esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The aim of this work was to determine how platform preheating and a sealed deposition environment, with respect to the exclusive use of an Ar flow as a protective mean, impacted on the final quality of Ti-6 Al-4V samples built via DED. The most important results that were obtained can be summarized as follows:  
* Environmental factors in DED processes have a great impact on process feasibility, by directly influencing the final properties of the component. In particular, it was proved that the process atmosphere has a direct impact on the final properties of the component, such as microstructure and grain size, which result in a change of mechanical properties and can even affect the possibility to effectively build components;  
Figure 10: Horizontal bars built in a sealed chamber and using a preheated platform. Top view (**a**) and cross-section (**b**). Similar results were obtained using a non-preheated platform.  
* It is possible to obtain large crack-free samples only if a sealed environment is used and if the oxygen content can be kept very low. However, this makes the production of very large components more complex;
* Carrying the deposition in shielding gas makes the process feasible only if small components are produced. However, these samples will be characterized by a completely brittle behaviour, due to oxygen pick-up. Moreover, repairing small portions of large parts, which result impossible to fit in a sealed chamber, results acceptable in this environment. Although, the feasibility depends on the final requirements for the repaired part, in terms of mechanical properties. In fact, ductility might be a limiting factor.  
Conceptualization, A.C., A.A. and F.M.; methodology, A.C. and A.A.; validation, A.A., S.B. and M.L.; resources, F.M., S.B. and A.V.; data curation, A.A., S.B., M.L.; writing--original draft preparation, A.C.; writing--review and editing, A.A., F.M., S.B. and M.L.; project administration, S.B., A.V. and P.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Horizon 2020 research and innovation program with grant number 723795.  
The authors would like to acknowledge the European Horizon 2020 research and innovation programme; grant agreement No. 723795/4D Hybrid-Novel ALL-IN-ONE machines, robots and systems for affordable, worldwide and lifetime distributed 3D hybrid manufacturing and repair operations.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Procedures  
#### 2.3.1 Training  
Training was performed for 7 weeks, 2 times per week with at least 72 h rest between training sessions. The TG performed 4 sets of 6 repetitions with 85% of 1-RM, with a 3-min rest interval between sets. When a TG participant performed more than 6 repetitions in a set the load was increased in the following set to meet the 6-RM. The new (increased) load was  
Figure 1: Training design for inter-repetition rest group (IRRG) and traditional group (TG). Both groups trained with 4 sets of 6 repetitions but IRRG added 20 s rest between repetitions. Rest between sets was 3 min for both groups. 1-RM = one repetition maximum, RFD = rate of force development, R = Repetition.  
also used for the initial set of the following training session. Participants of the IRRG also performed 4 sets of 6 repetitions with 85% of 1-RM with 3 min rest between sets. However, the IRRG training included 20 s rest between single repetitions [9; 32]. Single repetitions were selected because of the previously reported positive effect on muscle strength [4]. During the 20-s rest between repetitions, the load was locked in the rack of the leg press machine and the participant remained seated with feet placed on the floor (not on the leg press platform). Five seconds before the end of the 20-s rest period, participants placed their feet on the leg press platform and performed the next repetition in response to the researcher instruction (precisely at 20 s). At all training sessions and repetitions two researchers assisted the participants in order to take off the load from the rack. This was performed because the load was locked in a lower position where the knees were bended approximately at 75-85\({}^{\circ}\). Thus, in order to avoid any unnecessary fatigue and injuries starting from this position all participants received assistance to unlock the loads. Given the advantage of inter-repetition rest training on achieving a high movement velocity in each repetition, participants were not specifically instructed to perform the training with maximum intended movement velocity. The total training session duration for the IRRG was 39.3 \(\pm\) 1.4 min and for TG was 36.9 \(\pm\) 0.9 min (\(p\) = 0.001, \(\eta^{2}\) = 0.176).  
Thirty minutes after the end of every training session, participants provided their rating of perceived exertion 1-10 scale score (RPE) [33], for the evaluation of the training intensity. An RPE score lower or equal to 7 (very hard range), signified a 2.5% load increase for the next training session for the IRRG [34; 35]. By the end of the current training intervention program, no significant difference was observed in mean session RPE (IRRG: 7.8 \(\pm\) 0.3 vs. TG: 8.1 \(\pm\) 0.3, \(p\) = 0.419, \(\eta^{2}\) = 0.047, Figure 2) and in mean total training volume (IRRG: 5090.
# Non-Classic Atmospheric Optical Turbulence: Review
## 5 Theoretical Predictions for Non-Classic Turbulence-Light Interactions
A similar analysis, performed on the degree  
\[W_{ij}(\mathbf{r}_{1},\mathbf{r}_{2};\omega)=\iint W_{ij}(\mathbf{r}_{1}^{ \prime},\mathbf{r}_{2}^{\prime};\omega)K(\mathbf{r}_{1},\mathbf{r}_{2}; \mathbf{r}_{1}^{\prime},\mathbf{r}_{2}^{\prime};\omega)d^{2}\mathbf{r}_{1}^{ \prime}d^{2}\mathbf{r}_{2}^{\prime}, \tag{39}\]0 The root-mean-square width of a scalar Gaussian–Shell model (GSM) beam on propagation in non-Kolmogorov turbulence. Reprinted with permission from Reference [91]. of coherence of such beams, leads to a similar set of cross-sections but the ellipses are stretched along the horizontal direction [96]. \[W_{ij}(\mathbf{r}_{1},\mathbf{r}_{2};\omega)=\iint W_{ij}(\mathbf{r}_{1}^{ \prime},\mathbf{r}_{2}^{\prime};\omega)K(\mathbf{r}_{1},\mathbf{r}_{2}; \mathbf{r}_{1}^{\prime},\mathbf{r}_{2}^{\prime};\omega)d^{2}\mathbf{r}_{1}^{ \prime}d^{2}\mathbf{r}_{2}^{\prime}, \tag{39}\]1 Changes in (**A**) the degree of coherence and (**B**) the degree of polarization of the electromagnetic Gaussian–Schell model (EM GSM) in non-Kolmogorov turbulence. \(\alpha=3.01\) (solid curve), \(\alpha=3.1\) (dashed curve), \(\alpha=3.67\) (dotted curve) and free space (dot-dashed curve). Reprinted with permission from Reference [90]. \[W_{ij}(\mathbf{r}_{1},\mathbf{r}_{2};\omega)=\iint W_{ij}(\mathbf{r}_{1}^{ \prime},\mathbf{r}_{2}^{\prime};\omega)K(\mathbf{r}_{1},\mathbf{r}_{2}; \mathbf{r}_{1}^{\prime},\mathbf{r}_{2}^{\prime};\omega)d^{2}\mathbf{r}_{1}^{ \prime}d^{2}\mathbf{r}_{2}^{\prime}, \tag{39}\]2 Spectral density \(S\) of (**A**) a coherent; (**B**) a partially coherent electromagnetic Gaussian–Schell model (EM GSM) beam in the up-link anisotropic turbulence, for \(\alpha=3.5\) and several values of the effective anisotropic factor \(\xi\sim\mu_{z}^{-1}\). Reprinted with permission from Reference [94]. Light propagation analysis through turbulence with the jet-stream spectrum slightly modified for integral convergence was performed in [97]. In addition, the behavior of higher-order statistics of light in non-classic turbulence was also studied by other methods, e.g., the Rytov method. We will discuss some of these results below on considering the applications. The evolution of the second-order statistics of non-stationary (pulsed) fields in non-Kolmogorov turbulence was treated in [98; 99].
# Experimental Study of Erosion Prevention Model by Bio-Cement Sand
###### Abstract  
Microbially induced carbonate precipitation (MICP) technology is employed to reinforce the surface soil of a dam, aiming to prevent erosion caused by water flow and damage to the dam slope. The relationship between penetration depth, calcium carbonate content, and bonding depth was investigated at eight measuring points on the sand slope surface of a mold under different reinforcement durations. It was observed that as grouting reinforcement times increased, there was a gradual increase in calcium carbonate content but a rapid rise in penetration resistance. Moreover, the bonding depth of sand on the bio-reinforced sand slope increased with higher levels of calcium carbonate content. Microbial grouting reinforcement enhanced soil particle bonding force, requiring water flow to overcome this force for activation of sand particles. Consequently, microbial grouting reinforcement significantly improved shear strength and critical starting flow velocity on sand slope surfaces. The experimental results demonstrated that after MICP surface treatment through spraying, a dense and water-stable hard shell layer composed of bonded calcium carbonate and soil particles formed continuously on sample surfaces, effectively enhancing the strength and erosion resistance of sandy soils. These findings provide reliable evidence for silt slope reinforcement and dam erosion prevention.  
scour; erosion; penetration resistance; critical starting velocity; microbially induced carbonate precipitation +
Footnote †: journal: Applied sciences  
Acticle
## 3 Results and Discussions
### Emission Characteristics  
#### 3.2.1 CO Emission  
Figure 8 illustrates the variation of CO emission for four different fuels according to various engine loads from 30 Nm to 90 Nm. As shown in Figure 8, the CO emission is significantly increased as the percentage of palm oil is increased at all engine loads. At 30 Nm, the CO emission of BP10, BP30 and BP50 increases by 7.04%, 31.69% and 36.62% compared with that of BP 0, respectively. At 60 Nm, the CO emission of BP10, BP30 and BP50 increases by 1.48%, 18.52% and 27.41% compared with that of BP 0, respectively. At 90 Nm, the CO emission of BP10, BP30 and BP50 increases by 21.13%, 24.65% and 45.77% compared with that of BP0, respectively. It is widely known that CO emission is produced by incomplete combustion of fuel due to insufficient oxygen. Many researchers have reported that oxygenated fuels such as biodiesel reduce diesel engine CO emission due to the oxygen contained in biodiesel. The oxygen carried by biodiesel improves the fuel/air equivalence ratio, especially for improving the problem of insufficient oxygen in local fuel-rich areas, thereby promoting more carbon molecules to oxidize [24,28]. However, in this study, the increase in CO caused by adding palm oil to diesel may be mainly related to the high density, high viscosity and low cetane index of palm oil. [PERSON] et al. [23] and [PERSON] et al. [29] also reported similar results. [PERSON] et al. [23] reported that the increase of CO emission is related to the difficult atomization caused by the heavy compounds produced by the chemical reactions of palm oil. [PERSON] et al. [29] also reported that the CO emission of a single cylinder diesel engine fueled with various raw vegetable oil fuels is more than that of diesel engine. They pointed out that the poor spraying qualities and uneven mixture were the main reasons for increasing CO emission. On the other hand, the CO emissions for all test fuels are decreased with the increase of engine loads from 30 Nm to 90 Nm. Compared with at 30 Nm, the CO emissions of all fuels are reduced by 55.26% and 48.30% on average at 60 N and 90 Nm, respectively. This is mainly attributed to the rise of temperature in the cylinder, which is conducive to the oxidation of CO emission with the increase of engine load [28].  
Figure 7: BSFC for all test fuels according to various engine loads.
## 4 Network Communication  
BSNs have many features similar to WSNs in the aspects of network communication, such as in the aspects of network architecture, radio technology, communication protocols, energy control, and network security, _etc._ As a result, many of the BSN technologies are derived from WSN technologies which are more mature [31, 88, 89, 90], but some characteristics of BSNs make it difficult to directly transplant technologies from WSNs. For example, sensor devices are basically deployed on or in a human body, so the electrical characteristics of the body can influence radio communications. Human activities will also cause frequent changes of network topology [89, 91]. Therefore, researchers should improve the existing WSN technology to apply in BSN communications properly, or propose new technology to meet the specific needs of BSN communications.
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 2 Related Work  
### Medical Image Segmentation  
The segmentation of internal structures from medical images is essential for a wide range of clinical applications. Modern medical image segmentation methods predominantly employ fully convolutional networks (FCNs) or UNet [10] architectures, framing the task as a dense classification problem. Broadly, current approaches to medical image segmentation can be divided into two key areas: network design and optimization strategies. The first category focuses on the development of various 2D and 3D segmentation network architectures, as explored in works such as [11; 12; 13]. These studies aim to improve feature representation through techniques like dilated convolutions, deformable convolutions, pyramid pooling, and attention mechanisms. Since the introduction of the vision transformer (ViT), several studies [14; 15] have reformulated segmentation as a sequence-to-sequence prediction task using the ViT architecture [16].  
The second category leverages medical prior knowledge to enhance network training [17; 18], focusing on the design of specialized loss functions tailored to medical imaging data. These loss functions aim to address challenges like class imbalance and improve the segmentation of uncertain pixel categories. In addition, our approach extends these concepts to real-world clinical scenarios by leveraging large amounts of unlabeled data and working with very limited labeled data during the training phase. The segmentation quality is further enhanced by providing additional supervision for each category of uncertain pixels, thereby improving the overall accuracy and robustness of the model.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 3 Method  
As previously described, the trajectory obtained based on the path-searching method is close to the obstacle and not smooth enough. This section introduces a trajectory generation method based on B-spline. The method proposed uses the convex hull property of the B-spline and the upper-bound property of the error between the B-spline curve and the control polygon to guarantee the dynamic feasibility and safety of the trajectory. Then we set and solve the optimization problem to obtain a smooth, safe, and dynamic feasible trajectory quickly.
## 3 Proposed Methodology
### Z-Number Based Fuzzy TOPSIS  
The following is an overview of the steps of Z-number based Fuzzy TOPSIS [67].  
Step 1: Create the Z-fuzzy initial decision matrix (ZFIDM) for the Z-number fuzzy TOPSIS to evaluate the circular models. Linguistics phrases is used in the building of fuzzy decision matrices for evaluating alternatives from the Tables 4 and 6.  
Step 2: Transform the Z-numbers comprising experts' preferences into fuzzy numbers using the Equations (3) and (5). Aggregate the experts' preferences. Using the geometric mean to combine the experts' preferences of the fuzzy initial decision matrix (FIDM).  
Step 3: Compute the weighted normalized matrix by multiplying the global weight into FIDM. The global weights employed from the Z-number based fuzzy DEMATEL.  
Step 4: To defuzzify the weighted normalized matrix using the Equation (2) to transform fuzzy numbers into the crisp numbers.  
Step 5: Calculate the High Impact Ideal Solution (HIIS) represents as the H\({}^{+}\) and Low Impact Ideal Solution (LIIS) represents as the L\({}^{-}\) by the following equations.  
\[a_{j}^{HIIS}=\underset{i=m}{\max}a_{ij} \tag{12}\]  
\[a_{j}^{LIIS}=\underset{i=m}{\min}a_{ij} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Linguistic Definition** & **Crisp Number** & **Triangular Fuzzy Number** \\ \hline Very Low Impact (VLI) & 1 & (0, 0, 0.1) \\ Low Impact (LI) & 2 & (0, 0.1, 0.3) \\ Medium Low Impact (MLI) & 3 & (0.1, 0.3, 0.5) \\ Medium Impact (MI) & 4 & (0.3, 0.5, 0.7) \\ Medium High Impact (MHI) & 5 & (0.5, 0.7,0.9) \\ High Impact (HI) & 6 & (0.7, 0.9, 1.0 \\ Very High Impact (VHI) & 7 & (0.9, 1.0, 1.0) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Triangular fuzzy membership linguistic terms for restriction part [68].  
Step 6: Compute the distance between the individual circular model of the _HIIS_ (\(D_{i}^{HIIS}\)) and _LIIS_ (\(D_{i}^{LIIS}\)) by the following equations.  
\[D_{i}^{HIIS}=\sqrt{\sum_{j=1}^{N}\left(a_{ij}-a_{j}^{HIIS}\right)^{2}} \tag{14}\]  
\[D_{i}^{LIIS}=\sqrt{\sum_{j=1}^{N}\left(a_{ij}-a_{j}^{LIIS}\right)^{2}} \tag{15}\]  
Step 7: To identify the high impact parameter, compute the closeness coefficient (\(CC_{i}\)) for each individual parameter.  
\[CC_{i}\frac{D_{i}^{LIIS}}{D_{i}^{LIIS}+D_{i}^{HIIS}} \tag{16}\]
## 4 Snake Robot Ethical Issues in SAR Response Phase
### Data  
Data collection, storage, and usage are the main stages of any SAR process. The sensitive data collected throughout these missions include location and personal information. Although sometimes it is necessary to gather personal information through cameras and sensors, it might conflict with the individual's interests. It is crucial to look at the consent of different victims. The stakeholders must keep this in mind by considering different issues like data storage and accessibility. Further, data authority and its usage must be taken into consideration [41].  
* Data safety and security:The sensible data gathered from the catastrophe need to be secured in a safe location. In such a case, who will be in charge of maintaining data safety and security from unethical practices? How do officials ensure that the information obtained during SAR is stored securely and is impervious to hacking attempts from any other agencies?
* Accessibility: How to identify and whom to provide ownership of the SAR data, whether it be the robot, local government, or an organisation. Further, defining the method of using data is difficult. By addressing various ethical challenges, the government must create operational criteria for data management (how and for what purpose the data can be utilised in the future).
* Privacy: The kind of information the robot gathers is crucial. People need to be aware of the data collected and how it will be used in the future. Different data-related issues must be communicated to the designers and robot operators to protect victim privacy. Further, if data is collected; it must be appropriately organised to avoid having an impact on people.  
From the above discussion, it is crucial to consider ethical issues while developing and deploying robotics for the SAR response phase. The ethical framework mentioned above can be used with snake robot technology. The following list outlines the dilemmas that will arise when using snake robots in the SAR response phase.
## 3 Results and Discussions
The variations of pitch angle at different entry conditions are shown in Figure 8. It is clear that, as the initial entry angles increase, the fluctuation amplitude of pitch angles decrease, and their vibrating periods become longer. When the entry angle is 10\({}^{\circ}\) (Figure 8a), the pitch angle grows quickly for short length projectile \(L=5D\), it touches the upper cavity wall earlier, and make the time taken for pitch angle begin to decrease, and its vibration period is shorter than that of projectile with \(L=8D\). With the increase of entry angles (Figure 8b for 45\({}^{\circ}\), Figure 8c for 80\({}^{\circ}\)), the variations of pitch angles become small. Zoom-in view of the initial entry stage in Figure 8a-c, it is clear that, compared with the short projectile (4D), the long projectile (8D) has small initial pitch angle and better stability. The time histories of drag coefficients during the water entry process are given in Figure 9. The drag coefficient is defined as \(Cd=2F/\rho V^{2}A\), where \(F\) stands for the drag force, \(\rho\) means the density  
Figure 2:0 Time histories of pitch angles at different entry angles: (**a**) 10\({}^{\circ}\), (**b**) 45\({}^{\circ}\), (**c**) 80\({}^{\circ}\). Figure 2:1 Drag coefficients of water entry for different initial inclination angles: (**a**) 10\({}^{\circ}\), (**b**) 45\({}^{\circ}\), (**c**) 80\({}^{\circ}\). of the water, and \(A\) denotes the characteristic area. It is clear that the initial impact causes very high drag and it grows quickly with the increase of entry angle and velocity. After the initial impact, the drag coefficients tend to decrease slowly for all cases and the value is almost constant. It can be concluded that, after the projectile entering into water, its drag is only related to its hydrodynamic shape, the influence of entry velocity and angle on the drag coefficient is not obvious.
## 5 Results and Discussion
### Electrode Distance  
The different electrode distance was achieved through inserting the electrode into the soil at a particular position between the fixed electrodes. The soil samples were saturated by slurry without CMC, and the water environment was deionized water. Figure 15 shows the change in soil resistance with different measured distances of the electrodes. The intercepts of fitting curves on the y-axis denotes the contact resistance, which is the sum of the constriction resistance and resistance of the film [34; 35; 36]. It was observed that a stable linear relation existed between the resistance of soil samples \(R\) and distance of electrodes \(L\). \(R\) increased with the increase of \(L\). However, due to the increased amount of bentonite particles between the electrodes and soil sample, the contact resistances decreased with the increase in slurry concentration [37]. The variance of the contact resistances may bring about a strong deviation in soil electrical resistivity in two-point tests.
# Hybrid Precoding Design for Wireless Fading Channels
## 2 System Model
Let us define the group of all users applying average-based linear precoding as group 1 and the group of users applying symbol-based precoding as group 2. Also, if we express the received signals for group 1 and group 2 as \(\mathbf{y}_{1}=\left[y_{1},\ \ldots,\ y_{K}\right]^{T}\) and \(\mathbf{y}_{2}=\left[y_{K+1},\ \ldots,\ y_{K+J}\right]^{T}\), respectively, \(\mathbf{y}_{1}\) and \(\mathbf{y}_{2}\) can be written as follows:  
\[\mathbf{y}_{1}=\beta_{1}\mathbf{H}_{1}\mathbf{W}_{1}\mathbf{s}_{1}+\beta_{2} \mathbf{H}_{1}\mathbf{x}_{2}+\mathbf{z}_{1}, \tag{5}\]  
\[\mathbf{y}_{2}=\beta_{1}\mathbf{H}_{2}\mathbf{W}_{1}\mathbf{s}_{1}+\beta_{2} \mathbf{H}_{2}\mathbf{x}_{2}+\mathbf{z}_{2}, \tag{6}\]  
where the noise vectors are \(\mathbf{z}_{1}=\left[z_{1}\,\ \ldots,\ z_{K}\right]^{T}\) and \(\mathbf{z}_{2}=\left[z_{K+1}\,\ \ldots,\ z_{K+J}\right]^{T}\) with sizes of K \(\times\) 1 and J \(\times\) 1, respectively, and the channel matrices, \(\mathbf{H}_{1}\) and \(\mathbf{H}_{2}\), of user groups 1 and 2, respectively, are given as follows:  
\[\mathbf{H}_{1}=\left[\mathbf{h}_{1},\ \ldots,\ \mathbf{h}_{K}\right]^{H}\in \mathbb{C}^{K\times M}, \tag{7}\]  
\[\mathbf{H}_{2}=\left[\mathbf{h}_{K+1},\ \ldots,\ \mathbf{h}_{K+J}\right]^{H}\in \mathbb{C}^{J\times M}. \tag{8}\]