esa-sceva/satcom-chunk-collection huggingface dataset:
# The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review
## 2 Materials, Processes and Methods
### Path Planning and Building Strategies
However, it is important to state that the Young's modulus and yield strength at elevated temperatures need to be taken into account for better correlation. A deeper look at material property-oriented path planning reveals a multi-criteria issue which needs to be solved properly and in an efficient time period. One promising approach is the usage of a state-of-research solver for common (now WAAM-related) path planning problems as shown in [25] by [PERSON] et al. Here, a modern Hamiltonian solver-based approach integrates the cooling conditions into material property-orientated path planning. Aside from material property-orientated path planning, different filling and layer-overlapping strategies for larger parts are under investigation as well [50; 175]. Additive manufacturing opens the way to honeycomb metal structures for lightweight design in any industrial application [59]. [PERSON] et al. described a procedure to optimize the inner structure of WAAM honeycomb structures in detail and compared their modeling approach with experimental results for a simplified test piece [59]. Furthermore, they included a thermal conductivity model and path optimization in the path planning process, which led to a reduction in the parts' distortion of around 30%. The geometrical deviations have to be as low as possible, and some effort is being put toward development prediction models. [PERSON] et al. developed a theoretical model for the prediction of the width and height of multi-layer single-tack additive manufacturing [176]. They used the \(\mu\)-plasma transferred arc welding process for additive manufacturing. They reached a prediction error of 2.3% for the manufactured height. Aside from the path planning itself, incorrect movement and speed from the manufacturing equipment, especially for robots, have to be taken into account as well [177]. [PERSON] et al. found that for robotic-based additive manufacturing, the travel speed is inconsistent with the desired values in corner situations. This results in a greater deposition height and needs to be compensated, and they developed a strategy to do so [177]. Overall, path planning for additive manufacturing is considered a key factor for achieving the desired geometry and the desired material properties. Different approaches have been found to calculate an optimal path for the occurring deposition modes, thin-walled parts or full-body parts. Currently, the research leads to material property-oriented path planning for modern lightweight load-adapted product development.
## 4 Results
### Knowledge Base Distribution and Robustness Tests
#### 4.2.3 Power Consumption  
The test of battery consumption was implemented with an isolated sensor attached to a base station doing the awake process. In this scenario, the sensor broadcasts the SNR_AWAKE message; when the base station receives it, it sends a BST_SLEEP message because there are no pending queries for the sensor. When BST_SLEEP is received by the sensor, it sends the SNR_OK message and enters deep sleep mode. Therefore, with this test, the sensor has to send two messages and receive one, each 10 bytes long. The transmission power used is the maximum available for the sensor. The results are  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{6}{|c|}{**Experiment A**} & \multicolumn{6}{|c|}{**Experiment B**} \\ \hline First sequence & \multicolumn{2}{|c|}{Second sequence} & \multicolumn{2}{|c|}{First sequence} & \multicolumn{2}{|c|}{Second sequence} & \multicolumn{2}{|c|}{Test T} \\ \hline N. of Hops & KB & N. of Hops & KB & & N. of Hops & KB & N. of Hops & KB & \\ \hline
3 & 2 & 3 & 3 & + &  
shown in Table 5 and shows that communications have to be minimized due to the power consumed. The average value is around 0.006357%, so a sensor with full battery can perform about 15,700 cycles, which represents almost 10 days with cycles of 60 seconds.
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 4 Experimental Results
### PSNR Comparisons with Prior Works  
In this section, we compare the PSNR between [25; 26; 27] and the proposed method, as shown in Table 3. In this experiment, 6-bit authentication code is embedded in all methods, and \(T=6\) is used in the proposed method. As shown in the table, the PSNR of the proposed method is the highest compared to [25; 26; 27]. The averaged PSNR of our method is 42.36 dB, which is \(42.36-39.71=2.65\), \(42.36-40.97=1.39\) and \(42.36-40.35=2.01\) dB higher than [25; 26; 27], respectively. Moreover, if an image contains a larger ratio of smooth blocks, the improvement of PSNR obtained by our method is higher. For example, compared to [27], the improvement of Baboon is \(41.04-40.69=0.35\) dB, while Splash is \(43.33-40.13=3.20\) dB, reflecting the effectiveness of the proposed method.  
An additional 200 images of sized \(512\times 512\) obtained from BOWS-2 image database [35] are used to explore the applicability of the compared methods influenced by image smoothness. To better evaluate the performance, we sort 200 images according to the number of smooth blocks of images in the ascending order, as shown in Figure 6. The figure shows  
Figure 5: The tampered and detection results. (**a**) The tampered image; (**b**) The tampered region; (**c**) The coarse detection result; (**d**) The refined detection result.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Lena** & **Jet** & **Baboon** & **Tiffany** & **Sailboat** & **Splash** & **Peppers** & **House** & **Average** \\ \hline
[25] & 39.71 & 39.69 & 39.73 & 39.76 & 39.67 & 39.67 & 39.70 & 39.70 & 39.71 \\ \hline
[26] & 40.98 & 41.08 & 40.96 & 40.89 & 40.96 & 40.98 & 40.99 & 40.93 & 40.97 \\ \hline
[27] & 40.32 & 40.07 & 40.69 & 40.22 & 40.55 & 40.13 & 40.49 & 40.25 & 40.35 \\ \hline Proposed & 42.49 & 43.31 & 41.04 & 42.78 & 41.64 & 43.33 & 41.94 & 42.47 & 42.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PSNR comparisons with [25; 26; 27] (in dB).  
that when the number of smooth blocks is close to 0, the PSNR improvement of our method is not significant compared to [25; 26; 27]. However, as the number of smooth blocks increases, the improvement becomes more obvious, as shown in Figure 6. From the above analysis, the proposed method is more suitable for smooth images compared to complex ones.
## 3 Results and Discussion
### HA Concentrations  
The influence of HA on contaminant transformation has been widely studied in both natural and engineered systems [52; 59]. HA, a ubiquitous macromolecular organic matter in aquatic environments, can affect reaction rate and degradation efficiency in oxidative processes [59]. As shown in Figure 6a, low HA concentration (0.5 mM) did not impact TCE degradation. However, the higher HA concentrations (5-250 mM), TCE removal efficiency decreased markedly. This likely results from competitive consumption of KMnO\({}_{4}\) by HA, with HA being preferentially oxidized at elevated concentrations, thereby reducing KMnO\({}_{4}\) availability for TCE degradation.  
The effect of HA on carbon isotope fractionation was evaluated using the relationship between \(\varepsilon\) values and HA concentrations. As shown in Figure 6b, from \(-23.5\pm 0.6\%\) at 0.5 mM (similar to the blank, \(-24.4\pm 0.9\%\)) to \(-26.6\pm 1.1\%\) at 50 mM HA. This trend reflects the growing competition between HA and TCE for KMnO\({}_{4}\) as HA concentration increases. At low HA concentrations (0.5 mM), excess KMnO\({}_{4}\) can oxidize both HA and all of the TCE [51; 60]. However, at higher HA concentrations (5-50 mM), KMnO\({}_{4}\) is partially consumed by HA, altering the oxidation conditions and possibly the reaction pathway for TCE degradation. Figure 6c shows a clear relationship between the \(\varepsilon\) values and HA concentrations, indicating that representative \(\varepsilon\) values can be selected for use in situ KMnO\({}_{4}\)-based remediation. It may be caused by three aspects: 1. HA contains some oxidizing functional groups, which could degrade TCE [52]. 2. HA acts in a competitive role with  
Figure 5: **(a)** Effect of 0.2 M inorganic anions on the oxidative degradation of TCE in the KMnO\({}_{4}\) reaction. **(b)** The variation of Rayleigh plots of TCE with respect to 0.2 M inorganic ions. **(c)** The variation of \(\varepsilon\) values of KMnO\({}_{4}\) degradation of TCE at 0.2 M inorganic anions. **(d)** Effect of 0–2 M CCl^- on the oxidative degradation of TCE in the KMnO\({}_{4}\) reaction. **(e)** The \(\delta^{13}\)C values of TCE at 0–2 M CCl^- in the KMnO\({}_{4}\) reaction. **(f)** The variation of Rayleigh plots of TCE with respect to 0–2 M CCl^-. Experimental boundary conditions: [TCE]\({}_{0}\) = 75 mg/L and [KMnO\({}_{4}\)]\({}_{0}\) = 250 mg/L at initial suspension pH 8.  
TCE during KMnO\({}_{4}\) oxidizing [43], and TCE may react with the reaction products of HA, and the corresponding values may differ from those obtained by direct oxidation of TCE with KMnO\({}_{4}\), which may cause the \(\varepsilon\) values changing. 3. The molar ratio of KMnO\({}_{4}\)/TCE decreased with the reaction proceeding, which could result in the \(\varepsilon\) values declining, as we discussed in Section 3.1 in this study. Applying the Rayleigh fractionation equation with the appropriate \(\varepsilon\) values allows for accurate evaluation of TCE degradation in environments with varying HA concentrations.
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs
### Super Capacitor (Sc)  
Battery-powered UAVs are subject to high energy density, sluggish response and limited lifespan. On the other hand, FC-powered UAVs are subject to sluggish dynamic response, which can cause voltage instabilities. In such cases, super capacitor technology emerges as a promising alternative to UAVs with a faster energy storage capability. This is due to the fact that SCs are characterized by reduced voltage instability, high lifespan, low maintenance cost, charging tolerance, wide operating temperature range, low energy density, fast response and higher energy [64]. The SC offers various benefits in terms of reinforced power density, rapid response of energy management system (EMS) and supplying architecture. Several research studies have focused on integration of SCs into UAVs. In [65; 66], the authors carried out critical analysis of a hybrid UAV propulsion system containing FC, FC and battery. These studies indicate a good performance of SC on system load stability and dynamic response. Some studies [67; 68] dedicated to hybrid UAV models indicate a significant contribution of SC in absorbing power instability and supplying peak power. Table 3 summarizes a comparison of the main characteristics of SCs and batteries.  
Figure 8: The comparison of energy density and power density for different energy storage devices [63].
## 3 Case Study
### Comparison of Effects Before and After Improvement
#### 3.5.3 Computational Efficiency  
Considering the time-sensitive nature of shield tunneling operations, it is important to minimize the time required for surface settlement prediction. The computational time required for different numbers of iterations was evaluated, and the results are presented in Figure 12. The evaluation was performed on a Windows \(11\) system with an Intel Core i7-12700H processor.  
Figure 11: Comparison of the Informer algorithm before and after improvement across different timescales.  
Figure 12: Comparison of the computational time of the Informer algorithm before and after improvement.  
At 50 iterations, the original Informer algorithm required 891 s, while the improved Informer algorithm reduced this time to 618 s--a reduction of 273 s (30.64%). At 100 iterations, the time decreased from 1740 to 1309 s--a reduction of 431 s (24.77%). For 200 iterations, the time decreased from 3011 to 2570 s--a reduction of 441 s (14.65%). These results indicate that the improved Informer algorithm, which adopts soil layer classification and characterization, achieved a higher computational efficiency and reduced prediction time.  
The Table 5 shows the ablation study. The marginal MAE increase (4.66\(\rightarrow\)5.76) after dimensionality reduction stems from trade-offs between error distribution and overall accuracy: 1 Soil layer encoding slightly increased small errors (MAE\(\uparrow\)) due to simplified stratigraphic inputs but significantly improved pattern generalizability (R\({}^{2}\) from 0.80 to 0.82; see Figure 9). 2 Crucially, this encoding reduced memory usage by 27% without compromising critical settlement trends.
## 5 Fragility and Leakage Risk Assessment of the NCS
### Leakage Risk Assessment
#### 5.3.2 Total Probability of Failure  
In the analysis presented above, internal pressure is treated as a deterministic variable. In fact, internal pressure also involves uncertainty, and the degree of uncertainty is associated with factors such as the severity of the accident and the structural form and volume of the NCS. To conduct a comprehensive assessment of the leakage risk, it is necessary to calculate the total probability of failure for the NCS under internal pressure. It should be noted that, in this paper, this failure probability inherently represents the total probability of leakage for the NCS under LOCA conditions. In this case, the internal pressure needs to be treated as a random variable. When the probability distribution and statistical parameters of the internal pressure are known, the total probability of failure for the NCS can be expressed based on the structural reliability theory, as follows:  
\[P_{\text{fail}}=\iint\limits_{R<P}f_{R}(r)\cdot f_{P}(p)drdp=\int_{0}^{+\infty }F_{R}(p)\cdot f_{P}(p)dp \tag{15}\]  
where \(f_{R}(r)\) represents the probability density function of the pressure capacity of the NCS; \(f_{P}(p)\) represents the probability density function of the internal pressure, and the mean value and logarithmic standard deviation of the internal pressure of the NCS investigated in this paper are 0.663 MPa and 0.3, respectively.  
According to Equation (15), the total probability of failure for the NCS at different degrees of liner corrosion is calculated, and the statistical results are presented in Table 5. It is evident from the results that the total probability of failure for the NCS gradually increases as the corrosion intensifies, indicating that liner corrosion can significantly increase the leakage risk of the NCS under LOCA conditions. Generally, the investigated NCS can fulfill  
Figure 15: Probabilistic safety margins of the NCS at different degrees of liner corrosion.  
the probabilistic performance objective of a total probability of failure not exceeding 0.1 when liner corrosion (the maximum corrosion degree of 30%) occurs in the gusset region.
# A Simple Method to Convert Cellular Polymers into Auxetic Metamaterials
## 4 Conclusions  
A simple and green method was proposed based on vacuum combined with mechanical compression (VMC) to convert a standard polymer foam into an auxetic foam by generating a re-entrant cellular morphology. As an upcycling project, the initial material selected was LDPE foam.  
The minimum tensile Poisson ratios reached were \(-\)2.89, \(-\)2.55, \(-\)1.52 and \(-\)1.50 for initial foam densities of 16, 21, 30 and 36 kg/m\({}^{3}\), respectively. On the other hand, the minimum compressive Poisson ratios obtained were \(-\)0.65, \(-\)0.35, \(-\)0.55 and \(-\)0.34 for the same samples. Furthermore, the tensile mechanical properties of these auxetic foams were substantially increased compared in terms of Young's modulus (40 kPa), tensile strength absorption energy (136 kPa at a strain of 3%), energy dissipation (15.5 mJ/cm\({}^{3}\)) and damping capacity (11%) compared to the most negative PR auxetic foams, which had the lowest NPR values. Under compression, the Young modulus (2.54 kPa), strength absorption energy (17.6 kPa at a strain of 3%), energy dissipation (3.43 mJ/cm\({}^{3}\)) and damping capacity (19%) were also better than those of the original foams.  
Based on the analyses performed, the NPR values for these auxetic foams were lower, and they presented better mechanical performances compared to the current literature. These results showed that an environmentally benign process can be applied to upcycle single-use LDPE foams of low density. As most of the literature on auxetic foams has focused on PU, this work increases our knowledge on using LDPE.  
Finally, these auxetic foams can be applied as impact mitigation for defense applications, such as behind armor padding, as well as in the production of protective sports equipment. The main application of these materials is for protection against impact, i.e., dissipating and absorbing energy while reducing peak forces/accelerations and pressures. Typical examples include helmets, knee/elbow pads, etc. Another application would be protection against penetration, abrasion and laceration. These auxetic foams can become personal protective equipment and supports for joints, muscles and the skeleton [37]. Nevertheless, more work is under way to apply the process (VCM) to larger samples for a wider range of densities and for other polymers.  
Conceptualization, D.R. and X.Y.C.; methodology, X.Y.C.; validation, D.R.; formal analysis, X.Y.C.; investigation, X.Y.C.; resources, D.R.; data curation, D.R.; writing--original draft preparation, X.Y.C.; writing--review and editing, D.R. and R.S.U.; visualization, X.Y.C.; supervision, D.R.; project administration, D.R.; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovation for Defense Excellence and Security (IDEaS) program from the Department of National Defense (DND) of Canada, grant number CFPMN1-032.  
Not applicable.  
Data available upon request.  
The authors declare no conflict of interest.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 4 Experimental Setup  
In this section, different network scenario configurations used in the simulation tests for performance validation of the proposed e-RLRP are described. Different network topologies with different numbers of nodes, routes, traffic flows and network mobility conditions are considered. Firstly, the four network topologies used in the simulations are described. Later, two simulation scenarios are explained. Finally, the transmission rate in the scenarios are explained, and the simulation environment is described.
## 4 Proposed Inference Latency Prediction Approaches
### Inference Latency According to Image Size  
This section presents the relationship between image size and inference latency when using the RFCN and SSD-MobileNet models for object detection. In [21], an approach where the inference latency is also increased in proportion to the increase in the image size was considered. However, this approach does not apply to all applications, and an approach suitable for specific application characteristics should be considered.  
We measured the inference latency for images with various sizes to analyze the relationship between image size and inference time for the RFCN and SSD-MobileNet models. As shown in Figure 4, we can confirm that the relationship between image size and inference latency is uncorrelated, which shows that it is difficult to predict inference latency with image size alone in object detection.  
For this reason, the client cannot predict the inference latency required for object detection in the edge and cloud devices, leading to a problem in which it cannot determine where to execute the task, locally or remotely, on the edge and cloud. In other words, when exploiting object detection services in edge computing, the process of predicting inference latency at the edges and clouds should be additionally considered.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 2 Background and Related Works
### Related Works
[3] provided a theoretical model to evaluate the influence of the BAW-ARQ scheme on aggregation size and derived the access delay and the throughput under the saturated settings. In [9], an analytical model that conformed to the BAW-ARQ scheme was introduced to investigate the impact on saturation throughput. Although all the above proposals investigated the performances of MU transmissions, or the aggregation-enabled ARQ schemes from different perspectives, none of them analyzed the performance of aggregation-enabled ARQ schemes together with MU transmissions, considered the full utilization of the channel resource originally used for padding bits in a protocol-friendly way or improved the blocking problem when the aggregation window slides. Therefore, we propose a novel QoS-aware BP-ARQ scheme to overcome these problems for MU transmissions in IEEE802.11 ax WLANs.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 2 Related Work
### Spatial Queries in Euclidean Distance
#### 2.1.1 K-Nearest Neighbor Queries in Euclidean Distance  
The effective execution of nearest neighbor (NN) queries, which holds special significance in the field of geographic information systems (GIS) [6], is essential when considering the commonly used query type known as k-nearest neighbors (kNN). The kNN query involves finding the potential neighbors closest to a given query point [6; 7]. In retrieving points of interest (POIs) using k-nearest neighbors, the distance between the query point and the POIs is calculated by measuring the straight-line distance from the query point's coordinates to the coordinates of each POI. The resulting POIs are then ordered based on their distance from the query point, thereby enabling the selection of only the top k-ranked POIs from the results. To illustrate, Figure 2 depicts a map diagram with weighted lines representing the actual distance of the road. The distances are measured using the Euclidean distance method, as shown in Figure 3. As a result, the retrieved POIs from the Euclidean distance method would be P6, P2, and P7, as indicated in Figure 4.  
Figure 4: The result of kNN query using the Euclidean distance method.  
Figure 3: Red lines show the measurement of the distance using the Euclidean distance method.  
Figure 2: Road and locations with lines represent roads with nodes and the distance and locations.  
[PERSON] and [PERSON] [8] proposed an effective method for computing k-nearest neighbors. Their approach involves a hierarchical decomposition of design samples into disjoint subsets. To achieve this, they applied the branch and bound algorithm, which is a well-regarded tree search technique [9; 10; 11]. This algorithm is known for efficiently searching through the resultant groups.  
Expanding the scope of k-nearest neighbors (kNN) query processing, [PERSON] and [PERSON] [7] discussed the extension of k-nearest neighbors (kNN) query processing to include obstacles in Euclidean space, known as obstacle nearest neighbors (ONNs). ONNs involve constructing a visibility graph, representing possible paths around obstacles, and using a disk-based approach with a restriction method to process the kNN queries while considering obstacle distances efficiently. The method iteratively refines search spaces based on obstacle distances to optimize query processing.  
Additionally, various types of nearest neighbor queries have been studied, including aggregate nearest neighbor (ANN) [12], reverse nearest neighbor (RNN) [13], and group nearest neighbor (GNN) [14]. However, our study focuses explicitly on the k-nearest neighbor (kNN) queries.