esa-sceva/satcom-chunk-collection huggingface dataset:
# SAM-PAY: A Location-Based Authentication Method for Mobile Environments +
## 5 Service Exploiting the SAM-PAY Method for Mobile Authentication
### SAM-PAY Based Refueling Service at the Self-Service Gas Stations
The SP is either a trusted third party that communicates with the PPS or with a bank for payment operations management (as shown in Figure 5), or it can even be part of the PPS or the banking system. The proposed SAM-PAY-based service is composed of two phases: the user registration phase and the (service) operational phase. In the registration phase, both the gas-station provider and the clients provide data to the SP to obtain access to the SAM-PAY-enabled refueling service at the self-service gas-station. In particular, the client needs to create a profile holding the authentication credentials (username and password) required to log into  
Figure 5: Architecture and workflow of the SAM-PAY-based refueling service at self-service gas stations. the service portal, the contact (name, surname, address), fiscal data, the unique identifier (i.e., the IMEI code) of the UT device, the bank coordinates required for the payment operations, and the consent for being localized during service provision. Moreover, the client also sets the symmetric keys \(KS_{a}\) and \(KS_{e}\) used for authentication and encryption in the modified LDEA algorithm. The operational phase of the proposed refueling service is composed of 11 steps, shown in Figure 5, consisting of interactions among the main components. First, the user needs to unlock his UT by inserting an 8-character secret PIN. We note that this functionality is available for almost any cellular mobile phone, though more modern phones allow local unlocking of the phone via biometric data or by swiping on a \(3\times 3\) grid of contactable points. Subsequently, the user exploits the UT to retrieve from the SP the data (TOKEN) required for the payment operation. To complete the above operation, the user must have registered at the SP for this and they need to be located in the proximity of the gas pump. Next, the UT asks for a TOKEN from the SP to be used for the payment operation. The SP checks first whether the UT has been declared lost or stolen (and thus, it cannot be used to complete the transaction). If this is not the case, it queries the LE to obtain the certified position (expressed in terms of \(X_{U}\), \(Y_{U}\), \(Z_{U}\) coordinates) of the UT at a given time t1. Next, the SP issues a TOKEN with the modified LDEA algorithm (illustrated in Figure 5), and that has a limited time validity, e.g., 5-10 min. From the TOKEN, the UT can recover the OTC data, which can be used for payment purposes (Step 5). Finally, the SP verifies the OTC's correctness (in Step 7) and unlocks the gas pump if the validation of the OTC has been completed successfully (Step 9).
# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 8 Conclusions and Future Directions  
This paper proposes a bio-inspired optimisation algorithm model for selecting energy-efficient cluster heads and the optimal routing protocol. The proposed model aims to increase network lifetime and reduce energy consumption, which are vital parameters in an IoT network during a disaster. Effective fitness functions for CH selection and routing are used to enhance the model's performance. The analysis considered different scenarios by placing the BS in different locations. The proposed model was compared and evaluated against benchmark models that use CH-based routing protocols for IoT networks. The performance metrics used to evaluate the proposed model included residual energy, network throughput, packet delivery, and network lifetime. The experimental results showed that the proposed model significantly enhanced residual energy by more than 10% in both scenarios across all the considered protocols and achieved 60% higher throughput than the LEACH model, 53% higher throughput than the DEEC model, and 37% higher throughput than the PSO protocol. In addition, network lifetime increased by 10-20% compared to the LEACH, DEEC, PSO, and hybrid PSO models.  
In the future, we will consider more comprehensive scenarios when extending our simulation models. It is also necessary to validate the proposed model against a broader range of bio-inspired models. To calculate energy consumption for cluster formation and CH selection, we will extend our model with more realistic propagation models, such as the Hata model, extended Hata model, Hata-Okumura model, and Ericson model, in rural, suburban, and urban areas. Our future research will address the challenges posed by rapid and unpredictable changes in network topology during disaster scenarios. Therefore, developing algorithms that can dynamically adapt to node mobility and ensure continuous communication is essential. In addition, we will explore specialised clustering algorithms for heterogeneous networks and investigate the application of machine learning and AI to adaptive clustering to improve network performance in disaster scenarios. In this research, we simulated the proposed method in a small area. In our future work, we will expand the areas and incorporate higher sensor density and complex propagation models.  
Conceptualization, S.A.; Methodology, M.A.H.; Software, S.A.; Formal analysis, S.A. and M.A.H.; Writing--original draft, S.A. and M.A.H.; Writing--review & editing, P.H.J.C. and S.K.R.; Supervision, P.H.J.C. and S.K.R.; Project administration, P.H.J.C. All authors have read and agreed to the published version of this manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data are contained within the article.  
Figure 13: Percentage of improved network lifetime for Scenario 2.
## 4 Discussions
### Physical Performance  
As hypothesized, obesity influenced physical fitness in young handball players. School-age handball players were stratified based on age-specific values of %BF as obese or non-obese. Between groups, BMI (\(\eta_{\text{p}}\)\({}^{2}\) = 0.243) was less different than %BF (\(\eta_{\text{p}}\)\({}^{2}\) = 0.766). Such a finding implies BMI is a poor surrogate for obesity (which is purportedly a disease of excessive fatness, although not if measured by BMI) in adolescent athletes [49; 50].  
The difference in sprint times between groups ranged from 8% (30 m sprint) to 9% (15 m sprint). This has implications for player success as players sprint for \(\sim\)10-30 m in game-defining epochs [51]. Recently, it was reported that normal weight schoolchildren players showed greater performance in jumping, but not for sprinting [33], and our data corroborate these conclusions.  
Concerning MBT, the difference between groups was 5%. Since muscle strength and power are imperative for ball throwing velocity, the MBT is an ecologically valid test to measure upper limb power in team handball players [51]. Greater upper limb power and strength ae advantageous for obese players in this regard [33]. However, we postulate that the greater upper limb strength and power in the obese players does not compensate for the disadvantages in other physical tests observed herein.  
For CMJ, differences between obese and non-obese groups existed at the \(p<0.01\) level within this study. The difference in jumping between groups ranged from 13% (SJ) to 18% (CMJ). These effects likely occurred because of having less body mass to accelerate against gravity, and we therefore suggest relative power is imperative in team handball. As handball is a contact sport, whereby hitting, blocking, jumping, and pushing are commonplace, muscle strength and power are needed to compete [49; 50].  
Agility is well-described as an important attribute in most invasion games, and handball requires the same [49; 50]. COD differences were present between groups herein, but differences in agility may become amplified on further growth in our participants as peak rate of agility development often occurs after 15 years of age [52; 53; 54; 55; 56].  
Figure 3: Relationship between (**a**) agility T-half test and mathematics and (**b**) agility T-half test and science. Please note that one dot can represent several subjects.  
In terms of Yo-Yo IR 1 performance, the obese group were 17% worse than the nonobese group. Recently, it was reported that the difference between normal weight and overweight schoolchildren athletes in Yo-Yo IR 1 was 27% [49]. In our study, surreally, Yo-Yo IR1 distance was not different between non-obese (824 \(\pm\) 293 m) and obese athletes (686 \(\pm\) 225 m) at the \(p<0.05\) level. Aerobic fitness is an important constituent in handball [35].
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
## 4 Experimental Result
### Performance of Multi-Core DDFS  
According to the analysis of the performance and resource consumption of proposed DDFS in the previous subsection, we choose the DDFS with MAE = 0.00001 to implement the 16-core DDFS, which increases the output signal frequency while maintaining high SFDR. The sampling rate of single-core DDFS is 244 MHz, so the sampling rate of 16-core DDFS can achieve 3.9 GHz. Figure 10 shows the spectrum of the output signal of multi-core DDFS with FCW = 1800, the output signal frequency is about 1.7 GHz, and the SFDR can reach 114.04 dB.
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 4 Experimental Results and Analyses
### BDS-3+QZO Constellation  
The QZSS with a service area covering the Asia-Pacific region is an augmentation system for GPS and Galileo [31]. The QZO satellites of QZSS are similar to the IGSO satellites of BDS-3. They all follow a figure-8-shape track on the spherical orbit surface in the ECEF system, but QZO satellite's ground track is not symmetric for the northern and southern hemisphere. This is because the eccentricity of QZO is 0.075 and therefore satellites with a higher elevation angle can be more easily observed in urban areas of Japan, which can improve the geometry distribution in the complex urban environment [32]. The specific parameters of QZO are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Constellation**} & \multicolumn{5}{c}{**Average (Min-Max)**} \\  & **VisiNum** & **HDOP** & **PDOP** & **TDOP** & **VDOP** \\ \hline BDS-3 in augmentation area & 13.20 (11.27–14.86) & 0.73 (0.66–0.80) & 1.46 (1.31–1.61) & 0.67 (0.56–0.74) & 1.08 (0.98–1.21) \\ BDS-3 on global scale & 10.48 (7.69–14.86) & 0.78 (0.66–0.93) & 1.68 (1.31–1.98) & 0.73 (0.54–0.84) & 1.29 (0.98–1.63) \\ GPS on global scale & 10.99 (10.8–11.75) & 0.75 (0.68–0.81) & 1.61 (1.51–1.86) & 0.69 (0.64–0.78) & 1.24 (1.12–1.54) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average, minimal, and maximal visible satellites and DOPs of BDS-3 and GPS.  
We replace 3 IGSO satellites of BDS-3 by 3 QZO satellites to investigate the differences between IGSO and QZO in terms of satellite visibility and GDOP. The inclination is set as 55\({}^{\circ}\) and the center of longitude is modified as 118\({}^{\circ}\) E which is the same as of BDS-3 IGSO satellites. The ground track of QZO satellites is shown in Figure 2. The values of the BDS-3+QZO constellation parameters are shown in Table 1.  
As revealed by Figure 5, after replacing 3 IGSO satellites, the performance in high latitude regions of the Northern Hemisphere is slightly improved but in some regions is slightly worse compared to BDS-3. This is because QZO is in an elliptical orbit and its apogee is in the Northern Hemisphere leading to QZO satellites spend about 2.3 h longer in the North Hemisphere.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Reconfigurable Intelligent Surfaces
#### 3.2.7 RIS Evaluation  
The evaluation of RIS systems demonstrates their ability for transformation. We can easily understand that active and dual-band RIS can solve many issues in future 6G communications and beyond, as we can see their performance metrics such as SNR, capacity gain, and energy efficiency in active RIS (refs. [61; 62]), as well as precise beam steering and seamless functionality in different frequency bands in DBI-RIS. However, the management of self-interference in active RIS and the independence of sub-6 GHz and mmWaves in dual-band RIS, in order to balance the complexity in large systems, are still issues that we need to overcome (refs. [62; 63]).  
Before closing this section, it is important to note that the RIS devices share common characteristics with the Holographic Matasurface radiator type studied next. One notable example is an advanced MIMO architecture relying on stacked intelligent metasurfaces (SIM, ref. [70]). The device has been developed, with removed digital precoding and combining a reduced number of RF chains. Furthermore, SIM can be utilized to generate OAM.
## 5 Results and Discussion
### Validation of the Methodology  
Due to the requirement for manual validation at each location, these tests could not be performed simultaneously with a single receiver and therefore the number of satellites changed during the tests. The satellite visibility calculation was repeatedly updated to coincide with each recorded checkpoint. Table 1 lists the terminology used in the validation and analysis.  
Upon comparing the maximum, the predicted and the observed number of satellites visible at each location, the promising results appear to validate the methodology. Table 2 displays these results. The largest discrepancy between \(Predicted_{\text{obs}}\) and \(Measured_{\text{obs}}\) was an over-prediction of two satellites at Point 3. There is an avenue of large trees lining the walled garden to the North West of this test location and another avenue of large trees lining the University boundary to the East. The lack of vegetation in the model has caused errors in this prediction instance. At six of the ten test-sites (Points 4, 5, 6, 7, 8 and 10) the \(Predicted_{\text{obs}}\) value was identical to the validation, \(Measured_{\text{obs}}\), _i.e._, the proposed method has returned a zero error for 60% of the tests. The results from Points 5, 7 and 10 are particularly promising as they were all located in close proximity to a tall building and therefore experienced severely restricted GNSS visibility.  
Table 3 lists the error in prediction and the azimuth of the two dominant sources of shadow for each point, the range to those obstructions and the height of each obstruction (also displayed graphically in Figure 10b). Each obstruction is also identified in Table 3. The correct number of satellites at 5, 7 and 10  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Term** & **Definition** \\ \hline \(Total_{\text{sat}}\) & Number of satellites predicted at the receiver location assuming no obstructions. \\ \(Predicted_{\text{obs}}\) & Number of satellites predicted once obstructions have been included. \\ \(Measured_{\text{obs}}\) & Number of satellites observed once obstructions have been included. \\ \(Error\) & Discrepancy between \(Predicted_{\text{obs}}\) and \(Measured_{\text{obs}}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global navigation satellite system (GNSS) prediction validation test terminology.  
was calculated when the range to the building varied between 2 m and 5.6 m and also calculated at Point 8, which was positioned at a junction between large buildings. Accurately predicting GNSS visibility in problematic areas such as these implies that our approach of modelling obstructions and predictions using existing software, 2D polygons and low-density elevation information was successful.
## 5 Result Analysis
### Comparison of Fusion Methods
The syntactic structure of Chinese is mainly expressed based on the position and order of words, while English pays more attention to the expression of grammatical relationships, such as the relationship between subject and object, and the relationship between subordinate clauses and main clauses. Therefore, adding syntactic information to English translation can better capture the syntactic structure of the source language and help improve the translation accuracy and fluency. Compared with the baseline systems in Table 1, it can be seen that integrating syntactic information into neural machine translation can indeed improve the translation accuracy \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**EN–ZH**} & \multicolumn{2}{c}{**ZH–EN**} \\  & **BLEU** & \(\mathbf{\Delta}\) & **BLEU** & \(\mathbf{\Delta}\) \\ \hline Transformer [27] & 34.54 & - & 29.23 & - \\ +CLIP & 37.91 & \(\uparrow\) 3.37 & 32.41 & \(\uparrow\) 3.18 \\ CAF & 35.85 & \(\uparrow\) 1.31 & 30.26 & \(\uparrow\) 1.03 \\ \hline \hline \end{tabular} Where: \(\mathbf{\Delta}\) is the BLEU score improved by the +CLIP model and the CAF model compared to the transformer model. \(\uparrow\) represents “increase”.  
\end{table} 
Table 1: Experimental results of +CLIP and CAF models in EN–ZH and ZH–EN. of the model, which also demonstrates the effectiveness of the two approaches proposed in this paper for incorporating syntactic information into neural machine translation models.
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 3 A Novel Object Stochastic Modeling Method
### Geological Modeling Steps  
The new geological modeling methodology comprises facies modeling (using unit element theory), structural modeling, and property modeling. These three modeling procedures are executed sequentially within the workflow, detailed below as follows:  
1. Facies Modeling:  
Modelers must first determine the depositional environment of the target reservoir. Well data and seismic data are imported and used as constraints for the modeling process. Sand bodies, treated as unit elements, are then stochastically distributed within the 3D space, constrained by the imported well and seismic data. The specific sand bodies used are selected from a database based on the identified depositional environments. It is important to note that the initial facies modeling assumes that the geological layers are flat. Subsequently, the layer depths and the sand body positions are deformed to match the actual depth data.
2. Structural Modeling:  
Structural modeling encompasses the depth variation in the different layers and the modeling of faults and fractures. The depth model for each layer is constructed based on seismic and/or well data. The fault and fracture models are derived from seismic data or \"soft data\" interpreted from various sources.  
Property Modeling:  
This step involves creating a grid with a resolution in each dimension determined by both the computational capacity available and the characteristic size of each facies. The model includes porosity and permeability. Porosity data, obtained from well logs or experimental measurements, are classified according to the facies type. Porosity values are then stochastically assigned to each grid cell within a specific facies type. Finally, porosity-permeability relationships are established for each facies, and the permeability for each grid cell is calculated based on these relationships.
## 2 Materials and Methods
### Methyl Methacrylate Monomer-Infiltration  
The methyl methacrylate monomer-infiltration into the Al\({}_{2}\)O\({}_{3}\) preforms was accomplished with the aid of a homemade system. It was composed of a stainless steel vacuum chamber (diameter = 170 mm, height = 230 mm and thickness = 6 mm), iron magnet (150 mm \(\times\) 60 mm \(\times\) 15 mm; mass = 60 g), a lid of the stainless steel vacuum chamber having a place for one iron magnet, a borosilicate beaker and a pressure system, see Figure 2a-b. The infiltration procedure was divided into three steps. In the first step, the samples were fixed in the chamber slid with an iron magnet. The vacuum chamber was then sealed using an o-ring fixed to a metal structure on a triangular tripod with an adjustable spindle. For the second step, firstly the system was kept under vacuum (750 mmHg) for 30 min. Such a procedure was accomplished to eliminate gases present in the samples. The iron magnet was removed in the third step, and the samples were dropped into a beaker containing methyl methacrylate monomer and benzoyl peroxide initiator in 1:1 (vol/vol) proportion. The Al\({}_{2}\)O\({}_{3}\) preforms were kept in the beaker for 6 and 12 h under pressure equal to 7 bar. In the last step, the monomer-infiltrated Al\({}_{2}\)O\({}_{3}\) samples were put in a hermetically sealed glass bottle and polymerization occurred in an oven at 60 \({}^{\circ}\)C. Table 1 summarizes the name of the Al\({}_{2}\)O\({}_{3}\) preforms prepared with their respective infiltration times and estimated amount of PMMA (vol.%) infiltration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Suppliers** & \
\begin{tabular}{c} **Specimens** \\ **Conditions** \\ \end{tabular} & **Time (h)** &
\begin{tabular}{c} **Estimated Amount** \\ **of PMMA (vol.\%)** \\ \end{tabular} \\ \hline Alglass & Without infiltration & 0 & 0 \\ In-ceram & Without infiltration & 0 & 0 \\ Vitro-ceram & Without infiltration & 0 & 0 \\ Alglass & With infiltration & 6 & 13 \\ In-ceram & With infiltration & 6 & 12 \\ Vitro-ceram & With infiltration & 6 & 9 \\ Alglass & With infiltration & 12 & 16 \\ In-ceram & With infiltration & 12 & 20 \\ Vitro-ceram & With infiltration & 12 & \(>\)9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of conditions for preparation of samples, infiltration time and the estimated amount of PMMA (vol.%) infiltration.  
Figure 1: Al\({}_{2}\)O\({}_{3}\) commercial powders used in this research (Vitro-ceram, Alglass and In-ceram).
## 5 Experimental Verification
### Experimental Results
#### 5.3.2 SMC Comparison Experiments  
Comparing ILC-ASMC with SMC and ASMC, the experimental results are shown in Figure 7. The reference tracking results show that after adding the adaptive law, the control performance of ASMC is greatly improved compared with the traditional SMC, and the tracking accuracy is increased by 41.49% and 37.47%, respectively. The better tracking effect of ILC-ASMC than ASMC is due to the increased learning gain of iterative learning. As shown in Table 1, the tracking effect of ILC-ASMC is 83.40% and 75.44% higher than that of ASMC, respectively. From this point of view, SMC effectively handles system disturbances, and periodic disturbances during repeated movements are suppressed through ILC. The experimental results verify the effectiveness of the proposed control scheme in terms of tracking and anti-disturbance performance.