esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The objective of this study is to compare the performance of different ultrasonic non-destructive testing (NDT) techniques for bonding quality evaluation. Aluminium-epoxy-aluminium single lap joints containing debonding in the form of release film inclusions have been investigated using three types of ultrasonic NDT methods: contact testing, immersion testing, and air-coupled testing. Apart from the traditional bulk wave ultrasound, guided wave testing was also performed using air coupled and contact transducers for the excitation of guided waves. Guided wave propagation within adhesive bond was numerically simulated. A wide range of inspection frequencies causing different ultrasonic wavelengths has been investigated. Average errors in defect sizing per ultrasonic wavelength have been used as a feature to determine the performance of each ultrasonic NDT technique. The best performance is observed with bulk wave investigations. Particularly, the higher frequencies (10-50 MHz) in the immersion testing performed significantly better than air-coupled testing (300 kHz); however, air coupled investigations have other advantages as contactless inspection. Whereas guided wave inspections show relatively lower accuracy in defect sizing, they are good enough to detect the presence of the debonding and enable to inspect long range. Even though each technique has its advantages and limitations, guided wave techniques can be practical for the preliminary in-situ inspection of adhesively bonded specimens.  
adhesive bonding; contact testing; immersion ultrasonic; air-coupled testing; air-coupled simulation; guided waves; defect sizing
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
###### Abstract  
With the developments in communication and mobile technologies, mobile users can access roaming services by utilizing a mobile device at any time and any place in the global mobility networks. However, these require several security requirements, such as authentication and anonymity, because the information is transmitted over an open channel. Thus, secure and efficient authentication protocols are essential to provide secure roaming services for legitimate users. In 2018, [PERSON] et al. presented a secure authentication protocol for global mobile networks. However, we demonstrated that their protocol could not prevent potential attacks, including masquerade, session key disclosure, and replay attacks. Thus, we proposed a secure and efficient three-factor authentication protocol to overcome the security weaknesses of [PERSON] et al.'s scheme. The proposed scheme was demonstrated to prevent various attacks and provided a secure mutual authentication by utilizing biometrics and secret parameters. We evaluated the security of the proposed protocol using informal security analysis and formal security analysis, such as the real-or-random (ROR) model and Burrows-Abadi-Needham (BAN) logic. In addition, we showed that our scheme withstands man-in-the-middle (MITM) and replay attacks utilizing formal security validation automated validation of internet security protocols and applications (AVISPA) simulation. Finally, we compared the performance of our protocol with existing schemes. Consequently, our scheme ensured better security and efficiency features than existing schemes and can be suitable for resource-constrained mobile environments.  
authentication; global mobility networks; roaming service; BAN logic; ROR model; AVISPA simulation +
Footnote †: journal: Applied Sciences
## 2 Microbial Processes of Anaerobic Digestion
### Methanogenesis  
Methane is produced as a metabolic byproduct in anoxic conditions by the methanogenic microorganisms Archaea. Methane production can occur following two different pathways:  
1. hydrogenotrophic methanogenesis, the most common metabolic pathway, where CO2 and H2 are transformed into methane;
2. the acetelastic methanogenesis, where acetate is directly converted to methane.  
Hydrogenotrophic methanogens play a key role in the overall process as Archea maintain a low partial pressure of H\({}_{2}\) (<10 Pa), necessary for the metabolic activity of aceticlastic methanogens and acetogens. Hydrogenotrophic methanogens, such as Methanospirillum hungatei, Methanoculles receptaculi, grow faster (doubling time of 6 h) than the aceticlastic methanogens, such as Methanosarcina thermophile, (doubling time of 2.6 days). As for pH value, the production of methane is affected by the low pH value. In order to support the growth of both acidifying and methanogenic bacteria, the pH value has to be above 6.6 and ideally 7-7.5 [10,11] as methanogens are very sensitive to environmental changes and prefer a slightly alkaline environment (at pH below 6 methanogenic bacteria cannot survive). Inhibitory compounds, such as sulphide, or lack of nutrients can negatively affect the growth and activity of these microorganisms, resulting in the accumulation of VFAs and a subsequent decrease in pH, which further inhibits the entire degradation process.  
Biogas contains approximately 55-65% methane, 30-45% carbon dioxide, traces of hydrogen sulfide and a percentage of water vapor. The composition of biogas and the methane yield depend on the feedstock type; fats provide the highest biogas yield but require a long retention time, carbohydrates and proteins show much faster conversion but a lower gas yield. The production of biogas from any substrate, as well as by pH, is highly affected by its carbon to nitrogen (C/N) ratio, and temperature [12]. Nitrogen represents an essential element for the synthesis of amino acids. It is converted to ammonia with buffering effects on the volatile acids produced by fermentative bacteria, thus preserving optimal pH conditions for the process. The C/N ratio should be well balanced for a correct process and to avoid the failure of anaerobic digestion [7; 13]. The C/N value should be in the range 15-30, with an optimal value of 25. If the C/N ratio is higher than 25, gas production will be low, whereas, if the C/N ratio is lower than 25, it will result in a pH value higher than 8.5 with toxic effects on methanogenic microbes [14]. A high C/N ratio can promote acid formation by inhibiting methane production, whereas at a low C/N ratio nitrogen is converted to ammonium at a faster rate than it can be assimilated by the methanogens [15], thus becoming toxic.
## 2 Materials and Methods
### Isometric EMG Measurement  
The methods of isometric EMG measurement have been described in a previous study [6]. Briefly, each participant lay prone (face down) on an examination bed with both ankles hanging over the edge [6]. The foot was fixed on the footplate, which was placed at a 90\({}^{\circ}\) angle to the tibia. A load cell (SLS410 Load Cell, Mettler-Toledo PacRim AG, Taiwan) connected to a footplate and an MP100 system (BIOPAC SystemSInc, Santa Barbara, CA, USA) and sampled at 1200 Hz was then used to record voluntary isometric torque. The myoelectrical activities of the gastrocnemius medialis, gastrocnemius lateralis, soleus, and tibialis anterior muscles were measured using wireless surface EMG recording electrodes (DTS, myoRESEARCHTM3.8.6, Noraxon, Scottsdale, AZ, USA) sampled at 1500 Hz. Each participant was instructed to gradually increase the plantarflexion and dorsiflexion forces in their foot from a relaxed status to the maximal voluntary isometric contraction (MVIC) and maintain the MVIC for 1 s. Visual feedback indicating the exerted plantar flexion and dorsiflexion forces was given to the participants in order to help ensure consistent performance. All measurements were averaged over at least three trials. For synchronization, software containing simulating switching circuits written  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Achilles Repair** \\ \hline Age (y) & 40.5 (19.5) \\ \hline Gender (\(n\)): Male/Female & 21/5 \\ \hline Repaired leg (\(n\)): Right/Left & 15/11 \\ \hline Body height (cm) & 173.6 (7.0) \\ \hline Post-surgery period (months) & 4.5 (5.15) \\ \hline Body weight (kg) & 74.8 (12.5) \\ \hline Injury mechanism (\(n\)): Sports/Non-sports injury & 24/2 \\ \hline LEFS score & 75 (6.5) \\ \hline \hline \end{tabular} Abbreviations: LEFS, Lower Extremity Functional Scale; NA, not available. Age, body weight, body height, and post-operation history and questionnaire scores are presented as median values, with the interquartile range in the parentheses.  
\end{table}
Table 1: Characteristics [reported as means (interquartile range)] and self-reported functional levels of the participants with an Achilles rupture (\(n=26\)).  
using LabVIEW7.1 (National Instruments, Austin, TX, USA) was used to add electrical signals to the EMG and MP100 system at the beginning and end of each measurement. The amplitudes of the root mean square (RMS) EMG during plantar flexion and dorsiflexion quantified for 1.0-s epochs and corresponding to the plateau levels of plantar flexion and dorsiflexion MVIC were used to normalize subsequent EMG data series gathered in the concentric and eccentric phases of the exercises [18,19].
## 3 The Complex Network of (p)ppGpp Signaling
### Regulation of Translation by (p)ppGpp  
In the stringent response, it is logical to assume that the shortage of amino acids would lead to the immediate and drastic reduction in protein synthesis. To this end, (p)ppGpp shuts off translation by directly binding to and inhibiting multiple components of the translation machinery [45; 53]. The bacterial translation initiation factor IF2, for instance, uses GTP in the formation of the 30S initiation complex; this is inhibited by high concentrations of ppGpp as the latter competes out GTP [89; 90; 91]. However, translation of selective mRNAs with distinctive hairpin structures can still occur, as, in these cases, IF2 is able to use ppGpp to initiate protein synthesis [91]. The mechanism of how the hairpins allow 30S assembly by IF2-ppGpp remains to be determined. (p)ppGpp also binds the elongation factors, EF-Tu and EF-G, and affects the translation rate in a complex manner that depends on its relative concentration and other translational factors [92]. Finally, (p)ppGpp inhibits a large number of known and putative GTPases, required for the biosynthesis and maturation of bacterial ribosomes, such as RsgA, RbgA, Era, HfIX, and Obg [93; 94].  
Experimentally determined crystal structures and docking by Molecular Dynamics simulation previously showed that (p)ppGpp indeed occupied the GTP-binding domain of these proteins [31; 95]. Of these short-listed proteins, the Obg protein of _E. coli_ (ObgE) has several important roles, including interactions with ppGpp and with the peptidyltransferase center, and maturation of the 50S ribosomal subunit [95]. As one of our goals was to study protein-ligand interactions in the stringent response by in silico analysis, as presented in the later sections, here, we tested selected docking programs for their conformity with the published 3D structure of the complexes, obtained from the structure databank (RCSB PDB). We used the CB-Dock2 program for this purpose [96], as described in the Supplementary material. Our independently predicted structure (Figure 3) obtained by the docking of ppGpp into the 3D structure of B. subtilis Obg protein, using this program, was also in close agreement with the previous ones [31], confirming that ppGpp docked very close to the active site.  
Figure 2: Structure similarity between DksA and GreA/B proteins. The coil-loop-coil domain of the proteins are shown, using the PyMol software [88]. The structures, fetched from the open-access RCSB PDB (Research Collaboratory for Structural Bioinformatics Protein Data Bank) at www.rcsb.org, are: 1 TJL (DksA), 1 GRJ (GreA), and 2P4V (GreB). The amino acid residues at the beginning and end of the domains are labeled; the helices are colored Red, whereas the loop regions and beta-strands are Green and Yellow, respectively.
## 4 Discussion
### Strengths and Limitations  
A major advantage of our meta-analysis is that it provides an up-to-date summary of differences in the effects of CSE and OSE on cognitive performance without limitations regarding the experimental design methods, age of subjects, and publication date. However, the conclusions of this meta-analysis should be considered regarding the following limitations. First, only 4 out of the 19 studies examined were intervention studies. Thus, our meta-analysis of the intervention studies was not sufficiently reliable, and the conclusions can only provide some references and suggestions for future research. Moreover, future studies need to cautiously consider causal inference. Second, the cross-sectional studies examined did not provide specifics regarding exercise time, frequency, and specific task performance for each of the participating genders. Therefore, the number of regulatory variables was not sufficient for further analysis, and this was not conducive for explorations of sources of heterogeneity. Third, while our moderator analysis indicated that OSE provided greater cognitive benefits to young adults, there have only been a few studies that investigated children participating in OSE. Therefore, it remains for future studies to confirm whether OSE is beneficial for children.  
Finally, we only used behavioral data to determine differences in cognitive performance between the two exercise modes. In future studies, it will be important to employ functional magnetic resonance imaging (fMRI) technology to explore functional and structural changes in the brain that affect cognition as a result of the two exercise modes. Key considerations will be which exercise mode is more helpful in promoting and updating current inference and which neural mechanism(s) underlie the changes observed. Additionally, our search strategy was limited to full-text studies published in English. Future studies should include all relevant literature, independent of language, to have a complete dataset that is not biased according to language.
## 3 Results
### Test of Combined Prediction Model  
The simulation parameters are set the same as Sections 3.1 and 3.2. The estimation result is calculated by Equation (9). The simulation results are shown in Figure 8. Compared to Figure 5, it is evident that the queue length estimation accuracy is developed. When the position of a connected vehicle is at the later part in the motorcade and the penetration rate is large enough, the shockwave-based queue length estimation model exhibits better accuracy because the information of connected vehicles imply the characteristics of queue forming process at the intersection. The well trained DNN model is capable of accurately estimating the queue length when the connected vehicle is in the front part of queue with a low penetration rate. Suffering from the stochastic arrival of vehicles and variable traffic environment, the simulation results reveal that the integrated queue estimation model takes the advantages of a shockwave-based model and camera-based model, which can be used as input to traffic signal control and transport monitor.  
Figure 7: Schematic diagram of model prediction results based on DNN.  
To give a full look of the performance of the integrated queue length model, the absolute error during the simulation is shown in Figure 9a, which shows the error to the actual queue length. The relative error represents the ratio of error to the actual queue length and is shown in Figure 9b.  
It reveals that the absolute error gets much bigger as the connected vehicles' penetration rate is less than 30%. The maximum absolute error value is about 10 m (almost two vehicles). However, the maximum relative error of queue length is less than 20%, and the average relative error is about 10%. In many cycles, the queue length estimation is near the actual value, which would be useful for  
Figure 8: Schematic diagram of combined model prediction results when penetration rate \(\rho=10\%\) (**a**), penetration rate \(\rho=30\%\) (**b**), penetration rate \(\rho=50\%\) (**c**) and penetration rate \(\rho=70\%\) (**d**).  
Figure 9: Error analysis of integrated queue length estimation model: absolute error (**a**) and relative error (**b**).  
traffic signal control. Therefore, we can conclude that this method is capable of taking advantage of the shockwave-based model and camera-based model.
## 4 Experimental Results
### Result and Discussion
#### 4.4.3 KNN Model  
The KNN model for regression was tested with various humidity levels, and Table 7 depicts the results. It was observed from Figure 9 that all the humidity level falls at certain 0.013 then it starts to deviate.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**100\% Humidity**} \\ \hline
**RMSE** & 0.0060 & 0.0052 & 0.0036 & 0.0292 & 0.0496 \\ \hline \(\mathbf{R}^{2}\) & 0.8489 & 0.8917 & 0.9716 & 0.0035 & 0.3107 \\ \hline \multicolumn{5}{c}{**50\% Humidity**} \\ \hline
**RMSE** & 0.0018 & 0.0009 & 0.0044 & 0.0134 & 0.0155 \\ \hline \(\mathbf{R}^{2}\) & 0.962 & 0.9762 & 0.8851 & 0.591 & 0.7851 \\ \hline \multicolumn{5}{c}{**10\% Humidity**} \\ \hline
**RMSE** & 0.0039 & 0.0029 & 0.0047 & 0.0173 & 0.0172 \\ \hline \(\mathbf{R}^{2}\) & 0.9467 & 0.9563 & 0.9261 & 0.5919 & 0.0172 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Various humidity comparison of LR model.  
Figure 8: LR model with various humidity levels.  
The study on comparative analysis of various humidity levels using various machine learning model was analyzed, and the results obtained from the various models show that 100% humidification works well when compared to other levels, as listed in Table 8. The results observed in Figure 10 show that LR methods perform well when compared to other models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **100\% Humidity** & \\ \hline
**RMSE** & 0.0034 & 0.0051 & 0.027 & 0.0178 & 0.0462 \\ \hline \(R^{2}\) & 0.9354 & 0.9281 & 0.8431 & 0.6681 & 0.5879 \\ \hline \multicolumn{5}{c}{**50\% Humidity**} \\ \hline
**RMSE** & 0.0001 & 0.0005 & 0.0139 & 0.0121 & 0.0106 \\ \hline \(R^{2}\) & 0.9982 & 0.9923 & 0.7843 & 0.7698 & 0.9031 \\ \hline \multicolumn{5}{c}{**10\% Humidity**} \\ \hline
**RMSE** & 0.0003 & 0.0006 & 0.0135 & 0.0162 & 0.0123 \\ \hline \(R^{2}\) & 0.9963 & 0.9914 & 0.7876 & 0.6186 & 0.8789 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of KNN method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **SVMR** & **LR** & **KNN** \\ \hline
**RMSE** & 0.0046 & 0.0034 & 0.004 \\ \hline \(R^{2}\) & 0.9343 & 0.9354 & 0.9444 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of various models.  
Figure 9: KNN model with various humidity levels.
## 2 Fluorocarbon Nucleoside Amphiphiles (FNs)
### Hybrid Fluorocarbon Nucleoside Amphiphiles  
The combination of the self-assembly potential of nucleoside amphiphiles with the hydrophobic character of highly fluorinated chains is of high relevance to prepare new self-assembled structures with tunable physico-chemical properties and functions. To assess whether fluorinated chains can enhance the scope of supramolecular assemblies formed by hybrid fluorocarbon nucleoside amphiphiles, different molecules were synthesized (Figure 4).  
The fluorocarbon amphiphile **1** is based on a uridine backbone modified by a phosphocholine moiety at the 5' position, and two _2H,2H,3H,3H_-perfluoroundecanoyl side chains at the 2' and 3' positions. One of the goals of the study was to characterize the properties of such new compounds and to compare those results with that observed for the analogous hydrocarbon nucleaomphiphiles. In aqueous solution, fluorocarbon nucleaomphiphiles were surprisingly able to form large vesicular assemblies and unexpected lamellar phases below their \(T_{m}\), contrary to hydrocarbon analogs that self-assemble into helical structures below their \(T_{m}\) and bilayers above it [54]. These highly hydrophobic fluorocarbon amphiphiles exhibited interesting hybrid microspherical supramolecular  
Figure 3: Examples of nucleoside amphiphiles; (**a**) diC\({}_{8}\)PA [37]; (**b**) a gemini nucleotide [39]; and (**c**) a zwitterionic nucleolipid.  
assemblies in the presence of thorium that were characterized by Scanning Electron Microscopy (SEM) [55].  
Recently, two glycosyl-nucleoside fluorinated amphiphiles (GNFs) were designed to construct new supramolecular hydrogels. Molecules **2** and **3** feature both sugar moieties attached to a thymidine base and a _2H,2H,3H,3H_-perfluoroundecanoyl side chain at the 5' position, linked via triazole bridges resulting from \"click\" chemistry. The self-assembly and gelation properties of such molecules were investigated. Similarly to their hydrocarbon analogs [56,57] GNFs stabilize hydrogels at low concentrations (0.1% w/w for compound **2**). GNFs gelators displayed supramolecular networks of varying morphologies, such as nanofibers and helical springs, which are correlated to the presence of fluorocarbon chains. Moreover, fluorocarbon amphiphile GNF **2** proved to be not toxic for human cells (Huh7) whereas hydrocarbon analogue becomes toxic above 100 \(\upmu\)M, demonstrating the greater potential of such fluorocarbon nucleoside amphiphiles for biomedical applications [58].
## 2 Materials and Methods
### Estimation Model
#### 2.5.3 Support Vector Regression  
SVR is an extension of the support vector machine (SVM) [37] and is used for numerical prediction. The SVM is a supervised classification system that can find the maximum margin hyperplane separating two types of data. The training instance closest to this hyperplane is called the support vector. SVR with a radial basis function (RBF) core was used for comparison with other models. The SVR function is expressed as follows:  
\[y(x)=\sum_{i=1}^{2}\alpha_{i}K\big{(}x_{i},x_{j}\big{)}+b, \tag{6}\]  
where \(\alpha_{i}\) denotes the Lagrange multipliers obtained in the minimization process, \(b\) is the bias, and \(K\big{(}x_{i},x_{j}\big{)}\) is the RBF kernel function. The form of this function is  
\[K\big{(}x_{i},x_{j}\big{)}=exp\Big{(}-\gamma\big{(}x_{i}-x_{j}\big{)}^{2}\Big{)}. \tag{7}\]  
In this study, the scikit-learn library in Python was used to build an SVR model. The GridSearchCV function of sklearn was applied to the training data using a grid search, and five-fold cross-validation was used to adjust the hyperparameters \(\gamma=0.1\), \(\varepsilon(-1\)\(\sim\)1), and C(1\(\sim\)10).
## 6 Conclusions and Future Work  
This paper presents a service-orientated architecture for proactive geospatial information services (PGIS), integrating the sensor, data, processing, and human services. PGIS is able to organize, aggregate, and co-operate services by composing small scale services into service chains to meet complicated user requirements. It is a flexible, reusable, and scalable system to share and interoperate geospatial data and information extensively. The developed PGIS framework has been implemented and preliminary experiments have been performed to verify its performance. The results show that the important properties of PGIS including interoperability, flexibility, and reusability can be achieved.  
The actual problem of service-oriented geospatial applications is much more complicated than the testbed shows. Further detailed research and developments will be performed in order to achieve a PGIS solution valid for actual complicated geospatial applications in different contexts and domains. Future efforts also include the development and evaluation of techniques for creation of workflows considering special constraints related to particular geospatial information services. Exploiting human input's role in the PGIS architecture will be emphasized. Investigation of the use of specialized tools to reduce the processing time is also of interest.