esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computer Simulation Results
### Experimental Setup for the Residual Dense Graph-U-Net  
In order to evaluate the extent to which the proposed methodology works under different acoustic environments, the proposed approach is subject to different room environments where the room dimensions are different from those of the original environment. The room dimen  
Figure 6: Plot of estimated (x-, y-) coordinates by MS-GLMB filter across all time frames at reverberation time RT60 = 550 ms and acoustic SNR = 35 dB. The colored tracks in the form of straight lines indicate the original i.e., ground truth trajectories corresponding to 3 speech sources, whereas the colored tracks with dots indicate estimates of the source trajectories obtained by the MS-GLMB filter.  
sions considered for evaluating the proposed approach under different acoustic conditions are chosen as 5.80 m by 3.30 m by 2.00 m, 7.50 m by 3.50 m by 2.80 m and 10.50 m by 6.50 m by 3.40 m. The rooms corresponding to these three different room dimensions are designated as 'Test Room I', 'Test Room II', and 'Test Room III', respectively. Table 4 presents the overall summary of experimental conditions, while Table 5 depicts the test rooms' dimensions. Table 6 presents the positions of microphone elements within each test room.  
involved in that research work involved training and testing the Dense-U-Net architecture using three test rooms as well as a six-element linear microphone array whose positions were varied in the three rooms to effectively train the neural architecture to predict the locations of the sources with high accuracy in the presence of acoustic impairments like reverberation as well as sources' motion. Motivated by the results presented in [45], the work presented in this article also utilizes the same test room dimensions and microphone array positions as in [45] for testing the proposed speech enhancement approach.  
In the test environments, apart from varying the microphone sensors' positions, the SNR is varied such that the selected acoustic SNRs fall in the set [0 dB, 10 dB, 20 dB, 30 dB, 40 dB, 50 dB, 60 dB]. To test the proposed approach, an SNR value is randomly selected from the above set of SNRs, whereby the test room dimensions and positions of the microphone array sensors are also randomly selected from Tables 5 and 6 respectively.
## 3 Protocols Verifying the Authenticity of Digital Files in Distributed Networks
### Example of Use Case in Distributed Networks
#### 3.2.2 Verification in Distributed Networks  
When the verification of the authenticity of \(F_{i}\) is required, the client communicates \(\omega_{i}\) to the agent. The agent then coordinates the effort between the smart contract and the storage node, executed in the protocol shown in Figure 5, which is summarized as follows:  
* The smart contract, which is equipped with the CRP mechanism collects \(\omega_{i}\), \(C_{i}\), and \(M^{\circ}_{i}=\{S_{i}\), \(M^{\circ}_{i}\}\)
* The CRP mechanism has the information needed to decrypt \(F_{i}\) and run the DSA verification with \(PK_{i}\).  
Figure 4: Block diagram of the enrollment cycle with CRP mechanism. The resulting data distributed to the agent is \(C_{i}\), the cipher text of \(F_{i}\), and \(M^{\circ}_{i}\): \(\{S_{i}\), \(M^{\circ}_{i}\}\), the encrypted information needed for verification of authenticity. The client keeps \(F_{i}\) and nonce \(\omega_{i}\).  
A variation of the protocol replaces \(\text{Pk}_{\text{i}}\) by a message of authenticity \(\text{M}_{\text{i}}\) such as:  
_\"Yes, I (client X), am confirming the authenticity of ciphertext \(\text{C}_{i}\)\"_.  
This variation can be useful if the client desires to protect file \(\text{F}_{\text{i}}\). The decryption of \(\text{C}_{\text{i}}\) can be performed separately, while outside the open network. A separate arrangement is then needed to share \(\text{Pk}_{\text{i}}\); for example, through a public key infrastructure (PKI).
## 5 Results and Discussions
### Ablation Experiments  
The prediction results of our ST-RANet model for the outbound passenger flow of a station in the SubwayBJ dataset are shown in Figure 10.  
In order to assess the impact of the CBAM module and external module of holiday and meteorological data on the model's performance, we conducted ablation experiments. We compared our ST-RANet with ST-ResNet without the CBAM module, ST-RANetExt without the external module and ST-ResNetExt without both CBAM and external modules. The metrics obtained from the ablation experiments are presented in Table 4.  
It can be seen that firstly, with regard to the CBAM attention mechanism, our ST-RANet model has improved the prediction accuracy in every aspect compared to ST-ResNet. Secondly, in the ablation experiment of the external module, both ST-RANet and ST-ResNet models have shown significant improvement in prediction accuracy, indicating that holidays and meteorological factors contribute to the prediction of rail transit passenger flow.
## 4 Simulations and Results Analysis
### Comparison of SDSE to Other Proposals  
In terms of accuracy, we do not compare our proposed SDSE scheme with the mentioned schemes proposed for WSNs [5; 6; 7; 8], because such schemes do not validate the metrics that they propose. However,  
Figure 11: Maximum number of hops to the base station, for different network sizes.  
Figure 12: Accuracy of the estimated security level, for differently sized networks.  
in order to give a notion of how high the accuracies achieved by SDSE are, we compare our scheme with two of the main security level estimation schemes proposed for traditional networks [1,2], since they do validate their proposed metrics.  
The work of [PERSON] _et al._[1] presents metrics with accuracies between 78% and 83%, while the scheme proposed by [PERSON] _et al._[2] presents accuracies that vary between 55% and 100%. Regarding SDSE, as we have seen in the above subsections, its accuracies remained between about 90% and 100%.  
From these values, we can see that SDSE presents accuracies similar to (or even better than) those of traditional networks' proposals.
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 2 Preliminary Knowledge  
### K-Means Clustering Algorithm  
K-means clustering is an unsupervised learning algorithm [24], and it is used for offline learning prior to self-organized learning. This algorithm randomly generates \(q\) cluster centers in the initial state and then calculates the distance from each point in the data to the \(q\) centroids using the Euclidean distance in Equation (1).  
\[dist(x_{i},c_{j})=\sqrt{\sum\limits_{t=1}^{k}{{{(x_{it}-c_{it})}^{2}}}},i=1,2, \ldots,n,j=1,2,\ldots q, \tag{1}\]  
where \(x=\left\{{{x_{1}},{x_{2}},{x_{3}},{x_{n}}}\right\}\) is the total of \(n\) offline training data samples, and \(c=\left\{{{c_{1}},{c_{2}},{c_{3}},\ldots,{c_{q}}}\right\}\) is \(q\) randomly generated clustering centers.  
Assign each sample to the cluster with the smallest Euclidean distance from the center and, when all points have been assigned, recalculate the center of that cluster according to Equation (2),  
\[C_{l}=\frac{\sum\limits_{{{X_{i}}\in{S_{l}}}}{{{X_{i}}}}}{{{|{S_{l}}|}}},i=1,2,\ldots,|{S_{l}}|;l=1,2,\ldots,q, \tag{2}\]  
where \(C_{l}\) is the updated cluster center, \(q\) is the number of clusters, \(|S_{l}|\) is the number of objects in the \(l\)th cluster, and \(X_{i}\) is the \(i\)th sample in the cluster.  
Then, the samples are reassigned, and the cluster centroids are updated until the specified number of iterations is reached. Finally, \(q\) cluster centers are obtained and set as the initial centers of the fuzzy neural network.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 2 Methodology
### Review of Some Basic Principles of Electromagnetism  
#### 2.1.1 Principle of Equivalence and Image Theory  
The principle of equivalence [9] allows us to compute the solutions in the region of interest (half-space in front of the conducting plane) by replacing the plane conductor with the images of the dipoles. The image sources must satisfy the boundary condition that the tangential electric field at the conducting surface should be zero. The total electric field above the conducting plane (Figure 2) is then computed as the sum of the one created by the original currents and the image ones, while the total electric field below the conducting plane is zero. Applying this principle to our problem, the transmitter can be replaced by its \"image\" underneath the plane, as illustrated in Figure 3.
# Factorial Design Analysis for Localization Algorithms
## 4 Results
### Anchor Nodes
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Levels**} & \multicolumn{8}{c}{**MSE (m)**} & \multicolumn{4}{c}{**CDF (\%)**} \\ \hline
**A** & **B** & **C** & **D** & **CL** & **REWL** & **Hyperbolic** & **WLS** & **CL** & **REWL** & **Hyperbolic** & **WLS** \\ \hline \(-1\) & \(-1\) & \(-1\) & \(-1\) & 69.76 & 57.39 & 78.1 & 57.91 & 5.829 & 8.2119 & 9.7503 & 11.31 \\ \hline \(-1\) & \(-1\) & \(-1\) & 1 & 127 & 87.85 & 104.45 & 73.04 & 5.663 & 7.5301 & 4.388 & 9.444 \\ \hline \(-1\) & \(-1\) & 1 & \(-1\) & 19.23 & 15.91 & 22.94 & 15.94 & 17.68 & 25.452 & 19.241 & 28.15 \\ \hline \(-1\) & \(-1\) & 1 & 1 & 26.52 & 18.97 & 28.56 & 16.34 & 15.56 & 22.738 & 11.722 & 27.26 \\ \hline \(-1\) & \(1\) & \(-1\) & \(-1\) & 265.6 & 255.06 & 372.4 & 390.34 & 2.554 & 3.9807 & 1.8664 & 3.096 \\ \hline \(-1\) & \(1\) & \(-1\) & 1 & 293.6 & 251.69 & 361.98 & 315.37 & 1.208 & 2.7236 & 0.2898 & 1.247 \\ \hline \(-1\) & \(1\) & 1 & \(-1\) & 170.2 & 160.64 & 325.19 & 320.49 & 4.41 & 5.6936 & 4.2078 & 5.828 \\ \hline \(-1\) & \(1\) & 1 & 1 & 183.2 & 154.97 & 322.31 & 255.76 & 2.296 & 3.5721 & 0.2872 & 3.82 \\ \hline \(1\) & \(-1\) & \(-1\) & \(-1\) & 58.3 & 44.28 & 20.77 & 24.28 & 6.254 & 7.8482 & 23.726 & 25.25 \\ \hline \(1\) & \(-1\) & \(-1\) & 1 & 108.2 & 43.61 & 16.63 & 10.19 & 6.29 & 9.2447 & 23.604 & 48.6 \\ \hline \(1\) & \(-1\) & \(1\) & \(-1\) & 16.33 & 13.15 & 5.89 & 6.04 & 22.61 & 31.812 & 84.193 & 78.08 \\ \hline \(1\) & \(-1\) & \(1\) & 1 & 22.86 & 12.42 & 4.49 & 2.94 & 18.31 & 35.898 & 98.97 & 100 \\ \hline \(1\) & \(1\) & \(-1\) & \(-1\) & 86.24 & 66.16 & 135.69 & 97.77 & 5.506 & 8.1518 & 8.5659 & 8.559 \\ \hline \(1\) & \(1\) & \(-1\) & 1 & 154.9 & 66.26 & 186.81 & 134.98 & 4.385 & 8.6823 & 1.8456 & 5.577 \\ \hline \(1\) & \(1\) & \(1\) & \(-1\) & 24.62 & 19.63 & 43.14 & 33.24 & 13.53 & 19.913 & 12.608 & 16.63 \\ \hline \(1\) & \(1\) & \(1\) & 1 & 32.65 & 18.18 & 62.36 & 30.68 & 12.34 & 22.398 & 6.3494 & 14.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MSE and CDF of the localization techniques. The MSE and the CDF are affected to a great extent by the noise level \((\sigma_{dB})\), which shows a strong impact on range-based localization techniques. * The path-loss exponent \(\eta\) has a greater impact on the MSE and the CDF of the range-based algorithms than on those of the range-free algorithms. * The combination of factors A and B shows greater impact on the MSE of the localization algorithms and very little on their CDF. * Node density \((\rho)\) has a greater impact on the MSE and the CDF of the range-free algorithms than on those of the range-based algorithms. * Node density \((\rho)\) is the factor that have the greatest impact on the localization algorithms' CDF. * The path-loss exponent and the noise level are the factors that have the greatest impact on the localization algorithms' MSE. * The number of RNs has zero impact on the range-based algorithms and shows very little impact on the CL algorithm's MSE.
## 2 Materials and Methods
### Laboratory Analyses  
Soil samples were air-dried, ground, and passed through a 2 mm sieve. Soil organic carbon (SOC) was determined by a standard Tuyrin dichromate oxidation method [16], and total nitrogen (TN) was determined by the standard Kjeldahl procedure [17]. To determine the pH values, we used the traditional method of analysis of aqueous extract at a ratio of soil:water of 1:5 (pH/ORP/Temp tester Milwaukee Mil106 (Milwaukee Electronics  
Figure 2: Examples of soil morphology of Calcisols (Serozems) of the northern Fergana. (**a**) Dark Calcisol, (**b**) Typical Calcisol, (**c**) Light Calcisol.  
(USA)) The particle-size distribution of the soils was measured by the sedimentation (pipette) method [18]. Also, the classical [18] methods of soil bulk density, porosity rate, and internal surface were used for a soil physic survey. Soil carbonates were determined gravimetrically [17] after treatment of the sample with hydrochloric acid. Potassium content was determined by flame photometry. The phosphorous content was determined with use of photometry. Exchangeable forms of Ca and Mg were determined by titration trilonometry [17]. Exchangeable phosphorous and potassium within the Olsen extract were detected with the use of molybdenum ammonium and flame photometry, correspondingly. Carbon of humic acids (CHA) and carbon of fulvic acids (CFA) were determined by the Tuyrin dichromate oxidation method [16] in extracts, prepared according to [19].  
Determination of amino acids composition is described as follows. From the averaged crushed homogeneous sample of soil fine earth and dry extract for analysis, an accurate sample was weighed into test tubes with an error of no more than 0.1%. Next, free amino acids were isolated [20]. Precipitation of proteins and peptides from the aqueous extract (1:10) of the samples was carried out in centrifuge beakers. To do this, 1 mL (exact volume) of 20% trichloroacetic acid was added to the test samples (1:1). After 10 min, the precipitate was separated by centrifugation at 8000 rpm for 15 min. After separating, 0.1 mL of the supernatant fluid was lyophilized dried. Phenylthiocarbamyl (PTC) derivatives of amino acids were obtained by reaction with phenylthioisocyanate according to the method of [PERSON], [PERSON] [21]. Identification of PTC amino acids was carried out on an Agilent Technologies 1200 (California, USA) chromatograph using a Discovery HS C18 column, 75 \(\times\) 4.6 mm with a diode-array detector. Solution A: a mixture of 0.14 M sodium acetate and 0.05% triethylamine, pH 6.4. Solution B: acetonitrile. The flow rate was 1.2 mL/min; absorption was 269 nm. Gradient %B/min: 1-6%/0-2.5 min; 6-30%/2.51-40 min; 30-60%/40.1-45 min; 60-60%/45.1-50 min; 60-0%/50.1-55 min. Qualitative analysis and quantitative calculation of the concentration of the studied free amino acids were carried out by comparing the retention time and peak areas of the standard and studied PTC derivatives of amino acids [20].  
Statistical processing and data visualization was performed using GraphPad Prizm 9.0.0, Golden Software Grapher 13.0 and Microsoft Office Excel 2016 16.0.
## 5 Discussion
### Limitations of the Study
#### 5.2.4 Process Optimization and Trade-Offs  
The trade-offs between process efficiency and surface quality are another limitation that warrants attention. In this study, we prioritized maintaining a small rolling offset (0.1 mm) to achieve uniform stress distribution, but this approach increased the number of tool passes, potentially extending the processing time. While this trade-off was deemed necessary to ensure uniform deformation, [PERSON] et al. (2019) suggested that larger offsets could achieve satisfactory results in shorter time frames, though they might compromise surface quality or introduce stress concentration zones. Additionally, [PERSON] et al. (2019) found that higher rolling speeds, though improving processing efficiency, often led to inconsistent stress distribution across the material surface. Our results similarly indicate that the chosen rolling speed of 1800 mm/min led to minor fluctuations in the pressure application, which, while not significantly affecting the overall results, could be further optimized to enhance process consistency.  
The limitations in both the experimental setup and FEA simulations reflect common challenges faced in studies of mechanical surface treatments like deep rolling. The simplifications made in the FEA model, the restrictions of the \(\cos\alpha\) technique in measuring deeper residual stresses, and slight variations in experimental control contribute to the observed discrepancies between simulation and experimental results. However, these limitations also provide avenues for improvement in future studies, such as incorporating more detailed material models, using complementary stress measurement techniques, and optimizing process parameters for a balance between efficiency and stress uniformity. By addressing these limitations, future research can build on our findings and further refine the deep rolling process for enhanced material performance.
## 4 Functional Description of D-MSR Algorithms in Different Protocol Layers  
In this section, we first illustrate the mechanisms used to select the advertisement cell and to construct schedule-matrices in the lower data link sub-layer. Next, we discuss the mechanism that is used to define the initial communication links with neighbors in the upper data link sub-layer. Finally, we explain the D-SAR protocol used to establish an end-to-end connection and to reserve the communication resources in the upper data link sub-layer.  
Figure 7: Point-to-point traffic model in RPL.
# Temperature Prediction Model in the Main Ventilation System of an Underground Mine
## 3 Methods
### Data Processing and Analysis
An analysis of the real data suggested a strong linear relationship among the effective temperatures in the nodes, especially in the case of adjacent nodes, as can be seen in Figure 3.  
Figure 2: Representation of the system modeled. Here, \(t_{n}\) is the temperature, whether measured or estimated (\({}^{\circ}\)C), \(c_{n}\) is the airflow measured (m\({}^{3}\)/s), x\({}_{n}\) is the relative position of the control points or nodes (m), and n\({}_{i}\) is the node number.
## 3 Experiments and Results
### Experimental Results
#### 3.4.2 Comparison of Different Algorithms
(**b**) Means of SSIM values of different algorithms. Figure 9: _Cont._  
Figure 9 are the images reconstructed with different algorithms. Among these five methods, the super-resolution image reconstructed by SPLINE interpolation is the most fuzzy and the edge preserving effect is the worst. SRCNN and ESPCN algorithm have good reconstruction effect and rich detail information, but the edge reconstruction effect is still not ideal. The MPSR algorithm proposed in this paper has obvious advantages in detail information enhancement and edge preservation, and can effectively reduce noise and improve the overall brightness of the image, and the subjective visual effect is better than other algorithms.
# The Role of Renewable Energy Sources and Industry 4.0 Focus for Africa: A Review
## 3 Renewable Energy
### Tidal Generation  
Tidal generation uses the ocean tides resulting from the Moon's gravitational pull on the Earth. Although tidal energy is not widely implemented, it can generate enormous power. It is more predictable than solar and wind energy [72]. It is 80% more efficient than solar and wind energy. The requirement for tidal energy is a tide of about 7 m for proper operation and a good head of water to propel the turbine. Three types of tidal generation technology are currently in use, as represented in Figure 9 [73].  
As tidal turbines and tidal fences are either in demonstration phases or not in operation, only tidal barrages will be considered for this section. Tidal barrages operate on a similar mechanism to a standard hydropower station. It uses a tidal basin to capture seawater flowing in during high tides and releases the water through a turbine to generate electricity on low tides. This concept is easily duplicated to create electricity during both high and low tides. Some installations in Eastern Europe and Asia exceed 200 MW in generation capacity per installation, with a few other facilities in the same region of smaller capacity. A quick search on the Internet indicates that no generation capacity of this type seems evident in Africa.  
A disadvantage of these types of technologies is their effect on localized ocean fauna and flora, with the technology causing increased turbidity and potentially affecting passing currents [74]. Furthermore, an oceanic operating environment would be severe to any materials placed therein, with special coatings and materials required for the equipment not to experience accelerated degradation due to corrosion.  
The locality would also be an issue, as access to the ocean is paramount to the operation of this technology. This means that land-locked countries may not be able to benefit from installations of this type. Even though tides are a continuous, naturally occurring phenomenon induced by a cosmic force, there is almost no limit to the energy drawn using these mechanisms, noting that each installation will have spatial constraints limiting capacity. Tidal energy can contribute about 10 GW of electricity to South Africa. This is because about 50 MW/km of energy is produced from the coastal waves of South Africa [75]. The entire coast is capable of supplying about 57,000 MW of power is harnessed.
## 5 Related Work
### Simulation  
Simulation approaches mainly focus on defining and simulating models of architectures (hardware platform), operating systems and applications. AVRORA [29] provides a cycle-accurate model of the MICA mote family and has been used to evaluate the power consumption of MICA and MICA2 hardware platforms.  
While some simulations are related to a specific mote or mote family, [PERSON] [30] presents a simulation framework to estimate power consumption of WSN applications for arbitrary hardware platforms. This framework describes the possible states of each component (e.g., memory and radio) separately and each state has the energy consumption and performance associated. A set of these components represents a particular hardware platform.  
The need of both evaluating new operating systems and testing real-code applications before their deployment motivated the appearance of operating system emulation environments [31]. A well-known example is the TOSSIM [32] simulator for TinyOS. Its main propose is to execute TinyOS applications without their deployment in actual WSN nodes. TOSSIM ignores some hardware behaviours like interruptions of the CPU.  
Unlike the aforementioned approaches that simulate directly the programming code, existing researches also concentrate on defining behavioural models of WSNs. [PERSON] and [PERSON] [33] use Deterministic and Stochastic Petri Nets (DSPNs) to model the behaviour of IMote2's CPU and radio. Each place represents a state of the processor or the radio. The number of tokens present in each placeat the end of simulation represents the time spent (in percentage terms) in that state. To find out the consumption of each state, it multiplies the amount of tokens by the average power consumption of the respective state. NS-2 [34] and OPNET [35] are well-known simulators that may be used to evaluate the power consumption of WSNs.  
Most of these applications are strongly coupled to a particular hardware platform, which makes difficult the adoption of the same approach for different WSN motes. In particular, our solution facilitates the evaluation of new platforms, as the presented models are generic and only need to be configured to a particular mote. Another advantage of the proposed approach is the generation of power consumption results from a programming language code in an automatic way.