esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bearing Fabrication and Characterization Tests  
### Fabrication of Test Bearing  
Figure 2a shows the GFPB's bump foil fabrication process. The top/bump/bottom foil structure was made of Inconel X-750 steel plates with a thickness of 127 \(\upmu\)m by Table 1. Inconel X-750 is suitable for bearing foil structures because of its high elastic deformation and high heat resistance with Ni-Cr alloys [14]. In addition, the top foil was coated with 20 \(\upmu\)m thick MoS2 to reduce friction during the initial drive and stop. MoS2 has a higher load-carrying capacity and a lower friction coefficient than other solid lubricants. The bump was molded by compressing molds with bump shapes into a hydraulic press machine. The hydraulic press machine applied a load of 177 kN (approximately 18 tons) to the bump foil. This is the load under which the bump foil is perfectly molded [15].  
Figure 2: Photos of bearing fabrication process: (**a**) a hydraulic press for bump foil forming, and (**b**) fabricated test GFB and GFPB.  
The manufactured GFBs and GFPBs are shown in Figure 2b. The polymer layer of GFPB used a type of natural rubber, nitrile butadiene rubber (NBR). NBR is used as an o-ring and seal because of its excellent oil resistance and wear resistance. It has a stable heat resistance of \(-55\)\({}^{\circ}\)C to 125 \({}^{\circ}\)C. NBR has better damping properties than commonly used engineering rubbers, such as natural rubber, styrene butadiene rubber, and ethylene propylene diene monomers, making it suitable for vibration reduction [16; 17]. Therefore, in this work, an NBR polymer with a thickness of 2 mm and width of 35 mm was applied to the bearings.
## 2 Data and Methodology
### Periodic Halo Orbits Calculation  
The periodic halo orbits calculation around L1 in the Earth-Moon system is derived from a solution to the three-body problem. Generally, the motion of a relatively massless orbiter moving in the vicinity of the L1 point is usually simplified as the circular restricted three-body problem (CRTBP), which supposes the Moon making a uniform circular motion around Earth. The three-dimensional equations of motion can be described as the following expressions [31; 32]:  
\[\tilde{r}+2\begin{pmatrix}-\dot{y}\\ \dot{x}\\ 0\end{pmatrix}=\left(\frac{\partial\Omega}{\partial r}\right)^{\mathrm{T}}, \tag{1}\]  
though generally, there is no analytical solution, the family of halo orbits around L1 can still be approximated by a third-order analytic solution using the series construction method under the hypotheses of a linearized non-homogeneous term and a small deviation from L1. The final formulation used to calculate halo orbits is expressed as follows [31]:  
\[\begin{array}{ll}\delta x=&\alpha\cos\tau+a_{21}\alpha^{2}+a_{22}\beta^{2}+ \left(a_{23}\alpha^{2}-a_{24}\beta^{2}\right)\cos 2\tau+\left(a_{31}\alpha^{3}-a_{32} \alpha\beta^{2}\right)\cos 3\tau\\ \delta y=&k\alpha\sin\tau+\left(b_{21}\alpha^{2}-b_{22}\beta^{2}\right)\sin 2 \tau+\left(b_{31}\alpha^{3}-b_{32}\alpha\beta^{2}\right)\sin 3\tau+\\ &\left[b_{33}\alpha^{3}+\left(b_{34}-b_{35}\right)\alpha\beta^{2}\right]\sin \tau\\ \delta z=&\beta\cos\tau+d_{21}\alpha\beta(\cos 2\tau-3)+\left(d_{32}\beta\alpha^{2}-d_{31} \beta^{3}\right)\cos 3\tau\end{array}, \tag{2}\]  
In order to obtain a more accurate solution, a differential correction method is needed to conduct iteration until convergence to a local solution. Note that the variables are based on normalized dimensionless units and an intermediate coordinate system, which will be demonstrated in detail later, and, of course, these transformation processes have been considered in the calculation program. For more explanation, please refer to [31]. However, the actual satellite orbit inevitably receives the influence of gravitational perturbation from inside and outside the solar system. Therefore, this paper replaces the Moon's uniform circular motion in CRTBP with JPL DE430 data and takes the small-amplitude halo orbits as examples in consideration of minimizing the effects of gravitational perturbations.
## 2 Keratin-Based Nanoparticles
### Human Hair Keratin-Based Nanoparticles
#### 2.1.1 Doxorubicin-Loaded Human Hair Keratin-Based Nanoparticles
Importantly, the authors demonstrated that their nanogels were able to improve intracellular NO levels and enhance the anticancer efficacy of DOX in vitro and in vivo, while having lower side effects compared to free DOX. [PERSON] and collaborators described keratin/DOX complex nanoparticles obtained by ionic complexation, hydrogen bonding, and hydrophobic interactions between the protein and DOX [51]. These particles exhibited excellent pH/GSH/enzyme triple stimuli-responsiveness, resulting in efficient drug release under tumor microenvironment mimicked conditions. In vivo cytotoxicity and hemolysis analyses showed that KDNPs exhibited excellent biocompatibility as well as good blood compatibility. Antitumor efficacy in tumor-bearing mice indicated that KDNPs had a strong antitumor effect, similar to that exerted by free DOX. Very recently, [PERSON] and colleagues described the production of nanoparticles made of a keratin-tannic acid complex loaded with DOX [52]. Tannic acid is a natural polyphenol generally used as a non-toxic cross-linker for proteins. These nanoparticles had an average hydrodynamic diameter of 240 nm and exhibited a dual pH and GSH responsiveness, being capable of releasing DOX at the tumor site. In vitro analyses demonstrated that DOX-loaded nanoparticles were effectively endocytosed by tumor cells and had a higher inhibitory effect on tumor cells as opposed normal cells. Similarly, [PERSON] et al. studied the use of human hair keratin for the preparation of keratin/DOX nanoparticles with the desolvation/cross linking method, obtaining particles of \(\sim\)182 nm with an encapsulation efficiency of 47%, and a drug loading rate of 14% [53; 54]. Moreover, the reported nanoparticles exhibited good pH/GSH responsiveness as well as charge reversibility under tumor microenvironment conditions, e.g., low pH and a high GSH level, which favored their internalization into A549 tumor cells. Keratin/DOX nanoparticles showed low toxicity towards normal cells and a low hemolysis ratio; conversely, they efficiently inhibited tumor cells growth in vitro.
## 3 Experimental
### Treatment of Trees with OC Kappa and Inhibitors/OC Kappa  
_E. globulus_ trees with an initial height of 30 cm (_n_ = 10 for each group) were cultivated outdoors in plastic bags containing compost. _E. globulus_ trees were sprayed in the upper and lower part of the leaves with 5 mL per plant with water/methanol 9:1 _v/v_ (control group, \(n\) = 10), an aqueous solution of OC kappa at a concentration of 1 mg\(\cdot\)mL\({}^{-1}\) (treated group 1, \(n\) = 10), a water/methanol solution of 250 \(\upmu\)M CHS-828, an inhibitor of nicotinamide phosphoribosyltransferase[27] and NAD(P)H synthesis (treated group 2, \(n\) = 10), a water/methanol solution of 250 \(\upmu\)M lycorine, an inhibitor of galactonolactone dehydrogenase[28] and of ASC synthesis (treated group 3, \(n\) = 10), a water/methanol solution of 1.5 mM buthionine sulfoximine (BSO), an inhibitor of \(\upgamma\)-glutamylcysteine synthase[29] and of GSH synthesis (treated group 4, \(n\) = 10), and with auranofine[30], an inhibitor of TRR activity (treated group 5), and with OC kappa at a concentration of 1 mg\(\cdot\)mL\({}^{-1}\). Trees of treated groups 2, 3, 4 and 5 were initially treated twice with CHS-828, auranofine, lycorine or BSO, and after two weeks they were treated with OC kappa once a week, four times in total, cultivated without any additional treatment for 4 months. Leaves were obtained from the middle part of control and treated trees and pooled into three groups to perform further analysis (_n_ = 3). The height of trees was determined using a measuring tape.  
It is important to mention that the selected concentration of OC kappa (1 mg\(\cdot\)mL\({}^{-1}\)) is the optimal concentration to stimulate growth in _Eucalyptus_ trees, since a higher concentration of 5 mg\(\cdot\)mL\({}^{-1}\) did not further increase growth and a concentration of 10 mg\(\cdot\)mL\({}^{-1}\) inhibited the increase in growth (unpublished data). In addition, the chosen period of culture was 4 months because from three months forward the effect of OC kappa on basal metabolism was clearly evident[20]. Moreover, it is important to point out that the optimal concentration CHS-828 decreased NADPH content, lycorine inhibited galactonolactone dehydrogenase (GLDH) activity, BSO inhibited \(\upgamma\)-glutamylcysteine synthase (\(\upgamma\)-GCS) activity and auranofine inhibited TRR activity at four months of culture without additional treatment[20].
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 5 Formal Verification
### Formal Verification via BAN Logic
#### 5.1.1 The Initiation Phase: Step 1  
The idealization form of the Initiation Phase of the protocol is shown below:  
\[\mathit{UE}\to\mathit{HN}\colon\langle\left\{\text{SUPI},\mathit{UE} \stackrel{{ k_{2}}}{{\Longrightarrow}}\mathit{HN}\right\}\rangle_{k_{1}} \tag{1}\]  
We added Assumptions (2) to (5) to verify through BAN Logic.  
\[\mathit{HN}\mid\equiv\mathit{UE}\stackrel{{ k_{1}}}{{ \Longrightarrow}}\mathit{HN} \tag{2}\]  
\[\mathit{HN}\mid\equiv\#\left(\mathit{UE}\stackrel{{ k_{1}}}{{ \Longrightarrow}}\mathit{HN}\right) \tag{3}\]  
\[\mathit{HN}\mid\equiv\mathit{UE}\stackrel{{ k_{1}}}{{ \Longleftrightarrow}}\mathit{HN} \tag{4}\]  
\[\mathit{HN}\mid\equiv\#\left(\mathit{UE}\stackrel{{ k_{2}}}{{ \Longleftrightarrow}}\mathit{HN}\right) \tag{5}\]  
Technically, all the aforementioned assumptions could be invalidated as the HN inherently lacks trust in the UE's public key \(\mathit{PK}_{\mathit{UE}}\). However, in the case of 5G AKA, since it was designed to tolerate a replay attack or Man-in-the-Middle (MITM) attack on the public key, we followed the standard and added the above assumption to continue this analysis.  
Therefore, we set a security goal that HN believes that UE believes in SUPI, and derived this goal through the derivation step.  
\[\mathit{HN}\mid\equiv\mathit{UE}\ \mid\equiv\text{SUPI} \tag{6}\]  
from (1), and we derive  
\[\mathit{HN}\triangleleft\langle\left\{\text{SUPI},\mathit{UE}\stackrel{{ k_{2}}}{{\Longrightarrow}}\mathit{HN}\right\}_{k_{2}},\mathit{UE} \stackrel{{ k_{1}}}{{\Longrightarrow}}\mathit{HN}\rangle_{k_{1}} \quad\text{by (1)} \tag{7}\]  
\[\mathit{HN}\mid\equiv\mathit{UE}\ |\sim\left(\left\{\text{SUPI},\mathit{UE} \stackrel{{ k_{2}}}{{\Longrightarrow}}\mathit{HN}\right\}_{k_{2}}, \mathit{UE}\stackrel{{ k_{1}}}{{\Longrightarrow}}\mathit{HN} \right)\quad\text{by (7), (2), MM} \tag{8}\]  
\[\mathit{HN}\mid\equiv\mathit{UE}\ |\equiv\left\{\text{SUPI},\mathit{UE} \stackrel{{ k_{2}}}{{\Longrightarrow}}\mathit{HN}\right\} \quad\text{by (8), (3), FR, NV, BC} \tag{9}\]  
\[\mathit{HN}\mid\equiv\mathit{UE}\ |\equiv\text{SUPI}\quad\text{by (9), (4), MM, (5), FR, NV, BC} \tag{10}\]  
According to the derivation above, the proposed security protocol can achieve the security goal (6) stated in the goal. This means that the HN believes that UE believes about SUPI, in the initiation phase of the proposed protocol.
# Vibration Analysis and Empirical Law Definition for Different Equipment in a Civil Construction
## 2 Materials and Methods
### Vibration Fundamentals  
The vibration propagation to the ground depends on many different factors, such as the rock mass anisotropy, its heterogeneity and the degree of fracturing, among other elements [42].  
The vibration behavior along the distance, from a certain source, can be analyzed taking into account, or not, a damped response from the rock mass. Its simplest way is the attenuation vibration law without a damped effect, as it is seen in Equation (1).  
\[v_{max}=v_{0}\ \left(\frac{r_{0}}{r}\right)^{\gamma} \tag{1}\]  
where vmax (mm/s) is the peak particle velocity (PPV) or maximum velocity at a distance r from the source; it is assumed that the initial velocity v0 (mm/s) at a certain distance r0 (m) is known. The \(\gamma\) coefficient (dimensionless) is an empirical value that depends on the characteristics of the ground and the wave frequency, usually between 1.4 and 1.7 [43], obtaining the value by in situ measurements or using bibliographical values. Equation (1) can be applied when the rock mass has very good quality and for short distances, assuming that the vibration amplitude waves diminish with distance due to the expansion of the wave front [44].  
On the other hand, the attenuation vibration with the damped effect occurs when we have soil or poor-quality rock masses and long distances, having an inelastic attenuation due to heat losses and yielding of the ground. Therefore, the vibration amplitude decreases because of the expansion of the wave front and the inelastic behavior, as in Equation (2).  
\[v_{max}=v_{0}\left(\frac{r_{0}}{r}\right)^{\gamma}e^{\alpha\ \left(r_{0}-\gamma \right)} \tag{2}\]  
where \(\alpha\) (m\({}^{-1}\)) is an empirical coefficient based on the damping characteristics of the  
The great advantages of empirical formulas, such as Equations (1) and (2), are their simplicity and easy understanding. They are widely accepted in real projects [27; 33; 34; 35; 36; 37; 38], achieving very good correlations with in situ measurements if they are applied under the proper conditions. However, further developments are necessary to cover as many conditions as possible in future situations.  
The procedure to fit Equations (1) and (2) to observed data from different soils or rock masses is extensively explained by [PERSON] and [PERSON] [17], obtaining the \(\gamma\) coefficient from each specific case study. This approach has been successfully used in many previous studies [21; 24; 37].
## 3 Results
### KPIs Definition  
A brief explanation of the indicators utilized for the presentation of the results is provided in this subsection.  
In order to assess the prediction power of the predictive models, we have used the confusion matrix (CM) as a basic unit of evaluation. The CM consists of four labels given to each prediction according to its veracity. In summary, these labels are true positives (TP, a failure occurs when a failure was predicted), false positives (FP, no failure when a failure was predicted), true negatives (TN, no failure when no failure was predicted) and false negative (FN, failure when no failure was predicted). Using the count of these basic evaluation units, the main KPIs are calculated.  
The main KPIs used in this project are sensitivity, specificity, accuracy, Kappa, precision and F1 score. Sensitivity, Recall is the ratio of predicted events over the total of events:  
\[\textit{Sensitivity},\textit{ Recall}=\frac{TP}{TP+FN} \tag{4}\]  
Specificity is the ratio of well-predicted negative events over the total of negative events:  
\[\textit{Specificity}=\frac{TN}{TN+FP} \tag{5}\]  
Accuracy is the ratio of the total well-predicted observations over the total number of observations:  
\[\textit{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
[PERSON]'s Kappa is defined as follows:  
\[K=\frac{p_{0}-p_{e}}{1-p_{e}} \tag{7}\]  
where \(p_{0}\) is the relative observed agreement among raters, which is analogous to accuracy, and \(p_{e}\) is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of each observer randomly seeing each category. For categories, \(k\), number of items, \(N\), and \(n_{ki}\), the numbers of times the rater \(i\) predicted category \(k\), \(p_{e}\) can be calculated as follows:  
\[p_{e}=\frac{1}{N^{2}}\sum_{k}n_{k1}n_{k2} \tag{8}\]  
A low value of \(K\) means that there is no agreement among the raters other than what would be expected by chance. A \(K\) value close to one is an indication of good performance of the classifier.  
Precision is the ratio of predicted events over the total of positive predictions:  
\[\textit{Precision}=\frac{TP}{TP+FP} \tag{9}\]  
F1 score is defined as the harmonic mean of precision and recall and it is typically used to measure the accuracy of a test:  
\[F1=2\frac{\textit{precision}\star\textit{recall}}{\textit{precision}+\textit{ recall}}=\frac{TP}{TP+\frac{1}{2}(FN+FP)} \tag{10}\]
## 3 Experimental Results and Discussion
### Load-Carrying Capacity
#### 3.1.1 Effect of Perforated Hole Arrangement  
The FP series specimens attained an ultimate load capacity ranging from 206 kN to 225 kN, which is 6.3% to 14.2% lower than specimen CS. The adoption of TMS plates without any holes appears to create a substantial area of partition effect that separates the side concrete cover from the core of the beam [19]. As a result, the FP series specimens had a much lower ultimate load capacity than specimen CS and the HP series specimens.  
For the HP series specimens, the attained ultimate load-carrying capacity ranged from 225 kN to 247 kN, recording \(-6.3\%\) to \(+2.9\%\) differences compared with specimen CS, but demonstrating a higher ultimate load attainment than the FP series specimens. This phenomenon was also noticed by [PERSON] et al. [12] in their study, in which concretenessed steel sections with holes in the web attained a higher shear capacity than the specimen without any holes in the web. The presence of perforated holes in the HP series encouraged the concrete matrix passing through the plate, allowing the formation of tenons enabling a better force transfer from the concrete to the steel plates [20]. Furthermore, the formation of tenons acted as concrete dowels that delayed slippage and debonding of the steel plate-concrete interface.  
When comparing the HP series specimens (HP3, HP4, and HP5), the HP3 series specimens showed a slightly higher ultimate load attainment than the other two series specimens. In particular, the HP3-2.0 specimen attained an ultimate load of 247 kN, which is 2.1% and 5.1% higher than the HP4-2.0 and HP5-2.0 specimens, respectively. When the number of columns of holes increased from three to five, the ultimate load decreased, regardless of the thickness of the TMS plate being used. This indicates that introducing more columns of holes into the TMS plate adversely affects the stiffness of the plate [21] and reduces the benefit contributed by concrete tenon formation to the overall load-carrying capacity. This is due to the compressive strut in the deep beam having passed through the TMS plate with a greater unreinforced area due to closely spaced holes in the TMS plate.
## 3 Experiment Verification  
### Experimental Dataset  
The study utilized the Guangdong-Hong Kong-Macao Greater Bay Area Radar Echo Image Dataset provided by the Shenzhen Meteorological Bureau. A pseudo-color-processed visualization sample, as depicted in Figure 9, was used. This sample comprises 41 consecutive temporal images with a time interval of 6 min between each image, covering a duration of 4 h. The images are colorized based on the radar echo intensity (DBZ) values. The conversion formula for relating radar echo intensity to image grayscale values is presented in Equation (8) [32].  
\[gray=\frac{DBZ*255.0}{73} \tag{8}\]  
In the equation, DBZ represents radar echo intensity measured in dBZ. Typically, radar echo intensity in areas experiencing rainfall is above 30 dBZ. For severe weather phenomena such as heavy rainfall, hail, strong winds, and thunderstorms, the echo intensity can reach 50-60 dBZ. In cases of extreme weather conditions, the radar echo intensity usually exceeds 60 dBZ [33]. The dBZ values corresponding to the image colors can be determined using the composite reflectivity table located on the left side of the dataset sample image.  
In the same sample sequence, the radar echo images have a horizontal resolution of 1 km. An important advantage of using radar echo images for research is their ability to significantly enhance the accuracy of precipitation forecasts for small areas when high-resolution images are used. These images have a width and height of 500 pixels, covering a 500 km \(\times\) 500 km region, and an altitude of 2.5 km. Lower-level images are often affected by clutter, which makes upper-level echo features less distinct. However, the 2.5 km-altitude layer exhibits strong horizontal distribution characteristics, making it the most representative. To improve the generalization ability of the model after training, the dataset includes samples of both precipitation and non-precipitation weather. The SRAD dataset is segmented and shuffled using a sliding window approach, resulting in sequences of 20 consecutive frames, with the first 10 frames serving as the input sequence and the last 10 frames as the prediction sequence.  
The dataset is divided into training, validation, and test sets in a 5:1:1 ratio. Specifically, 10,000 data samples are used for training, 2000 for validation, and 2000 for testing. During  
Figure 9: Sample dataset visualization.  
training, the model is trained using the training set, its fitting performance is evaluated using the validation set, and its final performance is assessed using the test set.
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 5 Discussion  
### Renewable Energy Sources  
Wind and solar energy are employed as renewable energy sources to generate electricity to power electrolysis for H\({}_{2}\) synthesis [75]. Solar energy has been used in applicationssuch as photovoltaic systems (PVs), concentrated solar power (CSPs) systems, and wind energy to power electrolysis equipment. A converter (DC/DC or AC/DC) is required for establishing the power of renewable energy sources with electrolysis input. Rather than the high expense of power transmission, renewable energy sources are being used to provide electricity in remote places [76]. The excess energy generated by green energy sources has been utilized to power electrolysis for H2 generation. The electrolysis is powered by PV panels, a solar charger, and a DC/DC converter via an electrical circuit known as a maximum power point tracker. In the event of low solar radiation, the battery is thought to serve as energy storage. The PV/H2 system has several advantages over other renewable energy sources, including the utilization of DC voltage and verified parts that require minimal maintenance. The CSP/H2 system uses heat instead of electricity to power the electrolysis system and convert water to steam via the SOE [77]. Thermal storage in the CSP/H2 system enables continuous H2 generation. Research was undertaken [78] to compare the efficiency of each system under identical operating conditions. The results revealed that the CSP/H2 system performs better than the PV/H2. Furthermore, wind energy was employed to power the electrolysis (wind/H2) unit by supplying electricity through an AC/DC converter. Wind energy is available 24 h a day, not just during the day, like solar energy; however, it is an uneven energy source owing to its nature.