esa-sceva/satcom-chunk-collection huggingface dataset:
# Deposition Efficacy of Natural and Synthetic Antioxidants on Fabrics
## 3 Results and Discussion
### Antioxidant Activity on Fabrics  
In order to obtain smart textiles, we subsequently deposited the two pine tannins and the commercial antioxidants previously selected onto cotton fabric samples and investigated their maintenance of functional features, especially the antioxidant activity. For this purpose, each treated fabric was subjected an adapted ABTS**+** decolourisation assay. Results are summarised in Table 4. Antioxidant activities of cotton fabrics significantly increased after the antioxidant deposition treatments, and the lowest ant-oxidative capacity is shown for the reference fabric. This could be attributed to the retention of antioxidants on the treated fabric surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entry** & **Pine Tannin** & **Extraction** &
\begin{tabular}{c} **Anti-Ox Activity** \\ **[mg AAE per Mg Pine Tannin]****[a]** \\ \end{tabular} \\ \hline
1 & **PST-sW1** & water/2\%NaHSO\({}_{3}\) & 0.30, 0.40 [b], 0.40 [c] \\
2 & **PTT-AW1** & acetone/water & 0.05, 0.11 [b], 0.05 [d] \\
3 & BHT & — & 0.14 [b] \\  & octadecyl & & \\
4 & 3-(3,5-di-tert-butyl- & — & 0.03 [b] \\  & 4-hydroxyphenyl)propionate & & \\
5 & MtBHPP & — & 0.01 [b] \\ \hline \hline \end{tabular}  
* Determined by a standard ABTS-assay if not stated otherwise. The maximum semi-dispersion is adopted for the error estimation (\(\pm\)0.01).[b] Determined by a slightly modified ABTS-assay, using a starting aqueous ethanol (70%) sample solution to be run in the assay instead of the aqueous one.
* Determined by running in the modified ABTS-assay (started from an aqueous ethanol (70%) pine tannin) only the supernatants obtained after centrifugation at 5000 rpm for 1 min of the pine tannin starting liquid suspensions.
* Determined by running in the standard ABTS-assay only the supernatants obtained after centrifugation at 5000 rpm for 1 min of the pine tannin starting liquid suspensions.  
\end{table}
Table 3: Antioxidant capabilities of pine tannins and commercial synthetic antioxidant as reference.  
The data reveal that treated fabric samples, i.e., industrial treatment (Table 4, entries 1-4) and laboratory treatment, (Table 4, entry 5) in comparison to control-fabrics (Table 4, entries 6-8), display an antioxidant activity under all treatment conditions. Most importantly, this observed activity for all treated fabric samples is comparable in all cases except for **PST-sW1** industrially treated in the presence of HDL (Table 4, entry 2): this sample exhibits lower antioxidant activity.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 6 Open Issue and Future Direction  
### HetNets  
Future HetNets will introduce other sources of interference in addition to SI, complicating SI management and FD transmission implementation. This problem is increased in a multi-tier HetNet or multi-cell hierarchical system as the number of SCs increases. Furthermore, there are several sources of ICI, UE-UE interference, and BS-BS interference when the end-user has an FD transmission enabled with a reuse factor of one. In this situation, the management of radio resources becomes highly complex and difficult. However, a probability of increasing SE is possible. This can be realized by utilizing an effective power allocation and radio RA strategy. The practical balancing between the performance of the system and the SI management strategy performance is a promising new topic for future research [174].  
The solutions to minimize co- and cross-tier interference are critical to ensure the HetNets total performance. To this day, interference minimization is a difficult problem in the management of resources since it must maintain the throughput, spectra, and EE of the system while still maintaining a reasonable amount of complexity. This will be an attractive research field for investigating the trade-off between suppressing interference and allocating radio resources. Future users may have diverse requirements for a wide range of applications; consequently, it is critical to develop the appropriate regulations for accessibility, radio resources impact, and management of interference in multi-tier HetNets. Another important issue that must be taken into consideration in interference minimization approaches is to mitigate the number of signals overhead that include CSI to minimize the number of information exchanges between small and macro-BSs [51].
## 5 Conclusions  
In this paper, multiple pseudo Bessel beams can be effectively generated based on the method of generating an off-axis pseudo Bessel beam and the principle of superposition, and the beams have the advantages of both accurately controllable propagating directions and high efficiency. The metasurface for generating dual pseudo Bessel beams was designed, fabricated, and measured, and the simulated and experimentally measured normalized E-field distributions demonstrate that nearly all the radiated energy from the metasurface propagates along the designed directions. The full wave simulation results show that the dual beams have a high efficiency of 59.2% at a transmission distance of 400 mm from the metasurface, and the efficiency keeps relatively high in the non-diffracting area. The proposed method and device for generating multiple pseudo Bessel beams can be applied to multi-user WPT systems, wireless communication systems, and multi-objects radio frequency identification (RFID) systems with a large operating distance.  
[PERSON] and [PERSON] conceived the simulations and finished the paper writing; [PERSON] analyzed the metasurface and performed the experiments; [PERSON] provided the main instructions of the ideas. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Science and Technology Project of State Grid Corporation of China \"Research on Ten-Meter Level Microwave Radio Wireless Power Transmission Technology\".  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Statistical Analysis  
The experimental data were statistically analyzed using SPSS Statistics for Windows, Version 22.0 (SPSS Inc., Chicago, IL, USA). Distribution normalization was determined by the Shapiro-Wilk normality test. One-way variance analysis with the Bonferroni post-test was used for data with a normal distribution (>0.05), and the Kruskal-Wallis non-parametric test was used for data not distributed normally (<0.05). Two-way analyses of variance for repeated measures with the within-subject factors of time (pre, 30 min, 60 min) and group were used for the statistical evaluation. The effects of each treatment over time were analyzed by using one-way analyses of variance with the Bonferroni post-hoc test when the analyses of variance yielded a significant difference. Data were presented as the mean \(\pm\) standard error of the mean. A \(p\)-value of less than 0.05 was considered to indicate statistical significance.
## 2 Theoretical Background
### Waveguide with an Infinitesimally Thin Strip Placed Inside at Varying Locations
The ratios \(\Delta_{m,n}(0.01)/\Delta_{m,n}(0.005)\), \(\Delta_{m,n}(0.02)/\Delta_{m,n}(0.005)\) and \(\Delta_{m,n}(0.05)/\Delta_{m,n}(0.005)\) were computed for the seven modes displayed in Table 8 and found to be in very good agreement with the ratios of the cross-sectional areas, being 2, 4 and 10, respectively: in fact, the arithmetic means of these ratios over the seven modes were 1.984, 3.967 and 10.02, respectively. Examining the data presented in Table 9 reveals that thick strips (with rounded-off corners) are associated with much more significant relative shifts \(\Delta_{m,n}\) than those shown in Table 8 for elliptic strips, at the same values of the parameter \(d/w\). This may be attributed to a significant difference in the cross sections of the elliptic strip and the thick strip: in the former case, the parameter \(d/w\) describes its maximum thickness, whereas in the latter, the parameter describes a thickness that is constant over most of the strip. Ignoring the effect of rounding the corners, the ratio \(S_{tc}/S_{es}\) approximately equals \(4/\pi(\approx 1.273)\). The simplistic idea of accounting for the difference by multiplication by this ratio in general fails; the shape of the inner conductor and its location influences the values of the shifts \(\Delta_{m,n}\) by more than the increase in the cross-sectional area of the thick strip over the elliptic strip. On the one hand, the arithmetic mean of the ratios \(\Delta_{m,n}(0.02)/\Delta_{m,n}(0.01)\) computed over the seven modes displayed in Table 9 equals 1.923 and this roughly corresponds to the doubling of the cross-section area (1.923). However, after increasing the parameter \(d/w\) to 0.05, the ratios \(\Delta_{m,n}(0.05)/\Delta_{m,n}(0.02)\) produce such a spread in values that it renders senseless the use of the arithmetic mean in a providing a cross-sectional area factor in a way comparable that extracted from the ratios \(\Delta_{m,n}(0.02)/\Delta_{m,n}(0.01)\). We conclude that the problems studied in this section are multi-parametric and requires comprehensive investigation, especially in addressing some practical problems.
# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 2 Experiment 1: YOLO Detection Performance for Camouflaged Persons
### Results  
The green squares (bounding boxes) in Figure 2a represent persons found by YOLO. It is clear from the few random examples shown in Figure 2a that YOLO succeeds in finding most persons in both scenes. Only a few were not found (as illustrated by red arrows in Figure 2a). In general, YOLO is able to detect the persons, at least when they are presented nearby, and the detection probability (i.e., the confidence score) decreases with increasing distance (since the person scales with distance, resulting in a decreasing amount of detail).  
The detection probability heatmaps are illustrated in Figure 2b for the bush/desert and the urban environment (left and right panels, respectively). Figure 2c illustrates the mean detection probability (collapsed over the horizontal plane) as a function of the person height (i.e., distance of the person, since the person size is scaled with distance). It is clear from this illustration that the detection probability decreases with increasing distance (i.e., with decreasing object resolution or distinctness). YOLO did an excellent job in detecting the persons, even though they were rather tiny (see the green squares in Figure 2a). More specifically, a probability of 80% corresponds to an object size of about ~60 and ~40 pixels in the bush/desert and the urban environment, respectively (see Figure 2c). Therefore, in Experiment 1, we conclude that the person's camouflage suit was slightly better camouflaged in the urban environment than in the bush environment. This is probably due to the higher homogeneity of the bush background or the color pattern of the camouflage suit, which was visually closer to the colors in the urban environment than the colors in the bush/desert environment.
## 3 Experiments
### Training on Simulation Data  
The CNN-based approach aims to extract features from low-quality images, then mapping those feature vectors to corresponding high dimensional space of high-resolution images. Therefore, by training the network through minimizing the loss function of initial direct reconstruction image \(p_{i}^{*}\) and ground truth image \(p_{i}\), problems like blurring, noisy patches, and modeling error from traditional PA reconstruction algorithms can be significantly mitigated. The optimization rule is expressed as follows [77]:  
\[\underset{\Theta}{\arg\min}L\left(f\left(p_{i}^{*};\Theta\right)-p_{i}\right) \tag{9}\]  
where \(L\) denotes the loss function of training data pairs \(\left(p_{i}^{*},p_{i}\right).\) Parameter matrix \(\Theta\) is defined as the combination of weight matrix \(W\) and bias matrix \(B\), represented by \(\Theta\triangleq W\circ B\triangleq(w_{1}\circ b_{1})\circ(w_{2}\circ b_{2} )\circ\cdots\circ(w_{l},b_{l})\). Here, the substitute \(l\) denotes the layer of CNN.  
For our network framework, significantly blurred and inaccurate modeling TR-reconstructed calcaneus images with gray transforming \({p_{i}}^{*}\in\mathbb{R}^{1\times 512\times 512}\) were fed in as the input, and binarized micro-CT images \(p_{i}\in\mathbb{R}^{1\times 512\times 512}\) was taken as ground truth labels. Overall, 3800 collected micro-CT calcaneus scans were simulated to generate a set of pairs \(\left(p_{i}^{*},p_{i}\right).\) Depending on the proportion of 60%, 20% and 20%, the dataset was divided into training set (2280 pairs), evaluating set (760 pairs) and test set (760 pairs). The resolution of all images is 512 \(\times\) 512 pixels.  
We implement our experiments on PyTorch [78], using an Intel Xeon Gold 6130 CPU and a Nvidia Quadro P4000 GPU. The training is run with [PERSON] for 100 epochs, with a learning rate of 0.001 and batch size of 2.
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 2 Related Work  
### Grid-Based Distance Maps  
In the context of robotics and game AI, the grid-based DM is a popular spatial representation applied in navigation and motion planning tasks. The principal component of the recent approaches for constructing or reconstructing grid-based DMs is the well-known Brushfire algorithm [20]. Intuitively, Brushfire launches wavefronts to propagate changes of maximal clearance (i.e., changes caused by insertion or deletion of obstacle cells), updating distance values from the source of the change, and terminates when the change does not affect any more cells. Brushfire represents the OPEN list as a priority queue to incrementally record the affected cells and propagate the wavefronts. The priority of an element in the OPEN queue is determined by its newly updated distance and all these elements are popped up in increasing priorities. Sequentially, new cells which are adjacent to the popped one are tested, among which, newly updated cells will again be inserted into the OPEN list so that the propagation continues.  
[PERSON] et al. [6], in their fundamental work, proposed a dynamic version of Brushfire algorithm, Dynamic Brushfire, to incrementally update grid-based DMs by propagating two kinds of wavefronts named \"lower\" and \"raise\" which start at newly blocked or freed cells, respectively; therefore, the update can be constrained within local areas. However, the wavefronts launched by Dynamic Brushfire roughly accumulate 8-connected grid steps to approximate maximal clearance, which overestimates the true Euclidean distances and would possibly lead to either a collision risk or overly conservative movements. To this end, [PERSON] et al. [21] proposed a method to propagate obstacle locations rather than counts of the grid steps, which reduces the absolute overestimation error below an upper bound of 0.09 pixel units. In the method proposed by [PERSON] and [PERSON] [22], the shortest distance at which this propagation error can occur is 13 pixels, which yields a maximum relative error of 0.69%. Regarding propagating obstacle references, [PERSON] et al. proposed an approach to provide the location of the closest obstacle rather than just the distance to it, which can be appealing for collision check tasks [23]. Moreover, [PERSON] et al. extended their method to 3D by adding the possibility to limit the propagated distances to maintain online feasibility in large open spaces and outdoors as proposed by [PERSON] et al.  
Although these dynamic algorithms are fast and efficient for dealing with local changes, they just indiscriminately expand all the adjacent cells surrounding a currently processed cell, which results in a lot of redundant cell visits and scales up the size of the OPEN list and restricts the overall efficiency of the algorithm. We introduce the Canonical Ordering strategy in our work to prune the search space.
## 2 Overall Design of the Integrated System
### WSN Nodes Deployment  
What is a good network topology? Based on network connectivity and coverage, by adjusting the transmitting power of nodes and selecting some nodes as backbone to deal with data processing and transmission based on certain principles, one can optimize the network topology and extend the lifetime of the whole network.  
Due to the harsh environment and complex conditions of wireless communication in underground coal mines, higher requests are put forward on the stability and reliability of the WSN. According to the zonal structure of the coal mine tunnel [7; 8; 9], a kind of mesh WSN for underground coal mine is designed based on the ZigBee technology, as shown in Figure 2. In addition, the coal mine is where personnel can come to, so WSN nodes are deployed manually, which is helps optimize and manage the network.  
As shown in Figure 2, the WSN for an underground coal mine consists of two layers: the upper backbone network and the lower perception network. The backbone network consists of a sink node and routing nodes; they can communicate with each other directly within their coverage, and mainly gather and forward data. As the gateway between the WSNs and the CMS, the sink node is deployed near the CMS cable monitoring sub-station. According to the zonal structure of the coal mine tunnels, the routing nodes are deployed along the tunnel in two symmetrical lines. In this way, every routing node can communicate with multiple routing nodes directly. If a routing node is broken, communication  
Figure 2: Topology structure of the WSN for underground coal mines.  
can still go on by using another routing node for relaying, thus the robustness of wireless communication in WSN is ensured.  
The perception network consists of sensor nodes in charge of collecting environment information. They are deployed around the routing nodes and with the consideration of the environmental monitoring needs. Because the sensor node is a Reduced Function Device (RFD), it is designed only to communicate with the nearest routing node as its parent node. Besides, the transmission power of a sensor node is adjusted appropriately by using an adaptive power control mechanism. The transmission power is mainly decided by the Received Signal Strength Indication (RSSI) value between the sensor node and its parent node. By adjusting the transmission power of sensor nodes, not only is the power consumption of the sensor nodes reduced, but also the interference among neighboring nodes is reduced.
# Modeling and Analysis of Noise Emission Using Data from Flight Simulators
## 1 Introduction
This may prove to be a very efficient method, as all important data can be exported from the simulator in a convenient format straight to the appropriate modeling software. Furthermore, simulators allow for virtual flights to be conducted in very specific conditions manually selected by the user. This may include, for example, the atmospheric conditions, airport location, aircraft's mass, balance and take-off configuration, and more. It needs to be emphasized that the simulation only allows for acquiring data in regards to the aircraft's trajectory, configuration, and power settings, but the \"power-specific\" and \"distance-specific\" noise metric values are still acquired from the Noise-Power-Distance (NPD) relationships from aircraft's type database. These so-called NPD Tables are a standard way of expressing the base noise levels in almost every software for noise modeling. In practical noise assessments, the NPD data are subjected to various adjustments that allow for a further increase in the accuracy of noise calculations.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 4 Artificial Damage Detection and Quantification for Aluminum Alloy Plates
### Damage-Sensitive Feature: NNPE  
Once the embedding parameters are selected, all segments with the same embedding parameters are used to reconstruct the attractors. Then, the NNPE values for the ultrasonic responses of specimens (b)-(h) are calculated by comparing their attractors with the reference undamaged attractor reconstructed from the ultrasonic response of specimen (a).  
Several linear features, including the root mean square \(x_{rms}\), standard deviation, and shape factor were calculated for the eight plates' response data in addition to the NNPE values. These features are chosen not only for their ability to reflect the vibration amplitude and energy in the time domain but also for their power to represent the time series distribution of the responses [33]. To compare the performances of these features, all the calculation results are normalized by the undamaged value. The features can be calculated by the following equations:  
\[x_{rms}=\sqrt{\frac{\sum\limits_{n=1}^{N}{{{(x(n))}^{2}}}}{N}}, \tag{13}\]  
\[x_{std}=\sqrt{\frac{\sum\limits_{n=1}^{N}{{{(x(n)-x_{m})}^{2}}}}{N-1}},\text{ \emph{and}} \tag{14}\]  
\[x_{sf}=\frac{x_{rms}}{\frac{1}{N}\sum\limits_{n=1}^{N}{{{|x(n)|}}}} \tag{15}\]  
where \(x_{m}\) is the mean value of \(x(n)\).  
Figure 10 shows the values of each feature calculated from the dynamic responses for specimens (b)-(h). It is clear that: (1) when the damage width exceeded 0.4 mm, all the feature values were changing, which means that all the features had the ability to detect damage larger than 0.4 mm; (2) the proposed linear features were relatively insensitive to damage smaller than 0.4 mm, as evidenced by the tangent slopes of the linear features' plots that were close to zero when the damage was smaller than 0.4 mm; and (3) compared to the linear features, the plot of the NNPE had the steepest tangent slope when the damage width changed from 0 to 0.8 mm. The value of the tangent slope of the NNPE plot is almost 10 times larger than those of the others, especially when the damage width is smaller than 0.4 mm. This result reveals that the NNPE feature outperformed the other linear features and can be used to detect smaller damage.  
Figure 10: A comparison of the normalized features at different damage widths.