esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Analysis of 3D (SLM) Co-Cr-W Dental Metallic Materials
## 3 Results and Discussion
### Microstructural Surface State  
It is known that the initial powder particles are mostly spherical, and the powder surface is clean and smooth [15]. Herein, the partial molten particles (Figure 2a,d) had more irregular shapes and their sizes were clearly bigger than those of the initial particles. After the evaluation of 100 particles prior to the SLM process, we obtained an average radius size of 12 \(\upmu\)m, a minimum size of 6.1 \(\upmu\)m, and a maximum size of 20.5, with a standard deviation of 2.6 \(\upmu\)m. The circumference of the particles presented an average value of 75 \(\upmu\)m (st. dev. 1.6) and a mean area of 468 \(\upmu\)m\({}^{2}\) (st. dev.: 205). In the case of the semi-molten particles, after SLM, a union phenomenon was exhibited. The average size of the radius was 13.87 \(\upmu\)m, with a minimum size of 8.85 \(\upmu\)m, a maximum size of 38.63 \(\upmu\)m, and a standard deviation of 0.86 \(\upmu\)m (for the 25 \(\upmu\)m--1 sample); meanwhile, the average size of the radius was 20.83 \(\upmu\)m, with a minimum size of 8.41 \(\upmu\)m, a maximum size of 89.31 \(\upmu\)m, and a standard deviation of 15.27 \(\upmu\)m (for the 50 \(\upmu\)m--1 sample).  
The big dimensions and irregularities of the semi-melted particles in most of the printing strategies negatively impacted the powder melting and layer-by-layer coating [17].  
This aspect, i.e., the surface of the material after 3D printing, is important for the selective laser melting process because this technique requires the melting of a powder layer (of a specific thickness) and recoating of this layer with a new one, up to hundreds of layers. The surface parameters of the melted powders must be strictly controlled due to their influence on the particle flux, which affects the laser consolidation [18]. For the semi-melted particles from the surface, a reduction in the number of attached parts of the particles can be observed in the main particles (Figure 2a,d).  
After the sandblasting process, all of the particles remaining on the surface after SLM were removed (Figure 2b,c,e,f). The surfaces presented different roughnesses and aspects after the sandblasting process. The 3D images (Figure 3) confirm the results observed from the 2D images and present the aspects of the unified powders after SLM and the new roughnesses after the sandblasting process.  
The structural and dimensional analyses show that the semi-melted particles remaining on the surface after the selective melting fabrication process are quite different from the initial powders in terms of size and appearance, and likely metallurgical characteristics as well, taking in consideration the high rate of cooling of the melted powder layers; however, no microstructural analyses were made in this article to confirm this.
## 2 RBF Mesh Deformation with Greedy Algorithm
### RBF Mesh Deformation  
For a grid model, whole points could be divided into two types. One is internal point, which fills the internal space of the grid model, and the other is boundary point, which is on the grid boundary and attaches to the rim of moving object. The RBF mesh deformation interpolation could be interpreted as follows:  
\[s(\mathbf{x})=\sum_{j=1}^{N_{b}}\lambda_{j}\phi(\|\mathbf{x}-\mathbf{x}_{b_{j} }\|), \tag{1}\]  
where \(\mathbf{x}\) is the coordinate of a random point, \(\mathbf{x}_{b_{j}}\) is the coordinate of the \(j^{th}\) boundary point, \(N_{b}\) is the total quantity of boundary points, \(\lambda_{j}\) is the weight coefficient, \(\phi\) is the selected radial basis function, \(\|\mathbf{x}\|\) is the Euclidean norm of vector \(\mathbf{x}\) and \(s(\mathbf{x})\) is the estimated displacement of point \(\mathbf{x}\). We could calculate the displacement of the moving boundary points which trace with the rim of moving object, and the displacements of the static boundary points are vector 0. Therefore, the displacements of the boundary points could be calculated as follows:  
\[s(\mathbf{x}_{b})=\Delta\mathbf{x}_{b}=\Phi_{b,b}\boldsymbol{\lambda}, \tag{2}\]  
\[\Phi_{b,b}(i,j)=\phi(\|\mathbf{x}_{b_{i}}-\mathbf{x}_{b_{j}}\|), \tag{3}\]  
where \(s(\mathbf{x}_{b})\) and \(\Delta\mathbf{x}_{b}\) are the estimated displacements and actual displacements of the boundary points, \(\Phi_{b,b}\) is an \(N_{b}\times N_{b}\) matrix, which stores the RBF calculations and \(\boldsymbol{\lambda}\) is the N-dimensional coefficients vector that contains \(\lambda^{x}\), \(\lambda^{y}\), and \(\lambda^{z}\) values. Thus, we could obtain the value of \(\boldsymbol{\lambda}\):  
\[\boldsymbol{\lambda}=\Phi_{b,b}^{-1}\Delta\mathbf{x}_{b}. \tag{4}\]  
After calculating the value of \(\boldsymbol{\lambda}\), we could obtain the estimated displacement of the internal points by:  
\[s(\mathbf{x}_{in_{i}})=\sum_{j=1}^{N_{b}}\lambda_{j}\phi(\|\mathbf{x}_{in_{i} }-\mathbf{x}_{b_{j}}\|), \tag{5}\]where \(\mathbf{x}_{in_{l}}\) is the \(i_{th}\) internal point and \((s(\mathbf{x}_{in_{l}}))\) is the estimated displacement of \(\mathbf{x}_{in_{l}}\). Therefore, the matrix formulation could be described as:  
\[\Delta\mathbf{x}_{in}=\Phi_{in,b}\boldsymbol{\lambda}, \tag{6}\]  
\[\Phi_{in,b}(i,j)=\phi(\|\mathbf{x}_{in_{l}}-\mathbf{x}_{b_{j}}\|), \tag{7}\]  
where \(\Phi_{in,b}\) is an \(N_{in}\times N_{b}\) matrix, and \(N_{in}\) is the total quantity of internal points.  
For the formulation above, the radial basis functions \(\phi\) possess various expressions. In general, it is divided into two categories: compact functions and global functions as presented in Table 1. Compact functions limit the range of the interpolation with a support radius. Global functions operate on the whole grid internal points. The distinct of the two categories is explained by [PERSON] et al. (2011). According to the research by [PERSON] et al. (2013), the radius basis function we have chosen is the thin plate spline (TPS), which generates the highest mesh quality of the update grid.
# Numerical Investigation of Slurry Fracturing during Shield Tunneling under a Reservoir
## 3 Verification of Numerical Solution  
In order to study shield slurry fracturing, [PERSON] et al. designed a laboratory hydraulic fracturing device, and used blind-hole samples to analyze the influencing factors of clay slurry fracturing, including the soil's unconfined compressive strength, slurry viscosity, loading conditions, etc. [17]. The objective of this section is to verify the capabilities of this method in modeling shield slurry fracturing in clay formations. To this end, the laboratory experiment reported by [PERSON] et al. is numerically duplicated. The XFEM method described in this paper is used to model and calculate the slurry fracturing of the blind-hole samples, and then compared and verified with the test data.  
Figure 4: Process of tension-shear fracture criterion.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 2 Literature Review  
Vision-based sign language recognition is a well-researched topic involving developing machine learning models to recognize sign language. These models use various inputs, such as cameras or sensors, to identify and interpret sign language [6]. There are several categories within sign language recognition, including fingerspelling, isolated words, lexicons of words, and continuous signs [7].  
Despite the promising potential of this technology, there is a noticeable gap in research focusing on NSL recognition. This is surprising, considering an estimated 250,000 to 300,000 hearing-impaired individuals in Norway, with 3500 to 4000 being completely deaf [8].  
We aim to research specific machine learning models, examining their strengths and weaknesses. We aim to highlight why these models are suitable in our comparative analysis of NSL recognition. We also reference results from previous research papers to provide context and results from research on other sign languages.
## 3 Experiment Setup
This variability in the original dataset is caused by the different number of repetitive tests conducted at each location. Due to practical constraints and the nature of the testing process, the number of successful and usable tests varied across locations, resulting in an uneven distribution of raw data, as seen in Figure 3.  
Figure 3: Time series augmentation showing the original data (orange) and generated data (green) to ensure each label has a balanced and sufficient number of samples for improved deep learning model performance. Figure 2: (**a**) Aluminum plate with the grid and fiber-optic sensor for AE testing (**b**) Schematic representation of the aluminum plate detailing the grid layout and test points. The proposed augmentation strategy is designed to address these discrepancies by generating high-fidelity synthetic data, with a particular focus on underrepresented locations. By supplementing the original data with these generated samples, we ensure a sufficient and balanced number of time series for each location. The labeling of the augmented dataset is preserved during this process. Each generated AE signal inherits the label (corresponding to the source location) of the original signal it was based on. This approach ensures that the augmented data maintain the same spatial distribution of AE sources as the original dataset, while increasing the volume and diversity of our training data. This data augmentation approach simultaneously tackles two critical challenges in deep learning: it mitigates the risk of overfitting on some particular labels (locations) with abundant data while addressing potential underfitting issues in locations with limited samples. The resulting balanced dataset, a product of this advanced augmentation technique, is anticipated to significantly enhance the model's capacity to accurately localize acoustic emission sources across all 35 locations. This improvement is expected to manifest in enhanced overall performance and superior generalization capabilities, ultimately leading to more robust and reliable acoustic emission source localization in real-world applications.
## 1 Introduction
The proposed method selects multiple time reference satellites using the bionic algorithm based on the change in LEO satellite network topology with the constraint of minimizing the sum of weighted clock tiers. The bionic algorithm calculates by iteration, and at each iteration the simulation result is closer to the optimal result, instead of calculating all the possible scenarios as the traversal algorithm does, so that consequently a large number of unnecessary calculations are saved; the time of calculation is greatly  
Figure 1: Schematic diagram of space and earth integration networks. reduced compared with the traversal algorithm, which can quickly calculate the optimal clock transfer path for the whole network and achieve the best time synchronization effect. When a single or part of the interstellar link fails due to unexpected circumstances, the topology of the satellite network changes, and the clock transmission path needs to be recalculated. The iterative approach of the bionic algorithm is far more efficient than the traversal algorithm that calculates one by one. In this paper, for the same model, the traversal algorithm needs to compute \(11^{6}\) times and takes 195.63 s, while the proposed bionic algorithm, with a minimum of only 10 iterations and 0.043 s, can enable the time synchronization network to recover quickly, which is an important application prospect in LEO satellite networks.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 3 Methodology
### Indirect Trust Value  
The indirect trust of any node is computed by getting indirect information from the common neighboring nodes. So, Equation (4) has been used in the proposed methodology to calculate the indirect trust value of any node.  
\[T.Indirect_{xy}^{n}=\frac{1}{p}\ \sum\
olimits_{Ci\in Ch}\left(T.Direct_{xCi}^{n} \times T.Direct_{Ciy}^{n}\right) \tag{4}\]  
As seen in Equation (4), \(c_{h}\) is the complete set of shared neighbors that both the trust evaluator nodes and the nodes whose trust is being assessed. Here \(p\) represents the total number of common neighbors. Moreover, in order to compute the indirect trust value from any node \(x\) to other node \(y\), first of all, direct trust is calculated from node \(x\) to common neighbors \(C_{i}\) and subsequently from common neighbors \(C_{i}\) to node y, as illustrated in Equation (4). After calculating the common neighbor node's direct trust value, it is compared to the threshold direct trust value. If their direct trust value is more than the threshold value or equal to it, it is proclaimed to be a trusted neighbor; otherwise, it is excluded from the list of common trusted neighboring nodes.
## 7 Conclusions  
The use of rapid prototyping tools such as MATLAB-Simulink and Xilinx System Generator becomes increasingly important because of time-to-market constraints. This paper presents a methodology for implementing real-time DSP applications on a reconfigurable logic platform using Xilinx System Generator (XSG) for Matlab. In this paper, we have proposed an FPGA-based stereo vision system for underwater 3D shape reconstruction. First, an efficient Matlab-code algorithm is proposed for laser spot extraction from stereo underwater images and 3D shape reconstruction. Algorithm evaluation shows that a mean error scale of distance measurements accuracy is of 2.5%, between real and estimated measurements, which is quite satisfactory regarding bad resolution of underwater images and light refraction effects. Nevertheless, the software algorithm takes few seconds to be executed which is not permitted by the real-time application performances. In order to satisfy required performances, we have developed a hardware architecture, focused on the fully use of pipeline, aimed to be implemented on a Virtex-6 FPGA. During the design process, optimizations are applied in order to optimize the used hardware resources and to decrease the execution time while maintaining a high level of accuracy. Hardware co_simulation results show that our architecture presents a good level of accuracy. An average error of about 0.0046 m is obtained between the software algorithm and the XSG architecture. Furthermore, timing analysis and evaluation of the complete design show that a high performance in terms of execution time can be reached. Indeed, the proposed architecture largely respects timing constraints of the used vision system and the application requirements. The developed system supports performance increase with respect to image resolution and frame rate. A further modification and amelioration could be applied in order to fit the 3D structure of the karst. In the end of the paper, experimental results in terms of timing performances and hardware resources utilization are compared to those of other existing systems. The authors would like to acknowledge the support of [PERSON], an engineer in charge of the mechatronics hall of the University, for the experimental setup. [PERSON] developed the algorithm, conceived and performed experiments and wrote the paper; [PERSON] contributed on the hardware design, experimentations and the paper writing; [PERSON] conceived the prototype and contributed on the algorithm development and experimentations; [PERSON] contributed on algorithm development and experiments performing; [PERSON] and [PERSON] contributed with general supervision of the project. The authors declare no conflict of interest.
# Assessment of the Impacts of Climate Change on Power Systems: The Italian Case Study
## 3 The Present Italian Power System  
The energetic mix of sources that contribute to Italian electricity production has strongly changed in recent years. In 2005, RES covered almost 16% of net production (due mostly to hydropower plants); in recent years this percentage has more than doubled.  
Figure 3: Stretch rescaling of a daily demand profile.  
Indeed, in 2020, renewable sources covered almost 42% of national production (114 TWh of the total 273 TWh); this percentage increased compared with 2019 (40%). Consequently, the amount of production from non-renewable plants as a proportion of overall Italian production reduced from 84% in 2005 to 58% in 2020. In absolute terms, the value of thermoelectric generation decreased from 236 TWh in 2005 to 157 TWh in 2020 (almost \(-\)33%).  
In Italy, in December 2020, the installed wind capacity was almost equal to 10,918 MW. A large part of the installed wind capacity is concentrated in southern Italy, due to more availability of wind in that part of the country. The installed PV capacity, in the same month and year, was equal to about 21,629 MW. Specifically, in 2020, the installed PV capacity increased by about 729 MW compared with 2019, whereas the wind source increased by about 160 MW compared with 2019 [36]. Figure 4 shows the evolution of the Italian production from 2011 to 2020. Figure 5 shows the historical demand profile from 2011 to 2020.  
Figure 4: Evolution of the net Italian production by color. The top of the columns shows the yearly energy production values in TWh.  
Figure 5: Historical Italian demand profile. The percentage change refers to the previous year.  
Regarding the size of PV systems, 99 % have a rated power of up to 200 kW so they are installed in the LV and MV distribution grid; in terms of globally-installed power this represents 42% of the total PV capacity. In this way, a real distributed generation is produced in Italy [36].
## 3 Results and Discussion  
### Growth Inhibition Assay on Beverage Spoilage Yeasts  
_Candida pyralidae_ Y1117 showed growth inhibition activity against all three beverage spoilage organisms (_D. bruxellensis, D. anomala_ and _Z. bailli_), while the two _P. kluyveri_ strains only showed inhibition activity against _D. bruxellensis_ and _D. anomala_ (Figure 2). [PERSON] et al. [10] reported growth inhibition activity of _C. pyralidae_ against _D. bruxellensis_, while this study showed the inhibition activity of _C. pyralidae_ against _Z. bailli_ and _D. anomala_ as well (Figure 2). According to the literature reviewed, some growth inhibition activities are associated with killer toxins produced by yeasts, including _Candida pyralidae_[10]. However, this study paid a special attention to the VOCs as well as the quantification aspects of the overall growth inhibition activity obtained using cheaply available raw material as fermentation medium.  
Figure 2: Growth inhibition activity of _Candida pyralidae_ Y1117 (1), _Pichia kluyveri_ Y1125 (2) and _P. kluyveri_ Y1164 (3) against _Zygosaccharomyces billii_**(a)**, _Dekera bruxellensis_**(b)** and _Dekera anomala_**(c)** on grape pomace extracts agar medium.
## 5 Experimental Results
### Results and Discussion
#### 5.2.1 Patch Applications for Standard Models  
The Patch performance results for standard models represented in Table 2. The parameters used for the standard models are noise_std = 0.01, N_Hiddens = 100, N_Layers = 3, and BATCH_SIZE = 512. Moreover, the hidden_dim = 256, latent_dim = 20, and 0.01 parameters are set for the VAE method. The LSTM model performed with low precision and accuracy values. The precision, accuracy, and sensitivity scores of the CNN model were higher, which indicates that the model could perform better. The MAE score was the highest, indicating more model value fluctuations. Although the accuracy score of the MLP model is very high, its precision, sensitivity, and F1 score are rather low. This may indicate that most of the model's correct predictions are negative samples. The Transformer is a model with a very high sensitivity score. However, the precision and accuracy values of this model could be higher. The precision and accuracy scores of the attention mechanism model are higher than those of other models. However, the sensitivity score is low, indicating that the model has difficulty in detecting all positive cases. The RESNET model has a high precision score, which indicates that most positive predictions are correct. The accuracy score is also at an acceptable level. The random forest model excels in terms of its accuracy, precision, and F1 score. The MAD and MAE values are low, indicating fewer errors. The Gaussian naive Bayes model has a high accuracy score, but its precision and sensitivity values are below average. This may indicate that most of the model's correct predictions are negative samples. The model's ability to detect positive situations is strong, but these predictions can often be false positives. The performance of this model could be improved.
## 3 Theoretical Penetration Model and Model Verification  
In this section, a modified Alekseevskii-Tate model based on the decay function for the lateral boundary effect is developed. Analytical solutions for the penetration depth are obtained. The penetration depth results from existing experiments and numerical simulations are subsequently used to validate the proposed model.
## 2 Demand-Side Management Classifications and Their Roles in Cost Minimization
### Classification of DSM  
DSM plays a vital role in minimizing the consumer load and shaping consumer demand according to the load curve provided by the generation or distribution of companies. An adequate DSM scheme can minimize the consumers' electricity bills, generation costs, peak hours' demands, etc. [57]. Moreover, the load curve of the consumers demands can also be flattened through efficient DSM. An efficient DSM scheme can achieve various performance metrics. These performance metrics include the reduction of consumers' electricity bills, the reduction of electricity demand during peak hours, generation cost minimization, demand curve flattening, consumer privacy, priority of consumer preferences and satisfaction, CO\({}_{2}\) emission reduction, and social welfare maximization. In the literature, various research articles are available on DSM. In all of these research papers, the authors tried to achieve one or a combination of the above-described objectives of DSM. We summarize these papers and classify them according to the performance metrics achieved by each author, as shown in Table 6.  
Consumer demand can be managed either directly by installing sensors inside or near to the home appliances or by shifting the high power rating devices into off-peak hours. For this purpose, DSM has been classified into two main types: direct load reduction and load scheduling.  
Direct load reduction can be further classified into two types: by the use of energy efficient appliances and by the use of energy efficient buildings [103]. In the same way, load scheduling is also divided into two types: centralized and distributed scheduling identifies, as shown in Table 6. A flow diagram of DSM classification along with relevant references is given in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. No.**} & \multirow{2}{*}{**Centralized**} & \multirow{2}{*}{**Decentralized**} & \multirow{2}{*}{\begin{tabular}{c} **Consumer** \\ **Privacy** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Consumer** \\ **Preference, Priority,** \\ \& Satisfaction** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **PAPR** \\ **Reduction** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Cost** \\ **Minimization** \\ \end{tabular} } & \multicolumn{2}{c}{**Use of DERs \&**} \\  & & & & & & & **Their** \\ \hline
[58] & ✓ & & & & & ✓ & ✓ & ✓ \\
[2] & ✓ & & ✓ & & ✓ & ✓ & ✓ \\
[59] & ✓ & & ✓ & & ✓ & ✓ & & \\
[60] & ✓ & & ✓ & ✓ & ✓ & ✓ & & \\
[61]