esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Micro-Benchmark Analysis  
To confirm the performance impact of STUN, we optimized Hackbench, a micro-benchmark for the kernel scheduler, using STUN. The Hackbench execution option had 120 processes with 1000 iterations. Through the filtering process, the scheduler policy chose \"Normal\" and the parameters kernel.sched_latency_ns and kernel.sched_wake  
Figure 5: STUN performance according to the number of steps. The x-axis of each graph is the number of learning iterations. The y-axes of (**A**,**C**,**E**) represent the performance of Sysbench (the total number of events), and the y-axes of (**B**,**D**,**F**) represent the state values of each parameter.  
up_granularity_ns as optimization variables. The optimal values of the learning result are as follows:  
* Scheduler Policy: Normal;
* kernel.sched_latency_ns = 20,100,000;
* kernel.sched_wakeup_granularity_ns = 190,000,000.  
The learning results show a 27.7% reduction in the Hackbench execution time from the default setting: 2.72 to 1.95 s. Figure 6 shows the execution time of Hackbench at each step of the learning process.  
While the learning was in process, the results of Hackbench randomly increased and decreased, but after 16,000 steps, the learning process showed stable performance. Figure 7 shows the changes in state of two parameters for optimization during the learning process. Although each parameter initially increased and decreased randomly during the learning process, STUN finally found the optimal values.
# Application of Directed Energy Deposition-Based Additive Manufacturing in Repair
## 5 Conclusions  
In principle, different industries intend to repair components that need to withstand severe working conditions and also are vital to the whole process. Thus, the quality of repair is highly considered and evaluated to achieve the final properties of the repaired part, equal to those of the base component. DED is a maturing disruptive AM technology that provides new possibilities to repair complex geometry and expensive parts for various industries, from automotive to aerospace ones. In comparison with traditional methods, the main advantage of DED process is its flexibility according to a CAD model, lower heat input, and consequently small HAZ and dilution zones and lower residual stresses. All the research studies on this topic reported that the repair process using DED is not only cost-effective but also time-saving. The free formability of DED process can also significantly change the existing manufacturing processes by the concept of adding special features to a simple damaged cast and forged components to repair and improve their performances. Apart from the scientific and lab-scale investigation, the repair approach based on DED is successfully being used in different industries for various applications to reduce remanufacturing costs. Nonetheless, since this technology is rather new, and there is no handbook available, the qualification phase is quite long before the final approval of the components. The next step is to define a standard procedure for repairing to reduce the period of qualification phase.  
To write this review article, [PERSON], [PERSON], and [PERSON] collected the papers and wrote the article; [PERSON], M.L., and [PERSON] revised the article technically and scientifically.  
This research was funded by Horizon 2020 research and innovation program with grant number [723795].  
The authors would like to acknowledge the European research project belonging to the Horizon 2020 research and innovation program; 4D Hybrid--Novel ALL-IN-ONE machines, robots, and systems for affordable, worldwide, and lifetime Distributed 3D hybrid manufacturing and repair operations (grant agreement no.723795).  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Compressive Strength  
Figure 7 shows the compressive strength test results according to the type of reinforcement fiber used in RCLMFRHCC for emergency concrete pavement repair. Regardless of the reinforcement fiber, all mixes satisfied the goal of a 4-h strength of \(\geq\)21 MPa. Furthermore, after a period of 28 curing days, they all satisfied the strength requirements of \(\geq\)35 MPa. Compared with the mix that contained no reinforcement fibers, the compressive strength of the mixes using reinforcement fibers was slightly higher. The compressive strength of the mix using macro synthetic fibers was the highest. It showed a similar compressive strength to the mixes with natural jute fibers and PVA fibers. Generally, when latex is added, it has the effect of delaying the hardening time of the concrete. Thus, this delays the time to achieve the desired compressive strength [5; 11; 12; 15]. To address this problem, we reduced the latex amount to 5% of the cement weight from 15%, which is used generally in pavement repair [6; 12; 13; 15]. Furthermore, during compaction, pressure vibration compaction was used to mimic roller compaction; it had the effect of tightening the concrete structure [24; 25; 26; 27]. Thus, all samples satisfied the 4-h curing compressive strength of 21 MPa and 28-day curing compressive strength of 35 MPa, which are standards for emergency repair concrete. When reinforcement fibers were added, it effectively suppressed crack generation by hydration heat and thus, increased the compressive strength slightly [6; 11; 12]. However, the increase in compressive strength was not significant. The reason for this is that the addition of reinforcing fibers is effective in increasing tensile and flexural strengths rather than compressive strengths. However, a slight increase in strength can be expected in terms of crack controls [16].  
Figure 6: Slump test results.  
Fiber reinforcing materials can be classified into structural fibers to improve structural performance, such as strength and non-structural fibers to be used for crack control and durability improvement [16]. Structural fibers include steel fibers and structural synthetic fibers [22; 23]. Nonstructural fibers include polypropylene fibers, jute fibers, and PVA fibers. In this study, macro synthetic fibers, which can replace steel fibers as structural fibers, was applied, while jute fibers and PVA fibers were applied as nonstructural fibers [16]. The mix using macro synthetic fibers had a higher compressive strength than the mixes using other reinforcement fibers. This is because the macro synthetic fiber is a structural fiber that can replace steel fiber and improve the mechanical performance of the concrete, which is reflected in the structural performance [16; 22; 23]. Thus, the mix with the macro synthetic fiber showed a slightly higher compressive strength than jute fiber and PVA fiber mixes, the main purpose of which was crack control, rather than structural performance.
## 3 LoRa and Localization Algorithms
### Overview of LoRa
#### 3.1.2 Signal-To-Noise Ratio (SNR)  
SNR is the ratio of transmitted signal powers to the unwanted signal i.e., noise power. It is preferred that the SNR is minimized to ensure that demodulation at the receiver end is straightforward and the signal can be decoded correctly. To enhance LoRa performance, it uses forward error correction (FEC) techniques and the spreading factor, thus allowing significant SNR improvements. In particular, the SNR range is between \(-20\) and \(+10\) dB. The received signal is less distorted if the range is around \(+10\) dB. LoRa has an SNR range of \(-7.5\) to \(-20\) dB.
## 4 Discussion
### Influence of Operating Parameters on the Adsorption of Lactic Acid onto GAC and Anionic Resins
#### 4.1.1 Effect of pH and Adsorbent Dose on the Lactic Acid Adsorption Mechanisms
The quaternary ammonium functional group can adsorb both molecular lactic acid and the lactate ion, however, further studies are required to demonstrate the competition between molecular lactic acid and the lactate ion. In addition, other organic components found in the real fermentation broth (e.g., acetic acid, alanine, glucose, tryptone, yeast extract etc.) are likely to impact the adsorption capacity and recovery efficiency due to composite uptake. The composite adsorption depends on therelative adsorption affinity, which is further linked to the polarity strength of the solute in the solution (e.g., acetic acid > ethanol > glycerol > glucose) [38]. In addition to pH, the adsorbent dose (Figure 1c) plays a major role in the absorption of lactic or organic acids. In this study, the increased adsorbent dose from 5% to 30% (\(w/v\)) has increased the lactic acid recovery efficiency by 10% for GAC and by 50% for both RLX425 and AMB400 resins (Figure 1c). However, an adsorbent dose of 10% (\(w/v\)) was found to be suitable for all the adsorbents at 303.15 K with an initial lactic acid concentration of 4.5 g/L (Figure 1c) as also reported by other authors [18; 22; 25]. [PERSON] et al. [18] studied the effect of adsorbent (Amberlite(r) IRA-67 and activated carbon) dose (2-20%, \(w/v\)) on the lactic acid recovery efficiency and found that 10% (\(w/v\)) was the most appropriate adsorbent dose at 298.15 K with an initial lactic acid concentration ranging between 6.7 and 7.0 g/L. [PERSON] et al. [25] also studied the effect of the adsorbent (Amberlite(r) IRA-67) dose (5-17.5%) on the lactic acid recovery efficiency and reported that 10% (\(w/v\)) is the best performing adsorbent dose for initial lactic acid concentrations varying between 21.0 and 99.5 g/L, at 323.15 K. In contrast, [PERSON] et al. [22] found that a 30% (\(w/v\)) activated carbon dose was suitable for lactic acid recovery from an initial lactic acid concentration of 50.0 g/L at 303.15 K when tested on adsorbent doses ranging between 10% and 50% (\(w/v\)).
## 3 Proposed Method for Defect Shape Estimation
### Regression  
To estimate the defect shape characteristics (e.g., depth) of new samples, we can find a regression function in the manifold space to represent the relationship between the embedded features \(y_{i}\) and the depth \(l_{i}\). A typical regression method is a polynomial model. The linear model is too simple, and third-order or higher-order models are prone to overfitting. Therefore, the quadratic model is a feasible choice; the estimated depth is \(\tilde{l}=w_{0}+w_{1}^{T}y+w_{2}^{T}y^{2}\), where \(w_{0}\), \(w_{1}\), and \(w_{2}\) are respectively the offset and first- and second-order coefficient vectors. They can be obtained by minimizing the distance between the true depth and estimated depth through least squares; that is, \(\min\limits_{w_{0},w_{1},w_{2}}||l-\tilde{l}||\). However, methods such as least squares minimize the empirical risk function that is sensitive to noise.  
Particularly with the small sample set in this study, these methods can easily lead to overfitting and poor generalization capacity.  
Support vector regression (SVR) [22] is preferable for its more robust regression of defect shape. SVR is a statistical learning theory that combines Vapnik Chervonenkis dimensions and minimizes structural risk. Given a training sample set \(\{(y_{i},l_{i})\}_{i=1}^{n}\), SVR finds a function \(f(\cdot)\) to make \(f(\boldsymbol{y})\) and \(\mathcal{L}\) as close as possible. Based on an \(\varepsilon\) - _insensitive_ loss function, SVR can tolerate a maximum of \(\varepsilon\) deviation between \(f(y_{i})\) and \(l_{i}\). Thus, SVR is less sensitive to outliers than are other methods and is suitable for our small sample set. To address the underfitting problem of small data set, we use nonlinear kernel to increase the SVR complexity and use more feature dimensions as well. Too many dimensions, on the contrary, may lead to overfit. This is discussed in Section 4.2.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 5 Discussion  
The results of this study highlight the superior performance of the proposed ELP-Net in the task of location prediction for wargaming. ELP-Net consistently outperformed the baseline models in both top 1 and top x accuracy. These results demonstrate the robustness and adaptability of ELP-Net in complex wargaming scenarios, providing a significant advancement in this field. Despite these strengths, there are still several areas for further improvement. Addressing these limitations will not only refine the performance of ELP-Net, but also extend its applicability to broader domains.  
* Generalization of the graph learning layer: The current approach of constructing a graph adjacency matrix for each time step of the situational graph using the graph learning layer shows limitations in generalizability. Future research could explore the impact of diverse graph topologies on model performance and develop adaptive graph construction methods that automatically select the optimal graph structure based on the data. This enhancement would improve the model applicability across diverse datasets.
* Optimization of the temporal convolution module: While employing multiple-sized dilated convolution filters aims to capture temporal patterns more comprehensively, it also increases model complexity. Future work could focus on refining the temporal convolution mechanism, potentially exploring alternative architectures or mechanisms that balance performance improvement with model simplicity.  
By addressing these limitations, we aim to further refine ELP-Net, ensuring it remains a scalable, flexible, and interpretable solution for location prediction in tactical and strategic wargames.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model Variant** & **Top 1** & **Top 3** & **Top 5** & **Top 10** & **Top 15** & **Top 50** & **Average** \\ \hline Base model & 0.251 & 0.365 & 0.430 & 0.539 & 0.610 & 0.817 & 0.502 \\ +Transformer & 0.288 & 0.399 & 0.468 & 0.588 & 0.667 & 0.872 & 0.547 \\ +GNN & 0.291 & 0.394 & 0.462 & 0.579 & 0.677 & 0.867 & 0.545 \\ Full model & 0.302 & 0.421 & 0.493 & 0.613 & 0.691 & 0.876 & 0.566 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of different modules on the top-x prediction accuracy and average performance.
## 3 Results and Discussion
### Single-Factor Experimental Results and Analysis
#### 3.3.3 Effect of Initial Concentration of Fe/Mn on the Iron and Manganese Ion Removal
In conclusion, the results of this analysis revealed that the model could replace the real point of the test to analyze the results. Figure 6 illustrates the correlation between the predicted value and the actual value of the removal efficiency in the response surface model. The experimental data points  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Sum of Squares**} & \multicolumn{3}{c}{**Mean Square**} & \multicolumn{3}{c}{**F-Value**} & \multicolumn{3}{c}{\(p\)**-Value**} \\ \cline{2-11}  & **Fe2\({}^{\star}\)** & **Mn2\({}^{\star}\)** & **df** & **Fe2\({}^{\star}\)** & **Mn2\({}^{\star}\)** & **Fe2\({}^{\star}\)** & **Mn2\({}^{\star}\)** & **Fe2\({}^{\star}\)** & **Mn2\({}^{\star}\)** & **Mn2\({}^{\star}\)** \\ \hline Model & 43.87 & 197.39 & 9 & 4.84 & 21.93 & 279.12 & 30.02 & \(<\)0.0001 & \(<\)0.0001 & significant \\ A-pH & 17.79 & 14.69 & 1 & 17.79 & 14.69 & 1018.64 & 20.01 & \(<\)0.0001 & 0.0029 & \\ B-Dosage & 0.026 & 10.49 & 1 & 0.026 & 10.49 & 1.51 & 14.35 & 0.2582 & 0.0068 & \\ C-F/Mn & 3.34 & 0.14 & 1 & 3.34 & 0.14 & 191.30 & 0.219 & \(<\)0.0001 & 0.6800 & \\ AB & 1.54 & 2.27 & 1 & 1.54 & 2.27 & 88.04 & 3.10 & \(<\)0.0001 & 0.1217 & \\ AC & 0.66 & 1.97 & 1 & 0.66 & 1.97 & 38.03 & 2.70 & 0.0005 & 0.1442 & \\ BC & 0.10 & 22.90 & 1 & 0.10 & 22.90 & 5.86 & 31.34 & 0.0460 & 0.0008 & \\ A\({}^{2}\) & 8.52 & 42.09 & 1 & 8.52 & 42.09 & 488.01 & 57.61 & \(<\)0.0001 & 0.0001 & \\ B\({}^{2}\) & 3.96 & 52.52 & 1 & 3.96 & 52.52 & 226.95 & 71.88 & \(<\)0.0001 & \(<\)0 in the figure were more distributed on the straight line or on both sides of the straight line, indicating a good fit of the model. The summary of the fitted output report showed that the quadratic response model was suitable for explaining the relationship between the pollutant variables, so it could be used to analyze and optimize the effect of iron and manganese ions in water on the adsorption system.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 3 Directed Graphs and Distributed Low-Density Parity-Check Codes for Data Collection
### Cooperative Data Collection with Distributed LDPC Codes
#### 3.2.1 Distributed LDPC Codes for One-Round Collection
In addition, if channel codes are also used at \(N\) sources to protect the information data vector \(m\) during the first phase, they can be combined with the distributed LDPC code to further improve the performance.
## 3 Results and Discussion
### Effects of SGS Severity on Glottal Flow Dynamics, Vocal Fold Vibration, and Acoustics
#### 3.2.2. Vocal Fold Dynamics
It is interesting to note that the threshold of SGS area reduction for noticeable effect on vocal fold dynamics is 90%, while on flow rate, it is 75%, suggesting that the change of the flow rate associated with the SGS area reduction from 75% to 90% is likely due to the flow viscous loss across the SGS itself.
## 4 Experimental Validation and Analysis
### Point Cloud Denoising Method  
In order to verify the proposed point cloud noise filtering method, a 10% proportion of Gaussian noise and random noise are added to the dataset and the results of different methods are compared. The proposed method was compared with the commonly used Gaussian filter, mean filter, and median filter methods, and the comparison results are shown in Figure 5. From the figure, it can be seen that Gaussian filtering is less effective, and mean filtering and median filtering can filter out part of the noise, while the proposed method basically achieves effective filtering of all the noise points.  
Figure 3: Point cloud data resampling comparison results of different methods.  
Figure 4: Comparison of the alignment accuracy of different resampling methods.  
Figure 5: Comparison of point cloud denoising methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\Delta\phi_{x}\) (rad) & \(\Delta\phi_{y}\) (rad) & \(\Delta\phi_{z}\) (rad) & \(\Delta\text{T}_{x}\) (mm) & \(\Delta\text{T}_{y}\) (mm) & \(\Delta\text{T}_{z}\) (mm) \\ \hline Random & \(-0.0039\) & \(0.0088\) & \(-0.0075\) & \(-3.20\) & \(0.49\) & \(4.71\) \\ Grid & \(-0.0034\) & \(0.0108\) & \(-0.0070\) & \(3.21\) & \(0.34\) & \(5.13\) \\ Nonuniform & \(0.0030\) & \(0.0086\) & \(-0.0015\) & \(1.32\) & \(0.77\) & \(3.12\) \\ Proposed & \(-0.0018\) & \(-0.0023\) & \(-0.0041\) & \(-0.25\) & \(-0.25\) & \(0.65\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the alignment accuracy.  
In addition, a quantitative analysis of the effect of noise filtering was performed. Different proportions of noise were added to the dataset and the noise filtering rate was analyzed, and the results are shown in Figure 6. Noise percentage refers to the percentage of noise points to the total number of points in the point cloud. It can be seen that the filtering rate gradually decreases as the proportion of noise increases, but all of them exceed 84%. That is, more than 84% of the added noisy data points are effectively removed. This indicates that the proposed method has good robustness and can guarantee the effective noise filtering of the point cloud dataset.
# Improved Mandel System for Prefabricated Vertical Drain Installation: A Macro to Micro Analysis
## 4 Results and Discussion  
### Macromechanical Properties  
#### 4.1.1 Water Content Reduction Effect of Preloading  
This study examined the extent of the smear zone from the water content reduction 24 h after the initial water content of 135%. For this purpose, samples were collected vertically and horizontally from the chamber apparatus depth. The results from the three types of PVD are shown in Figure 9.  
Figure 8: Image analysis processing: (**a**) SEM image; (**b**) interpretation of pores and particles using clip studio plot; (**c**) ImageJ processing.  
Figure 9: The water content according to the depth of chamber due to vacuum preloading: (**a**) PVD type A; (**b**) PVD type B; (**c**) PVD type C.  
As a result, the water content in Type A at the 18.5 mm and 55.5 mm radial distances decreased vertically and horizontally by 21-37%.  
It is indicated that the radial distance of the PVD has a direct relationship with the water content. For Type B, a more uniform pattern was observed than Type A, with water content decreasing by 25-36% at 18.5 mm and 55.5 mm radial distances. On the other hand, the water content in Type C decreased by 30-47% at radial distances of 18.5 mm and 55.5 mm. The difference in Type C is that the water content significantly decreased around the middle of the chamber at 300 mm, with the lowest water content at 45% at the radial distance of 55 mm, resulting in a local buckling. However, the uniformity of the result shows that Type B was the least disturbed PVD among all three.  
From this study, it was found that the smallest smear zone occurred during Type B installation because of the effect of decreasing water content. Moreover, when the compressive load was generated and the sudden deflection changed the PVD structure, the effects from buckling and migrating soil particles were small. Therefore, the differences in PVD shape influenced the soil disturbance during installation with the mandrel, consequently decreasing the water content.  
For a PVD that works ideally, the water content should decrease more in the area smear and transition zone, or near the PVD, compared with the undisturbed zone. However, there are times when water content does not significantly change at a given depth in all radial locations as expected, implying reduced performance of the PVD. The average water content in the transition area of the extended smear zone as a function of the radial distance due to the effect of the mandrel is shown in Figure 10.  
The average water content in the area close to the PVD was increased. The indication of reduced water content (\(r_{s}\)) at 55.5 mm radial distance near the PVD clarifies that the inner smear zone (s) at 18.55 mm had nonuniform discharging water content. Therefore, the results were in accordance with the analysis of the PVD depth. However, analyzing the water content due to vacuum preloading proved that the effect on soil surrounding the PVD differed at each measured height and radial distance.
# Accurate Algorithms for Spatial Operations on the Spheroid in a Spatial Database Management System
## 3 Validation of Results
### Tests  
In order to check the geodesic intersection algorithms in a quantitative way we designed two tests to show the deviation in azimuth (Test A) and distance (Test B).