: Binomial Point Process-Based Energy-Aware Data Dissemination  
### \(n\)-th Nearest Distance by BPP  
By analyzing the BPP, we can calculate the distance from an arbitrary source device to the \(k\)-th furthest point. If \(R_{n}\) is the distance between the arbitrary source device and the \(n\)-th nearest device, \(f_{R_{n}}(r)\) is the probability density function (pdf) of \(R_{n}\), and \(F_{R_{n}}(r)\) is the cumulative distribution function (cdf) of \(R_{n}\). Then, \(F_{R_{n}}(r)\) can be interpreted as the probability that the number of points within \(R_{n}\) is less than \(n\)[23], which can be represented as:  
\[F_{R_{n}}(r)=\sum_{i=0}^{n-1}\binom{N}{i}p^{i}(1-p)^{N-i},\ \ 0\leq r\leq R, \tag{1}\]where \(N\) is the total number of points in the entire network, \(R\) is the radius of the overall coverage and \(p\) is the probability that a point is within the radius \(r\).  
According to [24], the binomial random variable can be converted into an incomplete beta function as follows.  
\[F_{R_{n}}(r)=I_{1-p}(N-n+1,n)=\frac{\int_{0}^{1-p}t^{N-n}(1-t)^{n-1}dt}{B(N-n+1,n)} \tag{2}\]  
where the beta function, \(B(a,b)\), is given by \(B(a,b)=\dfrac{\Gamma(a)\Gamma(b)}{\Gamma(a+b)}\) and \(\Gamma(\cdot)\) indicates the gamma function. Using (2), \(f_{R_{n}}(r)\) can be calculated as:  
\[f_{R_{n}}(r)=-\dfrac{\partial F_{R_{n}}(r)}{\partial r}=\dfrac{dp}{dr}\dfrac{ (1-p)^{N-n}p^{n-1}}{B(N-n+1,n)}. \tag{3}\]  
From (3), the expected value of \(R_{n}\) (\(E[R_{n}]\)) can be represented by:  
\[\begin{split} E[R_{n}]&=\dfrac{2}{R}\dfrac{1}{B(N- n+1,n)}\int_{0}^{R}\biggl{[}r\biggl{(}\dfrac{r}{R}\biggr{)}^{2n-1}\biggl{(}1- \biggl{(}\dfrac{r}{R}\biggr{)}^{2}\biggr{)}^{N-n}\biggr{]}dr\\ &=\begin{cases}\dfrac{R\cdot n^{\frac{1}{2}}}{(N+1)^{\frac{1}{2} }},&\text{if $n+\frac{1}{2}>0$}\\ \infty,&\text{otherwise}\end{cases}\end{split} \tag{4}\]  
where \(t=\Bigl{(}\dfrac{r}{R}\Bigr{)}^{2}\), \(B_{0}(a,b)=\begin{cases}0,&Re(a)>0\\ -\infty,&Re(a)\leq 0\end{cases}\) and \(B_{1}(a,b)=B(a,b)\) if \(Re(b)>0\).  
Moreover, from \(n=N-k+1\), the \(k\)-th furthest distance from the source device can be calculated from the n-th nearest distance.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 3 Experiments and Results
### Satellite Visibility Analysis  
In the simulated constellation, we randomly selected 500, 1000, 2000, 4000, 6000, 8000, 10,000 and 12,000 satellites and calculated the grid codes of each latitude, longitude, and height area at the level of 15 th. We then compared the efficiency of the grid method after testing in the BigTable, including a time comparison with the grid geometric visibility algorithm and the traditional geometric visibility algorithm (the satellite visibility means whether the two satellites can see through the earth and atmosphere). The calculation time of the methods above is shown in Table 4 and a comparison of different methods is shown in Figure 10.  
It can be seen from Figure 10 that the calculation time using traditional algorithms is related to the number of satellites, and increases exponentially with the number. However, when using the grid code method, only one of the height codes in the two-point connection grid set needs to be negative. This method avoids complex spatial operations, and the calculation efficiency is 1.81 times higher than that of the traditional algorithm.  
What is more, the position of the grid relative to the center of the earth is fixed, so the visibility relationship between the cell and other cells can be calculated in advance. When the satellite enters several cells with a clear visibility relationship, it is only necessary to query the grid look-up table calculated in advance. Through the pre-calculated grid visibility look-up table, the visible cells of the grid can be directly queried, and the efficiency is 23.62 times that of the traditional algorithm.
## 2 Materials and Methods
### Determination of the Percent (%) Angiotensin Converting Enzyme (ACE) Inhibition  
The ACE activity is determined using the method described by [PERSON] et al. [22] with few modifications. Initially, 100 uL of HHL buffer (5 mM hippuryl-L-histidine-L-leucine [HHL] in 0.1 M phosphate buffer [pH 8.2] with 0.3 M NaCl) was placed in different 2.0 mL Eppendorf tubes. For the sample replicates (undigested and digested crude, partially purified, and purified protein extracts), 25 uL of the sample was added to the HHL buffer in the Eppendorf tubes. For the method blank, 25 uL of enzyme blank (mixture containing only the enzymes used for digestion) was added. For the positive control, 25 uL of 50 ug/mL ctoppril solution was added. For the main control (full reaction control where ACE was not inhibited), only 25 uL of the extraction buffer was added. The resulting mixtures were incubated in a water bath at 37 \({}^{\circ}\)C for 4 min with occasional stirring in an incubation chamber. Then, 25 uL of purified ACE was added to the sample tubes except for the method blank. For the method blank, 125 uL of 1.0 M HCl was added. The resulting mixtures were incubated again in a water bath at 37 \({}^{\circ}\)C for 30 min. After incubation, 125 uL of 1.0 M HCl was added to the mixtures (sample replicates, positive control and main control) to terminate the reaction, while for the method blank, 25 uL of ACE was added. Afterwards, 750 uL of ethyl acetate was added to all the sample tubes and the mixture was stirred using vortex mixer. The resulting mixtures were allowed to stand under room temperature condition until there was a visible separation of layers. The top layer of the mixture was obtained and placed in another set of empty Eppendorf tubes and was evaporated to dryness in a steam bath until there were no more traces of ethyl acetate detected. These replicates were then reconstituted with 500 uL of 1.0 M NaCl and the absorbance of the resulting solution was read at 228 nm using a UV-Vis spectrophotometer. The percent (%) ACE inhibition of the samples was calculated using the formula:  
\[\%\ \text{ACE Inhibition}\ =\ \left(\frac{C\ -\ A}{C\ -\ B}\right)\times 100 \tag{1}\]  
where \(C\) is the absorbance of the main control, \(A\) is the absorbance of the sample or the positive control, and \(B\) is the absorbance of the method blank.
## 6 Challenges and Future Works
Therefore, there will be a requirement to build a collaborative generic platform, while keeping the overall interoperability issues of hardware and software in mind, to enable reliable communication and information sharing among millions of AVs while on the road, as the platform is collaborative; thereby, vehicular companies will be able to monitor the overall performances of their vehicles on the road in real time. However, depending on the polices and defined rules, a vehicular company can be restricted in the specific information that it can access and share with others;
4. There are several cloud and blockchain platforms, such as public, private, and hybrid, and their services have been offered by numerous companies, such as IBM, Google, and Oracle, in addition to others. Similar to other sectors, vehicular companies will install their AVs with distinct cloud and blockchain platforms; thus, there will the requirement to have a collaborative platform to share information among different clouds and blockchain platforms. For example, let us suppose that there is a vehicular company (A) that employs Google Cloud and the blockchain service by Ethereum, and there is another company (B) that installs the IBM cloud and the Hyperledger Fabric platform. There is a necessity to have a collaborative platform to share information without any limits of interoperability, availability, security, etc. This platform is important, but it is a challenging problem to have AVs from several companies on the road;
5. In the near future, there will probably only be AVs on the road; for this, an intelligent platform will be required to monitor and manage a huge number of resources and information 24 h a day, or whenever the AVs function to perform any service, with a guarantee of maintaining better QoS. To offer better QoS, a more resilient hybrid cloud platform will be required to manage a large amount of information and resources. Offering a hybrid cloud service will be a better solution, as the vehicular companies will need to participate in collaboration to run their millions of vehicles on the road. Without collaboration, it will probably be impossible to operate AVs from different companies on the road. However, there should be a cybersecurity mechanism that employs cryptography, blockchain, etc., to ensure the overall information between vehicular companies having either private or hybrid cloud servers, hundreds of millions of AVs, parking stations, etc. For this, and particularly to manage such a crowded AV infrastructure with concrete reliable cybersecurity mechanisms, introducing distributive cloud-based blockchain approaches will be a better solution to combatting the challenges and to ensuring secure two-way communication in an autonomous world.