esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Visual Predictive Check  
We evaluate the robustness of the proposed model and potential misspecifications in order to understand how the proposed model can interpret and correctly describe the observed process, i.e., providing accurate parameter predictions and avoiding inaccurate estimates. Many of the statistical methods used to assess the suitability of the model are based on graphical representation and they comprise 'goodness-of-fit inspection methods'. These statistical techniques are simple to implement, but they can give misleading results when applied to non-linear mixed-effects models. Here, we choose to perform a visual predictive check (VPC), which allows us to evaluate the ability of the proposed model to reproduce both the central trend and the variability in the observed time-varying data [28]. VPC bases its analysis on performing several simulations, which are used to design the structure of the observed data. The first step consists of evaluating the empirical percentiles from the observed data in each temporal bin, allowing for a summary of their distribution. Then, the theoretical percentiles are computed from data generated by multiple Monte-Carlo simulations, and at the same temporal bins as the empirical ones. From these simulated replicas of the observed data, it is possible to generate non-parametric confidence intervals. The size of the confidence intervals allows for the evaluation of the real deviation between accurate observations and simulated data. In the VPC representation, the confidence intervals indicate the uncertainty of the predictions.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Artificial Intelligence (AI) and Machine Learning (ML)
#### 6.3.1 AI-Driven Avatars  
AI-driven avatars represent a remarkable fusion of artificial intelligence and digital animation, poised to revolutionize how we interact in virtual environments [108]. These avatars are sophisticated digital representations of individuals designed to mimic human behavior, appearance, and communication with high realism and responsiveness. At the core of these avatars is advanced AI technology, enabling them to learn, adapt, and respond to users naturally and intuitively [191]. The AI-driven avatars are integral in enhancing the user experience within the metaverse, providing a seamless and immersive interaction that bridges the gap between humans and machines. One of the most significant aspects of AI-driven avatars is their ability to understand and process natural language [192]. Using natural language processing (NLP) algorithms, these avatars can interpret spoken or written language, allowing for fluid and dynamic conversations with users. Machine learning models further enhance this capability, enabling avatars to learn from interactions and improve their responses over time. As a result, users can engage in meaningful and personalized dialogues with their avatars, making virtual interactions more engaging and lifelike.  
In addition to linguistic capabilities, AI-driven avatars are equipped with advanced facial recognition and emotion detection technologies [193]. These technologies allow avatars to read and respond to human emotions, adapting their behavior and expressions accordingly. For instance, an avatar can recognize when a user is happy, sad, or frustrated and tailor its responses to provide appropriate emotional support or feedback. This emotional intelligence adds another layer of depth to virtual interactions, making AI-driven avatars not just functional tools but empathetic companions in the digital world. The applications of AI-driven avatars extend far beyond entertainment and social interactions. These avatars can serve as personalized tutors in educational settings, offering tailored instruction and feedback to students [194]. They can provide efficient and empathetic support in customer service, handling inquiries, and resolving issues with a human touch [195]. In healthcare, AI-driven avatars can act as virtual companions for patients, providing comfort and monitoring their well-being [196]. By integrating AI-driven avatars into various sectors, the metaverse can unlock new possibilities for enhancing user experiences and improving the quality of services across multiple domains.
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 5 Results and Discussion  
This section provides the results and a discussion of the results for the simulation and each of the three experiments described in Section 4. These results demonstrate the feasibility of the proposed FAGPS scheme and the performance improvement over traditional non-corrected GPS receiver systems.
# A Survey on Blockchain-Based Federated Learning
## 3 Blockchain-Based Federated Learning
### Functions of BFL
#### 3.2.5 Efficiency Enhancement  
Lastly, to address the issue of inefficiency, BFL schemes often employ various methods to reduce the amount of data that need to be transmitted. The approach proposed in [82] stores specific related data in an off-chain distributed hash table, and only pointers are stored on the blockchain, thereby reducing the data transmission volume. [PERSON] et al. [87] introduced an asynchronous FL scheme for the edge data learning model which further enhances the efficiency of FL by selecting participating nodes. [PERSON] et al. [30] introduced the committee consensus mechanism, which verifies local gradients before attaching them to the chain. Under this mechanism, only a few nodes are used to verify model updates, eliminating the need to broadcast to every node and reach a consensus and, thus, improving the efficiency of model verification. [PERSON] et al. [88] described a gradient compression scheme, which can enhance the communication efficiency of blockchain-authorized federated edge learning without compromising learning accuracy. Furthermore, [PERSON] et al. [121] proposed a method that incorporates hyperparameter optimization and elastic weight consolidation into federated learning to enhance the accuracy and efficiency of the model training.  
The integration of FL and blockchains makes the system a comprehensive closed-loop learning mechanism. On the one hand, FL technology provides a secure, cross-domain sharing solution for participants with private data. On the other hand, blockchain technology, serving as the core database, provides participants with application needs such as secure storage, trust management, fine-grained differentiation, and incentive returns, encouraging users with data to actively participate in data federation.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Data Rate
#### 3.3.2 DR for LTE-V2X Protocol (DR\({}^{\rm LTE}\)) and NR-V2X (DR\({}^{\rm NR}\))  
The effective DR\({}^{\rm LTE}\) is defined as the number of data bits sent per frame divided by the frame duration (\(t_{fr}\) = 10 ms), such as  
\[DR^{LTE}=\frac{P_{b}.8.n_{RB-fr}^{LTE}}{n_{RB}^{LTE}.t_{fr}} \tag{11}\]  
where \(n_{RB-fr}^{LTE}\) is the total number of RBs available for data in a frame (see [34], Table 2).  
The DR for the NR-V2X protocol (DR\({}^{\rm NR}\)) is obtained by  
\[DR^{NR}=\frac{P_{b}.8.n_{RB-slot}^{NR-slot}}{n_{RB}^{NR}.s_{slot}^{NR}} \tag{12}\]  
where \(n_{RB-slot}^{NR}\) is the number of RBs per slot (see [37], Table 5.3.2-1), and \(t_{slot}^{NR}\) is the slot duration depending on the numerology as given in (see [38], Table 4.3.2-1).
## 3 Results and Analysis
### Effect of OIINS Irrigation on Characteristic Aroma Compounds of Tomato Fruit
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Serial** & **Characteristic Aroma** & **Odor** & \multirow{2}{*}{**RI**} & \multicolumn{5}{c}{**Relative Content of Each Component (\%)**} \\
**Number** & **Compound** & **Description \({}^{\dagger}\)** & & **NNNN** & **ONNN** & **NONN** & **NNON** & **NNNO** \\ \hline \(1\) & (Z)-3-Hexenal & Tomato, green & 802 & 20.99 \(\pm\) 1.13 & 51.49 \(\pm\) 1.37 & 43.82 b \(\pm\) 3.26 & 49.49 \(\pm\) 2.85 & 29.1 c \(\pm\) 0.68 \\ \(2\) & (E)-2-Hexenal & Green & 855 & 26.81 \(\pm\) 0.53 & 20.22 b \(\pm\) 2.88 & 26.08 a \(\pm\) 1.8 & 26.95 a \(\pm\) 2.27 & 27.81 a \(\pm\) 1.63 \\ \(3\) & (E)-2-Heprenal & Green & 967 & 1.37 \(\pm\) 0.09 & 0.93 c \(\pm\) 0.01 & 1.13 b \(\pm\) 0.01 & 0.71 e \(\pm\) 0.01 & 0.81 d \(\pm\) 0.014 \\ \(4\) & Hexanal & Green, grassy & 803 & 19.07 \(\pm\) 0.08 & \(-\) & \(-\) & \(-\) & \(15.31\) b \(\pm\) 0.23 \\ \(5\) & 3-Methylbutanol & Musty & 753 & \(-\) & 0.12 c \(\pm\) 0.01 & \(-\) & \(0.15\) a \(\pm\) 0.006 & 0.13 b \(\pm\) 0.006 \\ \(6\) & Methyl salicylate & Wintergreen & 1223 & 5.74 b \(\pm\) 0.03 & 2.27 d \(\pm\) 0.012 & 3.43 c \(\pm\) 0.05 & 1.14 e \(\pm\) 0.012 & 6.16 a \(\pm\) 0.05 \\ \(7\) & 1-Penten-3-one & Fruity, floral, green & 724 & 2.15 a \(\pm\) 0.04 & 2.19 a \(\pm\) 0.031 & 2.09 a \(\pm\) 0.016 & 1.71 b \(\pm\) 0.015 & 2. a \(\pm\) 0.23 \\ \(8\) & 6-Methyl-5-HepHep-2 one & Fruity, floral & 997 & 1.39 bc \(\pm\) 0.03 & 1.64 a \(\pm\) 0.004 & 1.41 b \(\pm\) 0.02 & 1.34 c \(\pm\) 0.01 & 1.2 d \(\pm\) 0.07 \\ \(9\) & 2-Isobutylthiazol & Tomato vine, green & 1054 & \(-\) & 0.73 \(\pm\) 0.015 & \(-\) & \(-\) & \(-\) \\ \(10\) & Total relative content & & & 77.52 a \(\pm\) 0.57 & 79.59 a \(\pm\) 1.54 & 77.96 a \(\pm\) 1.52 & 81.49 a \(\pm\) 5.1 & 82.52 a \(\pm\) 1.28 \\ \hline \end{tabular} Note: “-” indicates that the compound was not detected or did not exist. \({}^{\dagger}\) The odor description refers to [PERSON] et al. [31]. RI = retention index. The retention indices were calculated using a normal alkane mixed label, soluble in methanol and ranging from C-7 to C-40. Values are presented as the mean \(\pm\) standard deviation (\(n=3\)). Different letters after the standard deviation of the mean indicate significant differences between treatments (Duncan’s test, \(p<0.05\)).  
\end{table} 
Table 4: Characteristic aroma compounds and their relative content in tomato fruits under the five treatments. Figure 5: _Cont_.
## 4 Secure Cooperation
### Secure Dispatching Service
#### 4.1.1 Confidentiality  
Block ciphers split cleartext in blocks of fixed, predefined bit-length. In the most general case, cleartext length is not multiple of the block length. Thus padding is necessary. However, padding has the negative effect that the ciphertext may result up to one block longer than the corresponding cleartext. This effect is called ciphertext expansion. While ciphertext expansion is negligible in a traditional network, it becomes relevant in wireless sensor networks and, in particular, underwater acoustic networks. In these networks, communication and energy limitations require keeping a message size small and ciphertext expansion may introduce an overhead that is not negligible anymore. Let us consider the cooperation algorithm. At each step a vehicle sends 104-bit payload specifying the vehicle identifier (8 bits), position (two real numbers for a total of 64 bits), and sonar detection range RD (a 32-bits real number). Encrypting the payload by means of AES, whose block size is 128 bits, would introduce 24 bits of padding thus causing a 23% of payload expansion. Of course, for any given payload, the percentage weight of the ciphertext expansion much depends on the block size and ultimately on the cipher. However, in order to completely avoid the ciphertext expansion problem, we have used the CipherText Stealing (CTS) technique that alters the processing of the last two blocks of plaintext, resulting in a reordered transmission of the last two blocks of ciphertext and no ciphertext expansion [21].
## 4 Simulation and Analysis
### Changing Field Density of Nodes  
Node deployment in the field is changed in this scenario. Thus, a large quantity of sensor nodes are deployed in one portion of the area, in zone 1 for example, while the remaining few are placed in another part, in zone 2. The base station is located in the center with no change. Nodes can be skewed in the x-axis as well as in y-axis of the area. The proposed EZ-SEP protocol performance is evaluated under these conditions, compared with the remaining protocols. Results shown in Table 3(b) indicate that proposed EZ-SEP is in better condition than other protocols, if the nodes position in the network is altered. The whole sensor network behavior is changed. SEP and LEACH does not display good results in comparison with the Z-SEP and EZ-SEP protocols in terms of stability period of alive nodes. Z-SEP and EZ-SEP operate the same till the 2000 th round, after which the proposed EZ-SEP has longer nodes' lifetime and nodes' stability than Z-SEP. In the data transmission ratio, SEP and LEACH deliver a significantly lesser amount of data than the proposed algorithm, while Z-SEP stays near to EZ-SEP. In any case, the EZ-SEP protocol gives better results than the parent protocol. In addition, our proposed protocol has a better energy consumption line in the graph, while LEACH is the most energy-consuming protocol than others.
## 5 Conclusions and Further Work  
The use of machine learning methods, shown in this work, indicates that there are many new research areas where it is possible to improve the quality of images or change the representation, for example, conversion to a different space. Images often contain a lot of information that is not fully exploited by humans due to the limitations of the brain. The use of computer methods allows for breaking these barriers.  
Presented network is of the regression type and it can be a network which will then be used for preprocessing for further image analysis. The analysis of structures, in this case microscopic structures of the liver, is interesting for research reasons, which was one of the motivations for the work in this field.  
The article shows the possibility of performing image conversion for microscopic liver slides. The ability to convert the H&E image to a virtual fluorescence UV image does not mean that this type of operation can always be performed for a slide of any tissue. Learning for a specific tissue type is essential each time that a system is designed.  
It would be interesting to investigate the effect of network size on the results, but this requires quite a long study. The neural network presented in the article was selected after several attempts. In general, it is necessary to optimize the network structure (meta-optimization). This will be the subject of further research.  
**Author Contributions:** Conceptualization, P.M. and D.O.-M.; methodology, P.M., M.P.; software, P.M.; validation, M.P., D.O.-M., M.P.; investigation, D.O.-M., P.M.; resources, D.O.-M., M.P.; data curation, D.O.-M.; writing--original draft preparation, P.M., D.O.-M.; visualization, P.M., D.O.-M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the UE EFRR ZFORR project Z/2.32/I/1.3.1/267/05 \"Szczccin University of Technology-Research and Education Center of Modern Multimedia Technologies\" (Poland). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used also for this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Comparison between the Monitoring Data and the Results of 3D FEM Analysis
### Three-Dimensional Finite Element Analysis of the Excavation and Retaining Piles
The simulation steps for the excavation and support were as follows: (1) calculating the initial stress state of the overall element (the stress distribution map of the vertical covering soil of the stratum); (2) defining the excavation range and setting its stiffness matrix to 0; (3) the obtained initial stress of the excavation face was applied to the excavation face with the reverse equivalent nodal force; (4) application of equivalent nodal forces to predetermined values; (5) iterative convergence of the equivalent nodal force on the excavation surface to 0 and calculation of the stress-strain state of the overall element after excavation; (6) restoring the support structure mesh and applying gravity; (7) carrying out the next round of the excavation calculation. The 3D finite element mesh of the excavation and support (as shown in Figure 10) showed the simulation method for the construction sequence of each excavation and support unit. The simulation of the construction sequence of the stratum excavation and the retaining piles' support was divided into three steps, namely, (1) the initial stress application of the stratum, (2) the first-stage excavation of the stratum to G.L.-3.5 m, and (3) the second-stage excavation of the stratum to G.L.-6.75 m. The results of the calculation with the 3D finite element program showed the total displacement distributions of the first and second stages of the excavation, which are shown in Figure 11a,b, respectively. It can be found from the figure that the action of the retaining piles was able to reduce the stratum displacement, and the unsupported excavation area produced more displacement. In addition, the distribution diagrams of the displacement and the second deviatoric stress of the retaining pile are shown in Figures 12 and 13, respectively. As shown in the figures, there was no plastic zone in the gravel layer. Figure 11: Iso-value of the total displacement obtained with the 3D FEM analysis for (**a**) the first-stage stratum excavation and (**b**) the second-stage stratum excavation.
### Tar Concentration
#### 3.2.2 Variation of Temperature \(\vartheta\)  
Figure 7 shows the tar concentration at different gasification temperatures \(\vartheta\). At the 659 \({}^{\circ}\)C a very high gravimetric tar concentration of 98 g m\({}^{-3}\) was measured. When the temperature was increased to 778 \({}^{\circ}\)C the concentration drastically dropped to 31 g m\({}^{-3}\). With further temperature increase, the gravimetric tar concentration steadily dropped to 27 g m\({}^{-3}\) and 21 g m\({}^{-3}\) for 842 \({}^{\circ}\)C and 894 \({}^{\circ}\)C, respectively. As known [55], at low gasification temperatures, considerable fractions of the fuel are not converted into small molecules, but form large tar molecules which are detected well by gravimetric analysis. At higher temperatures those gravimetric tars are cracked or reformed into smaller tar species or into permanent gases.  
Figure 6: Gravimetric and GC tar concentrations dry, N\({}_{2}\)-free and STP basis at different oxygen ratios \(n_{\text{O2}}\) and constant \(\vartheta\).  
The total GC tar concentration was 31 g m\({}^{-3}\) at 659 \({}^{\circ}\)C. When the temperature was increased, the GC tars were found to be decreasing slightly but steadily to 23 g m\({}^{-3}\) at 894 \({}^{\circ}\)C. It has to be noted that the concentration of tar species from ECN2 and ECN3 classes decreased severely with temperature, while benzene is increasing.  
In the literature, overall similar behaviour is reported [22, 47, 52, 56, 57, 58, 59].
## 3 Results
### Calibration Curve of Fat Content  
We have plotted the experimental dependence of fat content in milk, \(C\), on the scatterometric parameter, \(X_{i}\), at the side-scatter angles, \(\ \theta_{i}\) (Figure 5). The experimental points for each \(\ \theta_{i}\) are approximated by a polynomial of the fourth degree, which can serve as a calibration curve for converting photodiode signals into fat percentage:  
\[C_{i}=A_{i0}+A_{i1}X_{i}+A_{i2}(X_{i})^{2}+A_{i3}(X_{i})^{3}+A_{i4}(X_{i})^{4}, \ \ i=1\ldots N \tag{2}\]  
where \(N=3\) is the number of processed side-scatter channels that are provided by the corresponding photodiodes located at the angles \(\ \theta_{i}=\{72^{\circ},90^{\circ},\ 108^{\circ}\}\); \(X_{i}\) is the scatterometric parameter of each channel. If averaging is used, then the fat percentage, \(FP\), can be determined as:  
\[FP=\sum_{i}C_{i}/N. \tag{3}\]  
Figure 5: Dependence of fat percentage, \(FP\), in milk on the scatterometric parameter, \(X_{i}\), in the range 0.05–10% at zero flow velocity in the tube at a temperature of 20 \({}^{\circ}\)C. Laser wavelength is 532 nm. The standard deviation of scatterometric parameter values is no more than 0.01.  
The coefficients of \(A_{ij}\) determined from the experimental data using the least-squares method are presented in Table 1.  
As shown below, the calibration that was found in the static case (Figure 5) is also valid during milk flow, since the scatterometric parameter for the corresponding fat percentage remains unchanged with good accuracy at flow rates up to 100 mL/s.
# Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit
## 1 Introduction
However, due to its complex calculation process, high hardware requirements, and extended calculation time, it is only suitable for small structures or typical joints to carry out process research, and it is difficult to be used in the prediction of the overall weld deformation of large structural components. In this regard, the Japanese scholar [PERSON] [12; 13] was the first to introduce the concept of inherent strain, positing that welding residual stress and deformation during the welding process are caused by inherent strain. For large and complex welded structures, only the inherent strain needs to be calculated, and then the residual stress and welding deformation can be obtained through a linear elastic calculation [14]. Since then, many scholars have begun using the ISM to predict welding deformations [15; 16; 17]. [PERSON] et al. [18] used the ISM to predict the welding deformation of the turbine guide vane's outer ring. The results of the calculations matched the experimental measurements well, and the deformation of the outer ring was reduced by changing the welding sequence. [PERSON] et al. [19] applied the inherent strains extracted from the TEP calculation results to solid and shell mesh models, respectively, and confirmed that accurate results can also be obtained by using the shell mesh model for the calculation. [PERSON] et al. [20] introduced an equivalent thermal strain method for predicting welding deformation in thin metal plates, building on inherent strain theory. Test verification and comparison revealed that this method offers high calculation accuracy and requires a short computation time. [PERSON] et al. [21] summarized an empirical formula for the inherent strain in the lifting platform using the TEPM. The results from elastic calculations matched the measured data well, and the welding deformation of the platform was effectively controlled by modifying the welding sequence. Based on the analytical properties of TEPM and ISM, the welding process of the upper frame of the hydropower unit is studied in this paper. Based on the weld structure type of the upper frame, typical T-joints were simulated using the TEPM, and the influence of welding processes such as welding speed and interlayer cooling time on the temperature field and stress field was studied. The welding process meeting the requirements was obtained, and the accuracy of the established finite element model was verified by experiments. The optimal welding sequence of the upper frame is determined by using the ISM, which can provide a reference for the actual welding of the upper frame.