esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Results and Discussion
### Analysis of Proposed Scheme Results against Existing State of the Art
#### 7.2.5 End-To-End Delay  
In Figure 12, the end-to-end delay is consistently because of more number of nodes participate in communication when node density increases. Highest delay is experienced by AUV-CH due to data gathering from every node in its communication range, and the delay is 2.5 s at 500 node number. This delay is occurring because of high traffic load that results in to more number of transmissions. Whereas, EnOR has higher delay due to opportunistic forwarding in which time consumed at assigning priorities to each node in the forwarder set for avoiding immutable selection of each node towards the destination. This incorporates more delay in EnOR, however, lower than AUV-CH. While, delay in RE-PBR is less than all schemes throughout the network lifetime except GDGOR-IA. The reason of less delay than other schemes is, the selection of high quality link which mitigates the chances of retransmissions.  
Whereas, GDGOR-IA bears the same delay as GEDAR in Figure 12. However, GDGOR-IA opts void hole avoidance mechanism along with interference avoidance in order to avoid communication voids and data loss. This incurs more delay due to several number of hops taken to by pass void holes. In GRMC-SM scheme, number of hops taken to deliver data to sinks is less while compared with other schemes because of mobile sonobuoys involvement for data gathering from acoustic nodes directly. Thus, reduced end to end delay is experienced in GRMC-SM and GDGOR-SM as shown in Figure 12. Performance analysis of GEDAR against proposed technique is given in Table 3.  
Figure 11: Energy consumption comparative plots.
## 3 Results
### Current Status, Challenges and Future Developments
#### 3.2.1 Renewable Hydrogen Production
Overall, while GIS remains the cornerstone of geospatial analysis in renewable hydrogen production, the convergence with other technologies such as remote sensing, IoT, and machine learning offers substantial opportunities for improving precision, responsiveness, and scalability in planning and operation.
# A Review of the Vibration Arthography Technique Applied to the Knee Diagnostics
## 3 Signal Analysis
### Signal Processing
#### 3.2.3 Time-Frequency Domain Analysis  
As VAG signals are non-stationary signals, the Fourier Transform is not adequate. Indeed, it only characterizes the frequency content of a given signal but it is not possible to detect signal variation characteristics over time [2]. Therefore, time-frequency techniques are often employed. These techniques enable the study of a signal by considering the time and frequency domains simultaneously. The most common time-frequency analysis techniques used for VAG signals are Short Time Fourier Transform, Wigner Ville Distribution, and Wavelet Transform (WT).  
Short-Time Fourier Transform (STFT) can be defined as Fourier Transform over a fixed window that slides over time. [PERSON] et al. [17] have used STFT to compare three types of patellofemoral joint pathologies, which are chondormalacia, lateral patellar compression syndrome, and osteoarthritis based on their vibroacoustic signals. However,  
Figure 5: The time domain waveforms and their respective spectrograms from [17]. Here, the different patellofemoral diseases were investigated: (**A**) healthy knee; (**B**) knee with lateral patellar compression syndrome; (**C**) knee with condro-malacia; and (**D**) knee with patellofemoral joint arthritis.  
even though STFT can overcome some drawbacks of Fourier Transform, it cannot give a good resolution in both time and frequency.  
The Wigner-Ville distribution has been used by [PERSON] et al. [16] to transform their VAG signal into a time-frequency distribution and thus extract four parameters--namely, the energy parameter (EP), energy spread parameter (ESP), frequency parameter (FP), and frequency spread parameter (FSP).  
Lastly, Wavelet Transform [33] is a time-frequency analysis technique based on the dilation and translation of a mother wavelet over time to characterize an entire signal. The main difference with Fourier Transform is that the WT breaks down the signal into wavelets instead of sine waves. Moreover, due to the scale and the translation parameters, the decomposition gives a better resolution than STFT, which has a fixed window sliding over time. Previous studies have employed the Daubechies wavelets, as they most resemble the VAG signals [2; 4; 24]. An illustration in Figure 6 shows some of these wavelets along with measured waveforms. [PERSON] et al. [34] have executed dynamic time warping on the wavelet coefficients of signals from healthy patients and patients with a history of ligament injuries. They have concluded that those coefficients can represent a useful indicator of the degree of ligament stabilization during recovery [34].
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 4 Simulation Results  
In this section, we provide the BER performance of different SCMA detectors and different quantized bits under various signal-to-noise ratios (SNRs). Both the additive white Gaussian noise (AWGN) and Rayleigh channels are investigated. In our simulations, two factor graphs are considered, and the corresponding index matrices, \(\mathbf{F}_{4\times 6}\) and \(\mathbf{F}_{5\times 10}\), are given in (4) and (5), respectively. The channel gain \(\mathbf{h}_{j}\) was modeled as an independently and identically distributed circularly symmetric complex-valued Gaussian random vector with zero mean and covariance \(\sigma^{2}\mathbf{I}\).
## 3 Technical Features and Socio-Legal Characteristics of Africa's Satellite Constellation
### Satellite Evidence of Relics and Self Determination
National Space agencies such as the NASRDA and the Algeria Space Agency [99] are particularly suited for the task of developing African states competence in this area. In Nigeria for instance, the power to act proactively is located in the function of its space agency to \"develop national strategies for the exploitation of the outer space and make these part of the overall national development strategies and implement strategies for promoting private sector participation in the space industry.\"  
The Department of Strategic Space Applications and Department of International Cooperation as identified empowered in S. 8 (2) of the NASRDA Act could play a more prominent role in studying and articulating Nigeria's capabilities in the area of identification of relict boundaries and in adopting a more systematic approach to their detection and interpretation. For those African states with no legislation or national bodies dedicated to space technology their capabilities to act independently is obviously limited. However, given the history of loss of Africa's archaeological heritage into the hands of the erstwhile colonial powers, south-south cooperation in respect of heritage research ought to be one of the central planks of cooperation within platforms such as CRTEAN and APRSAF.
## 4 Non-Selfadjoint Operator Pencil Generated by the Harvester System
\tag{155}\]Due to Lemma 2, when \(\lambda\) belongs to the strip parallel to the real axis, we have \(\big{|}e(\lambda)\big{|}\asymp 1\) and \(\big{|}\widehat{e}(\lambda)\big{|}\asymp 1\); therefore, the asymptotical form of the spectral equation becomes  
\[\Big{[}d_{11}(\lambda)\big{(}e(\lambda)\big{)}^{-2}-1\Big{]}\Big{[}d_{22}( \lambda)\big{(}\widehat{e}(\lambda)\big{)}^{-2}+1\Big{]}=d_{21}(\lambda)\big{(} e(\lambda)\widehat{e}(\lambda)\big{)}^{-1}O\big{(}\lambda^{-1}\big{)}. \tag{156}\]  
Using Equations (117) for \(e(\lambda)\) and \(\widehat{e}(\lambda)\), and Equations (147) for \(d_{11}(\lambda)\) and \(d_{22}(\lambda)\), we can readily check that Equation (156) splits up into the following two equations:  
\[\exp\Bigg{[}2i\lambda\int_{0}^{L}\sqrt{\frac{\rho(\tau)}{K(\tau)}}d\tau\Bigg{]} =\frac{\alpha-\sqrt{\rho(L)K(L)}}{\alpha+\sqrt{\rho(L)K(L)}}+O\Bigg{(}\frac{1} {\lambda}\Bigg{)}. \tag{157}\]  
\[\exp\Bigg{(}2i\lambda\int_{0}^{L}\sqrt{\frac{S\rho(\tau)}{EI(\tau)}}d\tau \Bigg{)}=-1+O(1/\lambda). \tag{158}\]  
Each equation has a countable set of solutions. The solutions of Equation (157) we call the \(\alpha\)-spectral branch and the solutions of Equation (158) we call the \(\theta\)-spectral branch. In the statement below (Theorem 5), we provide the formulation of the results on the asymptotical distribution of the roots of Equations (157) and (158). We do not present the proof of the theorem since very detailed proofs of similar results can be found in [PERSON] [2,3], [PERSON] and Shubov [4], [PERSON] and Peterson [41], and [PERSON] [25,38]. **Theorem 5**. : _(1) The following asymptotic approximation is valid for the \(\alpha\)-branch of the spectrum:_  
\[ \begin{split}&\lambda_{n}^{\alpha}=\Bigg{(}\int_{0}^{L}\sqrt{ \frac{\rho(\tau)}{K(\tau)}}d\tau\Bigg{)}^{-1}\pi\ n+i\Bigg{(}2\int_{0}^{L} \sqrt{\frac{\rho(\tau)}{K(\tau)}}d\tau\Bigg{)}^{-1}\ln\Bigg{(}\frac{\alpha+ \sqrt{\rho(L)K(L)}}{\alpha-\sqrt{\rho(L)K(L)}}\Bigg{)}+O\Bigg{(}\frac{1}{n} \Bigg{)},\\ &\ n\to\infty,\ \lambda_{-n}^{\alpha}=-\overline{\lambda_{n}^{ \alpha}}.\end{split}  \tag{159}\]  
_(2) The following asymptotic approximation is valid for the \(\theta\)-branch of the spectrum:_  
\[\lambda_{m}^{\theta}=\pi\bigg{(}m+\frac{1}{2}\Bigg{)}\Bigg{(}\int_{0}^{L} \sqrt{\frac{S\rho(\tau)}{EI(\tau)}}d\tau\Bigg{)}^{-1}+O\Bigg{(}\frac{1}{m} \Bigg{)},\qquad m\to\infty,\qquad\lambda_{-m}^{\theta}=-\overline{\lambda_{m}^ {\theta}}. \tag{160}\]  
**Conclusion. ** If the harvester model is based on the cantilever beam as a substructure, then the leading asymptotical terms are not affected by the piezoelectric parameters. For the Timoshenko beam, this conclusion follows from Equations (159) and (160). For the Euler-Bernoulli beam, it follows from the asymptotic formulas derived in Shubov [1, 2, 3] and [PERSON] and Shubov [4].
# Speckle Noise Reduction in Digital Holography Using a DMD and Multi-Hologram Resampling
## 1 Introduction
The combination of numerical and optical techniques has been attempted in the past under different methodologies [17; 19], albeit with more complex processing algorithms. We evaluated our technique with different illumination phase patterns and demonstrate that the reduction in the speckle noise is significant.
## 4 Results
### Calculate the Desorption Rate Ratio with Particle Distribution  
Assuming that prior to the occurrence of coal and gas outbursts, the coal was composed of centimeter-sized coal particles, under the influence of factors such as geological stress, coal and gas outbursts occurred, causing the coal to break into particles of different sizes. Additionally, the coal particles are also broken due to collisions with each other during the outburst. For instance, the coal sample with a particle size of 1-2 mm and a mass of 50 g was ground and crushed using a ball mill at 150 r/min and 250 r/min [53]. The mass of the coal sample after sieving can be seen in Table 2.  
Figure 7: Relationship between the gas desorption rate ratio and the coal particle breakage ratio (**a**) Effective diffusion coefficient from Yang; (**b**) Effective diffusion coefficient from Guo.  
After outbursts occurred in the Zhongliangshan Coal Mine, the outburst coal samples were sieved. The sieving results of coal samples after two outbursts are provided in Table 3 [54].  
The coal particles were ground and crushed using a ball mill, and the crushed coal was then sieved for particle size analysis. The mass distribution of the sieved particles was fitted using Equation (22), and the fitting results are depicted in Figure 8. The fitting parameters are recorded in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Distribution Index of Granularity \(\mathbf{\alpha}\)** & **Coefficient of Determination** \\ \hline Cai-1 & 1.7647 & 0.9980 \\ Cai-2 & 0.5359 & 0.9788 \\ Zhong-1 & 0.4121 & 0.9644 \\ Zhong-2 & 0.3834 & 0.9722 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of coal crushing.  
Figure 8: Cumulative ratio under sieve. (**a**) Data from Cai; (**b**) Data from Hu.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Diameter/mm** & \multicolumn{3}{c}{**Proportion/\%**} \\ \cline{2-3}  & **Zhong-1** & **Zhong-2** \\ \hline \(<\)0.1 & 4.3 & 6.6 \\
0.1–1 & 29.9 & 27.5 \\
1–5 & 21.6 & 16.9 \\
5–10 & 14.1 & 18.2 \\ \(>\)10 & 27.1 & 30.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sieving results of coal samples in outburst coal seams.  
Figure 8 reveals that the particle size distribution after coal crushing conforms to the GGS model. The finer the coal was broken, the smaller the distribution index of granularity \(\alpha\).  
By substituting the data from Tables 1 and 4 into Equation (27), the desorption rate ratio before and after crushing were calculated. The relationship between the desorption rate ratio and the distribution index of granularity \(\alpha\) is shown in Figure 9.  
Figure 9 indicates that the desorption rate ratio increases with a decrease in the distribution index of granularity \(\alpha\) when the index of desorption \(\gamma\) is constant. With the increase in the index of desorption \(\gamma\), the ratio of desorption rate after and before coal crushing increases.
## 2 Materials and Methods
### Wavelength Selection and Gas Mixing  
In order to successfully implement the MPC-PAS system, it is essential to carefully select the laser emission wavelength to match the absorption characteristics of ammonia in nitrogen. It is fundamental to be aware of the spectral characteristics of the gas to be detected, as well as potential interference from other gases. In this instance, the emphasis is on the near-infrared spectral region corresponding to 1532 nm. For this purpose, the HITRAN (high-resolution transmission molecular absorption database) spectroscopic database has been used. At this wavelength, no significant interference from other common gases such as H\({}_{2}\)O, CO\({}_{2}\), CH\({}_{4}\), or CO is detected, even at high concentrations.. This has been corroborated by experimental validation, which identified the main absorption lines in the 1532 nm range, as illustrated in Figure 3.  
This spectrum was simulated using the HITRAN database and measured by laser scanning. The different concentrations were generated from two gas cylinders: one containing pure N\({}_{2}\) as the carrier gas and the other containing NH\({}_{3}\) at a certified concentration of 5000 ppm in N\({}_{2}\). Both gas flows were regulated by two mass flow controllers (F-201 CV-AGD-22-K, Bronkhorst, Ruurlo, Netherlands), which were also used to perform dilutions, while the total flow was kept constant [34]. It should be noted that NH\({}_{3}\) is a common gas that easily adheres to surfaces and pipes due to its physical and chemical properties. It has been observed that NH\({}_{3}\) can also exhibit capillary condensation in pipes or porous materials, where gas molecules condense in small pores or capillaries at pressures below the bulk saturation pressure. This effect is especially pronounced in tight spaces or when the temperature is low. For this reason, a system based on materials that prevent this effect, such as Teflon (polytetrafluoroethylene or PTFE) and gold coating, were used. Ammonia, a polar gas, exhibits minimal interaction with the non-polar surface of Teflon, thereby ensuring minimal adsorption and accumulation of ammonia on surfaces, a crucial consideration in this application. Constant gas replenishment prevented cumulative heating within the  
Figure 2: Multipass cell. (**a**) Cell on custom-designed mounting for easy installation. (**b**) Diagram of the reflection polygon due to multiple reflections inside the cell.  
cell, while the gas mixture was fluxed through the gas cell by means of a valve system, a pressure controller, and a rotary pump.
## 2 Materials and Methods
### Model Evaluation Index  
In this experiment, the modeling effect is evaluated by the coefficient of determination (\(R^{2}\)), the root mean square error (RMSE), and the residual predictive deviation (\(RPD\)). The coefficient of determination \(R^{2}\) reflects the level of intimacy between variables, the RMSE reflects the accuracy of the model, and \(RPD\) reflects the predictive ability of the model. The higher the \(RPD\), the lower the RMSE, and the closer the \(R^{2}\) is to 1, the better the performance of the prediction model. In this paper, \(R_{c}^{2}\) and \(R_{p}^{2}\) represent the coefficient of the determination of the calibration set and the prediction set respectively, while RMSE\({}_{c}\) and RMSE\({}_{p}\) represent the root mean square error of the calibration set and the prediction set respectively. In addition, the \(RPD\) was suggested to be at least three for agriculture applications; while 2 < \(RPD\) <3 indicated a model with a good predictive ability; 1.4 < \(RPD\) < 2 was an intermediate model needing some improvement; and \(RPD\) < 1.4 indicated that the model had a poor predictive ability [28].
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 3 Modeling of the Stem Cement in Femur  
### Interface Model  
The mechanical behavior of the bonded interface could be studied from the tensile and shear tests. As shown in Figure 3, the cement-metal interface under tensile loading exhibits a linear relationship between apparent stress and displacement after the initial loading stage. Within the linear region, the surface traction (or apparent stress) \(T_{i}\) can be related to the relative displacement \(\delta_{i}\) of the bonded surfaces by the following constitutive equation:  
\[\begin{array}{ll}T_{n}=K_{in}\cdot\delta_{n}&T_{n}\leq\sigma_{u}\;,\\ T_{s}=K_{is}\cdot\delta_{s}&T_{s}\leq\tau_{u}\;.\end{array} \tag{1}\]  
In the above equation, \(K_{in}\) and \(K_{is}\) are the stiffness values calibrated in the normal and tangential directions to the interface, respectively, which also represent the ability of the interface to resist the relative deformation of the bonded materials.  
For development of the finite element model of a cemented stem, the spring layer model used in fracturing analysis of bimaterial adhesive joint structure [31; 32] was employed to simulate the mechanical characteristics of the cement-stem interface. The interdigitated region between the bone cement and the stem was considered to be the adhesive layer, which was replaced by spring elements distributed over this layer (Figure 4). Each spring element connecting the node pairs at the cement and stem element was characterized based on the stiffness and strength along the normal or tangential directions of the interfaces, respectively.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Extract** & \multicolumn{2}{c}{**IC\({}_{50}\) (mg/mL)**} \\ \hline  & **Aqueous** & **Methanolic** \\ \hline Wheat & 3.4 & 9.0 \\ Rice & 4.6 & 10.0 \\ Rye & 4.2 & 9.6 \\ Quinoa & 5.4 & 4.9 \\ Amaranth & 2.8 & 5.8 \\ Buckwheat & 0.3 & 9.4 \\ Chia & 9.6 & 11.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MGO-trapping capacity of different grain and seed extracts. Figure 3: MGO-trapping capacity of extracts from (**A**) cereals (wheat, rice, and rye), (**B**) pseudocoreals (quinoa, amaranth, and buckwheat) and seeds (chia) at 168 h. Results are expressed as mean \(\pm\) SD (n = 4). BSA-Glc system since the conversion of glucose to the open-chain form is necessary, as well as its autoxidation to generate reactive dicarbonyl compounds. In parallel, some natural pure compounds exert their antiglycative activity by binding to protein residues through hydrogen bonds or van der Waals forces, and effectively protecting the structural integrity of the proteins and inhibiting glycation [4]. MGO reacts with BSA via the arginine and lysine residues to generate AGEs. Then, the coexistence of antiglycative and hypoglyative compounds in the extracts is plausible. The presence of high levels of reducing sugars other than glucose or reactive dicarbonyl compounds could boost the reaction and participate in the glycation process as promoters [4]. Previous research has emphasized that antioxidant compounds are potent inhibitors of the formation of AGEs through the scavenging of oxidative free radicals and chelating of metal ions [6,28]. In the present study, the antioxidant capacities of the extracts were evaluated using FRAP (electron donating ability) and ABTS (free radical-scavenging capacity) assays. The results showed significant variations, ranging from 29.7 to 282.9 umol TEAC/g for the FRAP method and from 113.1 to 684.2 umol TEAC/g for the ABTS method (Table 3). With the exception of rye and amaranth, methanolic extracts exhibited higher electron-donating capacity, with buckwheat having the highest activity (282.9 umol TEAC/g) and amaranth the lowest (41.1 umol TEAC/g). In aqueous extracts, buckwheat again showed the highest results (165.9 umol TEAC/g), while wheat had the lowest activity (29.7 umol TEAC/g). Similar to the FRAP results, buckwheat displayed the highest free radical-scavenging activity in the ABTS assay, while amaranth exhibited the lowest activity in both aqueous and methanolic extracts. Once more, methanolic extracts showcased superior antioxidant capacity compared with aqueous extracts, except in the case of rice and amaranth. Significant correlations were found between FRAP and ABTS (r = 0.7549, \(p\) < 0.0001). Polyphenols are natural compounds extensively described in the literature that potentially inhibit the formation of AGEs [4]. Phenolic compounds and flavonoids were also examined for their role in antiglycative activity (Table 3). TPC varied widely, from
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 4 Results
### PI (\(P_{SQI}\)) Analysis  
As shown in Table 6, the \(P_{SQI}\) reaches its maximum mean value of 3.07 in the CF range of 0.2-0.4, indicating the best perfusion and signal quality. The high mean and median values and the variability suggest an optimal coupling between the sensor and the skin. As the CF increases to moderate and high levels (0.4-0.6 to 0.8-1.0), there is a general decrease in the mean and median PI. Specifically, the range of 0.8-1.0 shows a notable drop in the mean PI to 0.66 and a median of 0.53, with the minimum falling dramatically to 0.20 and the maximum to 2.21, evidencing excessive compression.