esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Thickness of the Hardened Layer and the Surface Hardness after Boriding Steel Products
For nitridded or substantially horizontal samples of curves, durability does not depend on friction (steel 40 Cr, and bearing steel Cr15) or is slightly reduced at the initial testing stage (high-speed tool steel W6 Mo5). After boring all steels, wear resistance considerably increases at the initial stage (up to 120 m of the way of friction, for high-speed tool steel W6 Mo5--80 m), and then it slowly changes. Experimental data showed that the cutting tool equipped with inserts from WC92 Co8 and WC85 Ti6 Co9 is more resistant to the untreated steel WC92 Co8 by 183% and WC85 Ti6 Co9--more than 200%. It is possible to increase wear resistance after nitriding up to 120-177% and boring and up to 180-340% depending on alloy steel. The scientific novelty of this research, among other works, is as follows. For the first time, a decrease in the coefficient of variation of microhardness, stability and an increase in the stability of the physical and mechanical properties of carbide cutting tools after processing with a pulsed magnetic field was theoretically determined and experimentally established. Additionally, based on the analysis of the patterns of structure formation of diffusion layers, it was found that the use of nanocrystalline nitrogen-containing powder allows us to obtain the necessary ratio of the surface layer hardness and its depth distribution, which allows us to increase the wear resistance of the machine parts and tools, as well as accelerate the process of the nitriding of alloy steels in comparison with traditional gas nitriding by ten times without the use of special equipment. Finally, it was found that the new proposed nanodispersed boron-containing powder intensifies diffusion processes, which accelerates boring during furnace heating by 2-3-old compared to existing technologies while significantly increasing the wear resistance of machine parts and tools. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Steel Grade**} & \multirow{2}{*}{\begin{tabular}{c} **Boriding** \\ **Temperature**, \\ \({}^{\circ}\)**C** \\ \end{tabular} } & \multicolumn{3}{c}{\
\begin{tabular}{c} **The Layer Thickness of Borides, \(\mu\)m**, \\ **for the Different Boriding Duration** \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} **Surface Hardness** \\ **(Microhardness), GPa** \\ \end{tabular} } \\ \cline{3-8}  & \({}^{\circ}\)**C** & **15 min** & **30 min** & **45 min** & **60 min** & **90 min** & **120 min** \\ \hline Bearing steel Cr15 & 850 & 14 & 31 & 44 & 54 & 66 & 73 & 21.4 \\ W18 & 1000 & 8 & 10 & 12 & 16 & 29 & 49 & 23 \\ W6 Mo5 & 1000 & 3 & 4 & 6 & 10 & 22 & 38 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The effect of the duration of the boring of steel products on the change of the boride layer.
## 2 Configuration Design and Problem Formulation
### Variable-Sweep Configuration Design
Although structural impact load and stability still need to be considered, until the most perfect solution is explored, the several variants mentioned above can be considered as feasible solutions for achieving complete functionality of the HAUV. In order to pursue a simple and streamlined shape and avoid the impact of water on vulnerable parts, such as the paddles of multi-rotor, combined with reference to the research and application of aerial vehicles and underwater gliders [19], this paper considers the blended wing-body configuration with outstanding hydrodynamic performance as the main shape, and its high lift-drag ratio characteristics are very attractive. For example, in response to DARPA's special transport submersible aircraft concept design, the Carderock Division of the US Naval Surface Warfare Center carried out corresponding research and launched the concept of two types of submersible aircraft with a blended wing-body layout in 2010 [20]. It can be said that the submersible aircraft with this configuration pointed out a new direction for the development of HAUVs in the future. Shanghai Jiao Tong University has also conducted relevant numerical simulation studies on the water-entry process of an aircraft with morphing configuration [21]. Therefore, HAUV with configuration of variable-swept BWB used in this paper is proposed, and a depiction of the multibody is shown in Figure 2.  
On the premise of not considering the structural strength of material and the implementation method of the morphing mechanism, the main focus stage is the process of the HAUV plunge-diving water with variable-swept configuration. Many studies have shown that there is a significant positive correlation between the peak impact force at the moment and the speed of water entry. Therefore, researchers suggest minimizing the speed before water entry as much as possible. In this regard, the usual measure is to shut down the engine so that the thrust T = 0 during the water-entry process. The entry mode is to complete a dive into the water with a fixed sweepback after completing a variable sweep in the air.
# Reducing Dynamic Power Consumption in Mixed-Critical Real-Time Systems
## 5 The Proposed Scheme
### DVFS Scheduling
```
1:functionJob-Release(\(\tau_{i}\))
2:if the current system mode is Low then
3: Insert job \(J_{i}(C_{i}(LO),t+VD_{i})\) into \(\mathcal{Q}_{ready}\)
4:if\(X_{i}=\) Low then\(\triangleright\) Low-criticality job
5:\(U_{i}\leftarrow(C_{i}(LO)/f_{LO}^{LO})/P_{i}\)
6:else
7:\(U_{i}\leftarrow(C_{i}(LO)/f_{LO}^{HI})/P_{i}+((C_{i}(HI)-C_{i}(LO)/f_{HI}^{HI})/P _{i}\)
8:endif
9:else\(\triangleright\) The current system mode is High
10:if\(X_{i}=\) Low then
11:\(U_{i}\gets 0\)
12:else\(\triangleright\)\(X_{i}=\) High
13:\(U_{i}\leftarrow(C_{i}(HI)/f_{HI}^{HI})/P_{i}\)
14: Insert job \(J_{i}(C_{i}(HI),t+P_{i})\) into \(\mathcal{Q}_{ready}\)
15:endif
16:endif
17:Power-aware Schedule( )
18:endfunction
19:functionJob-Finish(\(J_{i}\))
20:if the current system mode is Low then
21:if\(X_{i}=\) High then\(\triangleright\) High-criticality job
22:if\(J_{i}\) finish \(C_{i}(LO)\) completely then
23:\(U_{i}\leftarrow(Ci(LO)/f_{LO}^{HI})/P_{i}\)
24:else
25: The system mode changed to High \(\triangleright\) Mode switch to HI
26:endif
27:endif
28:else\(\triangleright\) The current system mode is High
29:if\(\mathcal{Q}_{ready}=\varnothing\)then
30: The system mode is changed from High to Low \(\triangleright\) Mode switch back to LO
31:endif
32:endif
33:Power-aware Schedule( )
34:endfunction
35:functionPower-aware Schedule( )
36:if\(\mathcal{Q}_{ready}\
eq\varnothing\)then
37:\(J_{k}\leftarrow\) the job with the earliest deadline in \(\mathcal{Q}_{ready}\)
38:if\(J_{curr}=\varnothing\)then\(\triangleright\) CPU idle
39:\(J_{curr}\gets J_{k}\)
40:elseif\(D_{k}<D_{curr}\)then\(\triangleright\) Preemption by EDF
41:\(J_{curr}\) is preempted and re-Inserted into \(\mathcal{Q}_{ready}\)
42:\(J_{curr}\gets J_{k}\)
43:endif
44:Frequency-Adjust( )
45:endif
46:endfunction
```  
**Algorithm 1** Algorithm of energy minimization consumption in mixed-criticality tasks.
## 4 Methodology
### Evaluation Metrics
#### 4.4.1 Qualitative Evaluation  
Visual evaluations are typically the focus of qualitative evaluation measures. Different approaches for visualization might be utilized, depending on the task and the model outputs' format. It is relatively easy to visualize two-dimensional data, but it is more difficult to do so with higher-dimensional (time-series) data. Plotting the created time-series and comparing them to the actual data is the simplest way to visualize the performance of a model.  
This might not be sufficient because it is challenging to distinguish tiny differences between actual and synthetic samples. Therefore, it would be impossible to determine whether the model had actually absorbed the entire distribution or not. To address this issue, dimensionality reduction to perhaps two or three dimensions via principal component analysis (PCA) [13] and plotting the findings from that are common approaches to visually evaluating multivariate data [13]. We utilized **PCA**[13] and **t-SNE**[14] to convert the multi-dimensional result sequence vectors into two dimensions so that we can visually compare the distribution of the synthetic with actual data values. We also represented  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**S. No.** & **Parameters** & **Value** \\ \hline
1 & Sequence Length & 60 s \\ \hline
2 & No. of samples & 32 \\ \hline
3 & Optimizer & Adam [39] \\ \hline
4 & Learning rate & 0.0005 \\ \hline
5 & pre-training epochs No & 100 \\ \hline
6 & No. of epochs & 50,000 \\ \hline
7 & No. of Stacked GRU layers & 6 \\ \hline
8 & No. of units & 32 units for layers 1, 2 and 3 and 16 units for layers 4, 5 and 6 \\ \hline
9 & Loss function LS, LR & MSE \\ \hline
10 & Loss function LU & BCE \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training parameters of MTS-TGAN.  
the sequences in numeric data form in terms of discriminative and predictive scores as discussed below.  
Given that it measures how good the synthetically created data are, the **discriminative score** can be used to evaluate any GAN model in general. As it demonstrates how well the model caught the temporal dynamics/step-wise dependencies of the data, the prediction score is particular to time-series data. This metric is based on the TSTR training phase, i.e., Train on Synthetic data and Test it on a Real one [7]. On the resulting dataset, we first train a sequence prediction model to determine this measure. We assess this model's performance using the original data set after training (much as the discriminative score implemented by optimizing a five GRU layer). By figuring out the mean absolute error, we may calculate the **predictive score**.
## 3 Discussion
### Adjacency  
In two out of the four tasks (the arpeggio training task and the transfer task), _Adjacency_ has a strong positive effect (35% and 42% decreases in inaccuracy). The statistical results from the other two tasks (see also Figures 2 and 5) also hint at this positive effect but the values are not significant. It seems that any impact of adjacency in the transfer task is swamped by the learning that has already occurred by the time the adjacent layout is played (remember that an adjacent layout always came second for all participants and never first or third). A future experiment that balances the design, by also having nonadjacent layouts first and third, will have greater statistical power to detect the impact of adjacency in this context. Judging the results in total, we have strong evidence that adjacent major seconds improve playing accuracy in a variety of one-handed melodic tasks. Although this could be a result of the high number of major seconds present in both the scales and the nursery rhyme chosen for the transfer task, the positive effect of adjacency effect is also seen in the arpeggio task, which has a large emphasis on other intervals--notably major and minor thirds, and perfect fourths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fixed Effect** & **Estimate** & **Factor** & _p_**-Value** \\ \hline _(Intercept)_ & 3.43 & 30.85 & \textless{}0.001 *** \\ _Adjacency_ & \(-0.05\) & 0.96 & 0.408 \\ _Shear_ & \(-0.50\) & 0.61 & \textless{}0.001 *** \\ _LayoutNo_ & \(-0.38\) & 0.68 & \textless{}0.001 *** \\ _PerfNo_ & \(-0.13\) & 0.88 & \textless{}0.001 *** \\ _LayoutNo:Shear_ & 0.33 & 1.39 & 0.002 ** \\ _LayoutNo:PerfNo_ & 0.07 & 1.07 & 0.021 * \\ \hline Log Likelihood & \(-\)996.89 & & \\ Num. obs. & 288 & & \\ Num. groups: ID & 24 & & \\ Var: ID (Intercept) & 0.11 & & \\ Var: Residual & 0.26 & & \\ \(r^{2}\) & 0.66 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Generalized linear mixed effects model for the transfer task (_Fèrer Jacques_).  
Here, we might have expected the adjacent layout to be no more useful than the nonadjacent layout. However, the findings suggest that successfully learning major seconds may also provide a useful foundation for learning and playing other intervals. Hence, adjacent major seconds may be a broadly useful property.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 4 Performance Evaluation
### Result of Experiments
#### 4.2.2 Comparison of Indexing Time  
We compared the indexing time of HI-Sky, BBS-R, BBS-Q, and Z-SKY. The experiments were conducted by varying the data type, dimensionality, and data distribution for various scenarios. We also show that the proposed method (i.e., HashGrid, the index structure of HI-Sky) can work well when data points are frequently inserted or deleted from the datasets.  
Figure 6 shows the results in terms of indexing time for each data type and distribution. The experimental results show that HashGrid was faster than the R-tree of BBS-R or the ZBtree of Z-SKY because both needed to repeatedly change the structures of their trees to maintain optimal conditions for computing. In particular, the optimal RZ-region of ZBtree was computed using a split process to maintain the optimum RZ-region. However, the cost of computing the optimal RZ-region required more computation than a simple partitioning of the R-tree or quadtree. Thus, the indexing time was longer overall. By contrast, the hash bucket search of HashGrid and storage in the hash bucket were reduced to a time complexity of O(1), which enabled fast data storage. Further, as the hash table did not structurally change due to the data input, there was no additional cost to maintain the optimal structure. However, HashGrid was slower than the quadtree of BBS-Q above twelve dimensions. This is because the asymmetric tree structure of the quadtree can be relatively inexpensive to maintain. However, HashGrid increases the number of buckets generated due to the high-dimensionality, which increases the cost of managing them.  
Figure 5: The performance changes according to varying np.  
Figure 7 shows the processing time when data is repeatedly inserted or deleted into the HashGrid. The experiments were conducted using varying dimensions and data distribution. In the case of deletion, Figure 7a,c,e show the consumed time when 10 K data are deleted 20 times consecutively from HashGrid composed of 1 M dataset. In the case of insertion, Figure 7b,d,f show the consumed time when inserting 10 K data 20 times consecutively into HashGrid composed of 800 K dataset. The result  
Figure 6: Indexing time (1 Million).  
of experiments demonstrates that for skewed data (i.e., correlated or anti-correlated), the time taken for the deletion process was relatively increased due to a large number of data stored in specific GLADs in a higher dimension. However, we did not observe a significant change in the overall performance of HashGrid caused by the volume of insertion and deletion operations. This is because HashGrid is a hash index-based approach that is not a volume-sensitive, unlike tree structure-based approaches [35].
## 6 Discussions
Yet more rigorous evaluation is expected in the future, such as the controlled user experiment comparing the perception on the node-link and the matrices diagrams [36], or the animation versus static display [33]. As a complementary, we also provide several designs to enhance user perception, adopting highlighting, zooming and time slider interactions, and providing multiple views. **Workarounds:** Before discussing workarounds, we would like to make clarification on the definition of \"large\" in our context. There are different definitions for \"large-scale\", ranging from 50 nodes [56] to thousands of nodes [57] and even to millions of nodes [58]. These variations account for differences in the domain, the data itself, tasks and visual techniques [56]. In the context of our presentation, directly presenting the network with node-link diagram and considering user perception capabilities, the number is relatively small. In the empirical study of literature on human-centered experiments [56], networks with 51 to 200 nodes are identified as a large network. According to this identification and combining our user experiment results, we define networks with more than 50 nodes as a large network in our context. To address the issue of visual clarity with large networks (with more than 50 nodes), other than using more sophisticated layout techniques, algorithms for network simplification have been investigated [1]. Network simplification techniques such as aggregating [50; 51; 52; 53], sampling [59; 60; 21; 58; 59; 61; 62], and filtering [6] minimize the problem scale while keep important information. As a workaround, for a large-scale network, users can first use these simplification techniques to reduce the network to a manageable size, then use our method to compute the layout of the final network. In light of the above discussions, we propose that our method can support the large-scale online dynamic network visualization in two ways. First, for usage scenarios of directly presenting large-scale dynamic networks with node-link diagrams, our method can better preserve stable structures of the network, while the challenge on visual and perception efficiency can be alleviated by interaction approaches such as highlighting, panning-and-zooming, and filtering; or it can be used together with other visual forms in multiple-view designs, as a \"level-of-detail\" or an \"overview + details-on-demand\" approach. Such example design can be found in egoSlider [6] which presents large dynamic network in node-link diagrams with multi-focus-highlighting and with multiple coordinated views. Second, for the scenarios where network simplification techniques are used, our method can act as a visual effect for the final layout. The clustering, sampling, bundling, and filtering techniques acquire representative \"small networks\" of the original network. Their analysis processes generally involve observing the \"small network\". For small network visualization, node-link diagrams are more readable and more familiar than matrices [36]. Our method can then be used to present the \"small network\" evolution in a coherent manner.
# An Overview of CubeSat Missions and Applications
## 8 The Use of CubeSats in Novel Technology Demonstration Missions
### Communications, AI, and Automation Technology Demonstration
In StarFox, they could track each other's orbits without GPS, with the orbit estimates within 1% accuracy. Finally, in ROMEO, they did not manage to autonomously plan and perform propulsive maneuvers to maintain swarm formation due to navigation filter errors. The Starling mission, despite ROMEO's challenges, was a major step towards future deep-space swarm missions.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Adsorption Data  
#### 4.2.1 Initial Concentration Effect  
The effect of concentration on cadmium and methylene blue adsorption was determined at 298 K and is indicated in Figure 3a,b. Both figures show that cadmium and methylene blue sorption increased as the concentration increased. This means that cadmium and methylene blue adsorption on PHSs, CHS-500 and CHS-700 depended on the concentration. A similar observation was made by [PERSON] et al. (2022) [41]. This type of behaviour by adsorbents was explained in a study by [PERSON] et al. (2011) [42], which states that the increased sorption capacity is due to high mass transfer resistance. At a high concentration of 100 mg/L, there were more collisions between the cadmium and methylene blue and adsorbents' surfaces; hence, higher sorption capacities were observed.  
Figure 1: FTIR spectra for hemp-based adsorbents.  
It was also shown that CHS-700 had a higher cadmium and methylene blue adsorption capacity than PHSs and CHS-500.  
Figure 2: SEM images for (**a,b**) PHSs, (**c,d**) CHS-500 and (**e,f**) CHS-700.
# Effect of Track Irregularities on the Response of Two-Way Railway Tracks
## 2 Train-Track-Soil Interaction Model  
A time-domain Train-Track-Soil interaction model was developed to study the effect of different level of track irregularities on the response of two adjacent railway tracks. In this study, three separate models are considered (1) train model, (2) track and soil model and (3) track irregularities. The train was modeled as a quarter train model ([PERSON], 2015). A track consisting of two rails was placed on sleepers that were connected by rail-pads. The rails were modeled as 3D-beams and rail pads as distributed spring-dashpot elements, which transmit all the forces caused by the moving train to the track-soil system. The other parts of the track (i.e., sleeper and ballast) and soil domain were modeled as solid elements. Lysmer and Kuhlmeyer boundary condition was incorporated to prevent the reflection of stress waves from the edges of the domain ([PERSON] and [PERSON], 2019). Furthermore, Non-linear Hertz contact theory was utilized to couple train and track. The track irregularity was modeled as a stationary Gaussian random process based on power spectral density function. A detailed explanation of each model is given as follows:
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 5 Recommendations for RL Algorithms in Robotics  
Robotics presents its own set of unique challenges for RL training whether in simulation or real life. Techniques like _PPO_, _SAC_, and _DQN_ have found extensive use in robotic control tasks due to their balance of stability, efficiency, and adaptability. Selecting the appropriate model, policy, and parameters for a set of environment and action space can be very a challenging task. Based on our review of successful experiments both with real physical robots and sim-to-real deployments, we present the following recommendations:  
* _Sampling Efficiency:_ Collecting real-world data from robots can at times be both time consuming and costly. While model-based methods are more efficient in terms of sampling, they require a very accurate and detailed model of the operating environment which might not always be possible, especially in the case of dynamic environments [94]. Algorithms with high sample efficiency are preferred, as they reduce the need for extensive data collection. Algorithms like _DQN_ and _PPO_ are popular in robotics due to their balance between sample efficiency and performance.
* _Action Space Exploration_: The high-dimensional action space needs to be explored to identify optimal behaviors. In cases where the action space is highly dimensional, techniques such as Random Network Distillation (RND) are often combined with RL algorithms to improve exploration in complex environments [95]. Robotic action space can be either be continuous or non-continuous. Policy gradient methods typically excel in continuous action spaces, making them more suited for robotic control tasks. At the same time, environments with parse rewards can present challenges for techniques such as _DQN_.
* _Action Space Constraints_: In real-world robotics, action spaces are often constrained and limited. For continuous action spaces, _TRPO_ methods are particularly well suited, while algorithms like _Q-Learning_ can suffer from instability in high-dimensional continuous action spaces, limiting their applicability in robotics [96].
* _Stability and Convergence_: To achieve good performance, it is important for the model to converge to an optimal policy while exploring the search space. RL algorithms such as _SAC_ have proved to be stable, ensuring reliable performance in dynamic environments by balancing exploration and exploitation [97].
* _Generalization of Learned Behaviors_: It is important for the robot to learn a generalized behavior so that it can be applicable in various dynamic environments and scenarios. Hybrid models or hierarchical models that sub-divide the tasks are generally more suitable for achieving better performance on a variety of environments/applications [98].