esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
\begin{tabular}{l l l l} \(h\) & The thickness of MRCS (m) & \(\delta\) & Variable Score Symbols (-) \\ \(R\) & The radius of the middle surface of MRCS (m) & \(t\), \(t_{1}\), \(t_{2}\) & Times, Starting moment, Ending moment (s) \\ \(L\) & Cylindrical shell length (m) & \(\omega\) & Round Frequency(rad) \\ \(u\), \(v\), \(w\) & Displacement of the shell along \(x\), \(\theta\), \(z\) direction (m) & \(n\) & Circumferential wave number (-) \\ \(\psi_{x}\), \(\psi_{\theta}\) & Rotation angles normal to the direction of the \(x\) and \(\theta\) axis (rad) & \(m\) & axial half-wave number (-) \\ \(a_{1}\), \(a_{2}\) & Lame coefficient in the x, \(\theta\) direction in the middle surface (-) & \(\delta_{kl}\) & Kronecker constant (-) \\ \(R_{1}\), \(R_{2}\) & The radius of curvature in the \(x\), \(\theta\) direction in the middle & \(f\) & Natural frequency of MRCS (Hz) \\ \(A_{1}\), \(A_{2}\) & Lame coefficient along \(x\), \(\theta\) direction in cylindrical shell (-) & \(\eta\) & The modal loss factor of MRCS \\ \(n_{m}\) & Number of screws of equivalent spring unit (-) & \(\beta\) & Half cone angle of equivalent spring unit (rad) \\ \(E_{1 kl}\), & Equivalent modulus of elasticity (MPa) & \(\eta_{1}\), \(\eta_{2}\) & Normal strain loss factor and shear strain loss factor of MR \\ \(\psi_{x}\), \(\psi_{x}\) & Normal strain of shell along \(x\), \(\theta\) direction (-) & \(N_{A}\) & Number of helix unit in the axial section of MRCS (-) \\ \(\psi_{x}\), \(\psi_{x}\), \(\psi_{\theta}\) & Rotation angles normal to the direction of the \(x\) and \(\theta\) axis (rad) & \(N_{L}\) & Number of helix unit in length \(L\) of MRCS (-) \\ \(a_{1}\), \(a_{2}\) & Lame coefficient in the x, \(\theta\) direction in the middle surface (-) & \(\theta_{m}\) & Relative density of Metal-Rubber samples (\(\psi_{x}\)) \\ \(R_{1}\), \(R_{2}\) & The radius of curvature in the \(x\), \(\theta\) direction in the middle surface (-) & \(\theta_{m}\) & Relative density of Metal-Rubber blanks (\(\psi_{x}\)) \\ \(R_{1}\), \(R_{2}\) & The radius of the middle surface (-) & \(a\), \(b\) & Load correction factor (-) \\ \(\mu\) & Poisson’s ratio (-) & \(\alpha\) & Guidance angle (rad) \\ \(\rho\) & The density of MRCS (kg/m\({}^{3}\)) & \(d\), \(D\) & Wire diameter, Coil diameter (mm) \\ \(N_{x}\), \(Q_{x}\) & Membrane stresses (Pa) & N & Order number of mode phase function (-) \\ \(M_{x}\) & Membrane moments (Pa-m) & \(C\) & Spring index of the helix unit (-) \\ \(K_{s}\) & Transverse shear correction factor (-) & \(\varphi\), \(\bar{\varphi}\) & Inclination angle of the helix unit and its mean value (rad) \\ \(U_{x}\) & Shell strain energy (N-m) & \(r\) & molding ratio of MR (-) \\ \(T_{c}\) & Shell kinetic energy (N-m) & \(K_{T}\) & Axial stiffness of the helix unit (N/m) \\ \(\mu_{m}\) & Poisson’s ratio of MR & \(K_{V}\) & Tangential stiffness of the helix unit (N/m) \\ \end{tabular}
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 2 Methods
### Wavelet Denoising Index  
We used wavelet soft threshold denoising and wavelet packet analysis denoising to perform denoising respectively and selected six indicators: mean square error (\(MSE\)), sum square error (\(SSE\)), root mean square error (\(RMSE\)), coefficient of determination (\(R^{2}\)), mean absolute error (\(MAE\)) and signal-to-noise ratio (\(SNR\)) to evaluate the denoising effect. Among these six evaluation indexes, except for the coefficient of determination (\(R^{2}\)) and \(SNR\) which are maximum indexes, the other four indexes are minimum indexes. In addition to quantitative evaluation indicators, the denoising effect can also be intuitively displayed by comparing the original signal with the denoised signal image.  
Let \(y_{i}\) be the sampling point of the original signal, \(\hat{y}_{i}\) be the sampling point of the signal after denoising, and \(m\) be the number of sampling points.  
\[MSE=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2} \tag{2}\]  
\[SSE=\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2} \tag{3}\]  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{m}(y_{i}-\hat {y}_{i})^{2}} \tag{5}\]  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}|y_{i}-\hat{y}_{i}| \tag{6}\]  
\[SNR=10\times 1\lg\left[\frac{\sum_{i=1}^{m}y_{i}^{2}}{\sum_{i=1}^{m}(y_{i}- \hat{y}_{i})^{2}}\right] \tag{7}\]
## 3 Results and Discussion
### Texture Profile Analysis (TPA) of Bread  
The results of the bread crumb texture analysis, including four key parameters--hardness, springiness, cohesiveness, and chewiness--are presented in Table 4. As expected, storage time had a marked effect on texture characteristics across all samples, with notable differences depending on the type of cereal-based beverage and the level of substitution. Figure 2: Percentage distribution of crumb pores by surface area across defined size ranges. (Different lowercase letters above the bars indicate statistically significant differences between samples (Tukey’s test, \(p\leq 0.05\))). Hardness, a primary indicator of crumb firmness and staling, increased progressively after 72 h in all formulations. At 24 h, hardness values ranged from 7.5 N (BS75) to 10.0 N (BS100), with no statistically significant (\(p\geq 0.05\)) differences compared to the control sample (9.7 N). At 24 h, a weak correlation was found between substitution level and crumb hardness for BM (r = \(-0.17\)) and BS (r = \(-0.11\)), while the correlation was moderately negative for BO (r = \(-0.70\)), suggesting that higher oat substitution may have contributed to slightly lower hardness. After 72 h, a significant increase (\(p\leq 0.05\)) was observed in most variants, particularly in BM25 (19.4 N) and BM100 (12.7 N), suggesting more rapid firming and moisture loss in millet-based breads. Conversely, BO100 and BS75 retained relatively lower hardness values (9.5 N and 11.5 N, respectively), indicating better textural stability during storage. These findings align with the results reported by [PERSON] et al. [10], who observed increased crumb hardness with oat beverage residue flour--particularly at higher inclusion levels and larger particle sizes--due to gluten network dilution and fiber interference with gas retention. While the present study used whole beverages instead of concentrated residues, a similar trend was observed at higher substitution levels, particularly in millet-based samples. The observed increase in crumb hardness over time may be partially attributed to the presence of dietary fibers in the cereal-based beverages. Dietary fibers--especially insoluble ones--are known to enhance crumb firmness by promoting inter- and intramolecular hydrogen bonding and by increasing the mechanical strength of the dough matrix. This effect stems from their ability to compete for water with gluten proteins, leading to reduced hydration and plasticity of the gluten network and more extensive starch retrogradation during storage [43; 44]. Additionally,
## 3 Results and Discussion
### Porosity Measurements and Gas Storage Capacity of **1-3**  
The CO2 adsorption isotherm can be predicted directly from the quantity of CO2 uptake using a gravimetric technique [52]. Also, the quantity of CO2 removed from the gas phase could be used to estimate the physisorption isotherms of the gas. The textural properties of the pores of polyphosphates **1-3** were determined from the N2 adsorption-desorption isotherms recorded at 77 K. The N2 isotherms and pore sizes of polyphosphates **1-3** are shown in Figures 5-7, respectively. Polyphosphates **1-3** have mesoporous structures and showed type-III nitrogen sorption isotherms, in which no monolayer formation was identified. The Brunauer-Emmett-Teller surface areas (S\({}_{\text{BET}}\)), pore volumes, and average pore diameters of **1-3** are listed in Table 3. Among the synthesized polyphosphates, **2** exhibits the highest S\({}_{\text{BET}}\) (213.5 m\({}^{2}\)/g) and total pore volume (0.32 cm\({}^{3}\)/gm) and the lowest average pore diameter (1.96 mm). Polyphosphates **1-3** have a tetrahedral geometry with sp\({}^{3}\) hybridized phosphorus core [53]. The CO2 sorption isotherms for **1-3** (Figure 8) showed no apparent adsorption-desorption hysteresis, indicating possible reversible adsorption of CO2 within the pores of **1-3** at 323 K and 40 bars. The CO2 uptake for polyphosphates **1**, **2**, and **3** was 2.04, 6.00, and 4.57 wt %, respectively (Table 4). The high CO2 uptake could be due to the high S\({}_{\text{BET}}\) of the polyphosphates and strong van der Waals interaction and hydrogen bonding between CO2 and the polyphosphates. In addition, polyphosphates **1-3** contain strong Lewis base sites that help in capturing CO2. Indeed, POPs containing heteroatoms (O, N, S, or P) can capture CO2 selectively over nitrogen and methane [54; 55; 50; 56]. The CO2 uptake using carbon-containing materials such as porous nanocarbons in the presence of additives (e.g., ethylenediamine and potassium oxalate) as media for CO2 adsorption was 1.9-4.6 mmol/g at 25 \({}^{\circ}\)C [27]. While carbon fibers containing polyacrylonitrile in the presence of potassium hydroxide led to a CO2 uptake of 2.7 mmol/g at 25 \({}^{\circ}\)C and 1 atm [28]. The CO2 uptake \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Polyphosphate**} & \multicolumn{3}{c}{**FT-IR (Wavenumber; cm\({}^{\text{-1}}\)) a**} & \multicolumn{3}{c}{**1H-NMR (Chemical**} \\ \cline{2-6}  & **P-O–C** & **P=O** & **C=C** & **CH=N** & **Shift; ppm\({}^{\text{b}}\)** \\ \hline
**1** & 1206 & 1162 & 1594 & 1600 & 6.84-7.78 (m, 24H, Ar), \\  & & & & & 9.03 (s, 3H, CH) \\
**2** & 1205 & 1135 & 1568 & 1620 & 6.77-7.82 (m, 24H, Ar), \\  & & & & & 9.03 (s, 3H, CH) \\
**3** & 1233 & 1185 & 1566 & 1616 & 6.80-7.68 (m, 24H, Ar), \\  & & & & & 9.30 (s, 3H, CH) \\ \hline \hline \end{tabular}  
* Recorded using KBr disc; b Measured at 300 MHz in DMSO-4\({}_{6}\) (\(\delta\) in ppm and \(J\) in Hz).  
\end{table} 
Table 2: FT-IR (Wavenumber; cm\({}^{\text{-1}}\)) a
## 4 Findings
### Other Works  
#### 4.7.1 Cloud Environments  
Protecting the data-sharing process in cloud environments is crucial to eliminate any leakage or breach. A secure data-sharing scheme that integrates blockchain technology, Information-Centric Networking (ICN), Identity-Based Encryption (IBE), and Proxy re-encryption (PRE) is proposed [128]. PRE allows transforming a file from delegator to delegatee by encrypting the file with the delegator's public key. In IBE, the email (identity) is used as the public key for encryption. Confidentiality is achieved using a secure access control framework; security and privacy are ensured using a complete protocol based on the PRE scheme; network bandwidth utilization and enhanced data delivery are ensured through proxy nodes. The designed platform consists of data producers responsible for generating the data, cloud service providers (CSPs), data owners to whom the data belongs, and data users representing the recipients of the information. Performance analysis and comparison indicate that the proposed scheme is efficient compared to other works.  
It is possible to convince the verifier that a particular assertion of information is correct without involving any valuable information; this is called zero-knowledge proof, which can be generated using the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) tool. This approach utilizes zero-knowledge proof combined with smart contracts to allow trusted sharing between semi-trusted cloud servers, Cloud Service Providers (CSPs), and data owners [129]. Proxy re-encryption technology is integrated into this model to provide authorized CSPs with a secure data-sharing model. The proposedsystem model contains six entities: the blockchain network, smart contracts, private key generator (PKG), semi-trusted cloud server, cloud service organization, and data owner. Performance analysis is completed for content privacy, identity privacy, data validity, and traceability. This model is still in the research phase and has not been implemented yet.
# Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic
###### Abstract  
The security of cryptographic protocols has always been an important issue. Although there are various verification schemes of protocols in the literature, efficiently and accurately verifying cryptographic protocols is still a challenging research task. In this work, we develop a formal method based on dynamic epistemic logic to analyze and describe cryptographic protocols. In particular, we adopt the action model to depict the execution process of the protocol. To verify the security, the intruder's actions are analyzed. We model exactly the protocol applying our formal language and give the verification models according to the security requirements of this cryptographic protocol. With analysis and proof on a selected example, we show the usefulness of our method. The result indicates that the selected protocol meets the security requirements.  
cryptographic protocol; security verification; action model; dynamic epistemic logic 2020 April 2020, 10, 6577; doi:10.3390/app10186577
## 7 Conclusions  
The key field of concern in this study is the analysis of landslide susceptibility. Single and ensemble models were successfully used for landslide susceptibility evaluation in the Rudraprayag district of Garhwal Himalaya. ROC curve, efficiency, accuracy, MAE, and RMSE were employed to assess and compare these LSS models. Finally, using the CF the best model was selected. The findings of the validation techniques show that the LSS models prepared by ANN, FR, SVM, LR, and their ensembles performed well in this analysis. The ensemble of ANN, RF, SVM, and LR models played an improving role as they improved the values of AUC. However, with respect to the results of robustness, the ANN-RF-LR ensemble had the highest reliability in terms of results where the highest agreement was found in the efficiency, accuracy, AUC of success rate curve, and predictive rate curve values, MAE and RMSE. The present research shows that ensemble models are effective tools that could be used in landslide prediction and landslide management for efficient decision-making by the authorities. In the present analysis, these models were utilized in the Rudraprayag district. However, for wider use, the output of such models may be used for areas of similar geo-environmental conditions. The key drawback of this analysis is that we used a limited set of LCFs and a fixed training testing samples dataset ratio (70:30) for the all the models. The performance of the models will have to be further tested through a variety of training and testing sets of sample datasets as well as LCF sets.  
Methodology; formal analysis, S.S., and T.K.H., and S.S.; investigation,; writing--original draft preparation,; writing--review and editing, S.S., and A.S. performed the experiments, wrote the manuscript, and collected the field data; S.S. wrote the manuscript and analyzed the data; B.P. supervised, edited, restructured, and professionally optimized the manuscript; B.P. and A.M.A., arranged the funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, the University of Technology Sydney (UTS). This research was also supported by Researchers Supporting Project number RSP-2020/14, King Saud University, Riyadh, Saudi Arabia.  
The authors declared no conflict of interest.
# Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events
## 6 Calculating Expected Survival Time by the Weibull AFT Model  
In reliability research, the expected survival time is often referred to as the mean time to failure (MTTF) or mean time between failures (MTBF) [8].  
To predict an individual's mean survival time \(t_{i}\) using the Weibull AFT model, we first use the MLE method, as described in Equation (20) to calculate the estimates \(\hat{\beta}\) and \(\hat{\sigma}\). Then, by the invariance property of the MLE, we can directly compute the predicted MTTF using Equation (15):  
\[t_{i}=\exp(x_{i}^{\prime}\hat{\beta})\Gamma(\hat{\sigma}+1)\]  
After calculating the MTTF, we can apply the Delta method to establish a confidence interval for the MTTF. This method treats the predicted MTTF as a function of \(\hat{\beta}\) and \(\hat{\sigma}\). The standard error of the MTTF can be calculated as:  
\[SE=\left\{\begin{array}{l}\left(\frac{\partial E(\hat{t}_{i})}{\partial \hat{\beta}}\right)^{t}\Sigma_{\hat{\sigma}\hat{\beta}}\left(\frac{\partial E (\hat{t}_{i})}{\partial\hat{\beta}}\right)\\ \left(\frac{\partial E(\hat{t}_{i})}{\partial\hat{\sigma}}\right)\end{array} \right\}^{\frac{1}{2}} \tag{21}\]  
where \(\Sigma_{\hat{\sigma}\hat{\beta}}\) is the variance-covariance matrix of \(\hat{\beta}\) and \(\hat{\sigma}\). It can be estimated by the observed Fisher information of the Weibull AFT model. The (1-\(\alpha\))% confidence interval is given as:  
\[\hat{t}_{i}-z_{1-\frac{\alpha}{2}}SE<t_{i}<\hat{t}_{i}+z_{1-\frac{\alpha}{2}}SE \tag{22}\]  
Here, \(\alpha\) represents the type I error, and \(z\) is the quantile of the standard normal distribution.
# Improvement of Mechanical Properties of 3D Bioprinted Structures through Cellular Overgrowth
###### Abstract  
The common use of hydrogel materials in 3D bioprinting techniques is dictated by the unique properties of hydrogel bioinks, among which some of the most important in terms of sustaining vital cell functions in vitro in 3D cultures are the ability to retain large amounts of liquid and the ability to modify rigidity and mechanical properties to reproduce the structure of the natural extracellular matrix. Due to their high biocompatibility, non-immunogenicity, and the possibility of optimizing rheological properties and bioactivity at the same time, one of the most commonly used hydrogel bioink compositions are polymer solutions based on sodium alginate and gelatin. In 3D bioprinting techniques, it is necessary for hydrogel printouts to feature an appropriate geometry to ensure proper metabolic activity of the cells contained inside the printouts. The desired solution is to obtain a thin-walled printout geometry, ensuring uniform nutrient availability and gas exchange during cultivation. Within this study's framework, tubular bioprinted structures were developed based on sodium alginate and gelatin, containing cells of the immortalized fibroblast line NIH/3T3 in their structure. Directly after the 3D printing process, such structures are characterized by extremely low mechanical strength. The purpose of this study was to perform a comparative analysis of the viability and spreading ability of the biological material contained in the printouts during their incubation for a period of 8 weeks while monitoring the effect of cellular growth on changes in the mechanical properties of the tubular structures. The observations demonstrated that the cells contained in the 3D printouts reach the ability to grow and spread in the polymer matrix after 4 weeks of cultivation, leading to obtaining a homogeneous, interconnected cell network inside the hydrogel after 6 weeks of incubation. Analysis of the mechanical properties of the printouts indicates that with the increasing time of cultivation of the structures, the degree of their overgrowth by the biological material contained inside, and the progressive degradation of the polymer matrix process, the tensile strength of tubular 3D printouts varies.  
3D bioprinting; tissue mimic structure; hydrogel bioink; cellular overgrowth; tubular printouts; long-term cell culture
## 2 Materials and Methods
### Pain Perception in Babies  
Traditionally, babies' pain has been undervalued, receiving limited attention due to the thought of babies suffering less pain than adults because of their supposed 'neurological immaturity' [32; 33]. This has been refuted through several studies over the last few years, especially by the one conducted by the John Radcliffe Hospital in Oxford in 2015 [34], which concluded that infants' brains react in a very similar way to adult brains when they are exposed to the same pain stimulus. Recent works suggest that infants' units in hospitals must adopt reliable pain assessment tools, since they may derive in short- and long-term sequels [35; 36].  
As mentioned before, the impossibility of expressing pain in a verbal way has created the need of using other media to assess pain, detect it, and take the appropriate actions. This is why pain assessment scales based on behavioral indicators has been created, such as PIPP (Premature Infant Pain Profile) [37], CRIES (Crying; Requires increased oxygen administration; Increased vital signs; Expression; Sleeplessness) [38], NIPS (Neonatal Infant Pain Scale) [39], or NFCS (Neonatal Facial Coding System) [40; 41]. While most assessment scales use vital signals such as heart rate or oxygen saturation, NFCS is based on facial changes through face muscles, mainly on forehead protrusion, contraction of eyelids, nasolabial groove, horizontal stretch of the mouth, and tense tongue [42]. Figure 1 shows a graphical example of the NFCS scale. As this paper uses an image database, this last scale is ideal to determine if the babies are or not in pain, by analyzing the facial changes in different areas according to the NFCS scale.
## 2 Modeling of Novel Electrical Load Resources in UDNs
### EV Charging Stations  
To facilitate the analysis of the regulatory potential of EV charging stations, this paper considers the entire charging station as a single entity. It assumes that electric buses' state of charge (SOC) is the same during their operating hours [25; 26]. When regulating a VPP,the primary method of load shifting is achieved by adjusting the charging power during different periods. The expression for this is given by the following:  
\[\left\{\begin{array}{l}P_{\text{EV}}^{\text{ch},t}=0\\ P_{\text{EV},t}^{\text{ch},t}\leq P_{\text{EV}}^{\text{ch},t}\leq P_{\text{EV}, \text{max}}^{\text{ch},t}\ \ \forall t\in t_{\text{dis}}\\ \end{array}\right. \tag{18}\]  
\[\left\{\begin{array}{l}\Lambda_{\text{EV}}^{t}=\Lambda_{\text{EV}}^{t-1}+ \eta_{\text{EV}}^{\text{ch}}P_{\text{EV}}^{\text{ch},t}\Delta t\\ 0\leq\Lambda_{\text{EV}}^{t}\leq\Lambda_{\text{EV},\text{max}}\\ \Lambda_{\text{EV}}^{t_{\text{ch},\text{end}}}\geq\Lambda_{\text{EV}}^{\text{ end}}\\ \end{array}\right. \tag{19}\]  
where \(P_{\text{EV}}^{\text{ch},t}\) is the charging power of the bus at time \(t\); \(P_{\text{EV},\text{min}}^{\text{ch},t}\) is the minimum power required at time \(t\) to prevent the vehicle from entering a dormant state; \(P_{\text{EV},\text{max}}^{\text{ch},t}\) is the maximum charging power at time \(t\); \(\eta_{\text{EV}}^{\text{ch}}\) is the charging efficiency; \(\Lambda_{\text{EV}}^{t}\) is the battery charge of the bus at time \(t\); \(\Lambda_{\text{EV},\text{max}}\) is the maximum energy storage capacity of the battery; \(t_{\text{dis}}\), \(t_{\text{ch}}\), and \(t_{\text{ch},\text{end}}\) are the bus operating period, night charging period, and charging end period, respectively; and \(\Lambda_{\text{EV}}^{\text{end}}\) is the minimum battery charge required at the end of charging.  
The regulation power of the EV charging station is given by the following:  
\[P_{\text{EV},\text{DR}}^{t}=P_{\text{EV},\text{ini}}^{\text{ch},t}-P_{\text{ EV}}^{\text{ch},t} \tag{20}\]  
where \(P_{\text{EV},\text{DR}}^{t}\) is the regulation power of the EV charging station at time \(t\), and \(P_{\text{EV},\text{ini}}^{\text{ch},t}\) is the charging power when the EV charging station is not participating in the regulation of the VPP at time \(t\).