esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction
## 3 Agng-Nu
### Dynamic Response Deletion Mechanism  
For the basic GNG algorithm, when there are non-uniform input signals, that is, the input samples are concentrated in a certain area, the age of the neurons in this area increases faster, and the probability of being deleted will also increase. For the construction of the environment map of the tunnel section, the number of input samples can also express the information of the area. The denser the samples, the slower the cutting speed of the area and the increased hardness of the coal and rock, but the neurons in the area can still be planned in the cutting trajectory and belong to the feasible area. If they are deleted directly, this unstructured environmental feature will not be expressed.  
The connecting edge is used to describe the degree of closeness between two neuron nodes and has the attribute of age. The smaller the age, the closer it is. When the age exceeds the set maximum age value, it means that the connection edge has aged and needs to be deleted.  
In the deletion mechanism of the basic GNG algorithm, the value of the maximum age value \(max\_age\) is a fixed value. Due to the uneven relationship between the input samples, the age value of the nodes where the sample points are dense will quickly reach the maximum age value and be deleted, that is, where the samples are dense, the generated network nodes are fewer and the topology structure is sparse, which is inconsistent with the actual environment. The presented topology map is not satisfactory enough.  
To this end, a dynamic response deletion mechanism is proposed, which dynamically adjusts the maximum age value according to the actual area density, that is, the number of neuron neighbor nodes. The more neighbor nodes, the denser the neurons there, then the maximum age of the node is appropriately set to increase according to the actual situation, so that the speed of the age growth reaching the maximum value in a certain traversal cycle is slowed, and finally the purpose of protecting important nodes is realized. The maximum age setting in the dynamic response deletion mechanism can be represented by Formula (9).  
\[max\_age_{i}=max\_age\times\gamma^{N_{i}} \tag{9}\]  
where \(\gamma\) is adjustment parameter of the maximum age, and its value is greater and close to 1, while \(N_{i}\) is the number of neighbors connected to the \(i\)th neuron.  
Algorithm 1 shows the connecting edge adjustment method based on the dynamic response deletion mechanism. Lines 1 to 4 show that the age value of all edges connected to the winning node is increased by 1; Lines 5 to 10 show that if there is no connecting edge between the winning node and the second closest node, it will add the connecting edge and set the age value to 0; Lines 11 to 19 show the calculation of the maximum age value according to the dynamic response deletion mechanism, and deletion of the edge that exceeds the maximum age. For non-uniform input signals, the topology formed by this method can better represent environmental information.
# An Exploration of Robot-Mediated Tai Chi Exercise for Older Adults
## 3 Results
### Participants' Experience of the Robot-Led Tai Chi Exercise (from the Post-Exercise Survey)
#### 3.3.3 Feedback Regarding the Robotic Tai Chi  
All subjects gave positive feedback for question 8 (\"What was the best part of the exercise?\"), and most said the session was a fun experience and appreciated practicing the gentle, comfortable, and calming motions with soothing music. For questions 9 (\"What was the worst part of the exercise?\") and 10 (\"What improvement will make the robot-mediated exercise better?\"), the most common feedback was on the confusion about whether to mirror the robot's sides, which aligned with the feedback for question 3 (\"Could you follow the postures of the robot?\"). A few participants also reported that some postures were challenging, and that the robot's motion was unnatural, which aligned with the feedback regarding the difference between the robot's and a human's body structure (question 2). Some participants mentioned individual preferences, such as skipping the breaks, rearranging the movement breakdowns, and making the robot more intelligent, which aligned with the feedback regarding making the robot understand the user's speech and postures (questions 5 and 6).
## 2 Related Works
### Wpcn  
WPCN basically guarantees ENO because all of the devices in the cell use only the energy supplied by the HAP or power beacon. Thus, most studies on WPCNs have been focused on system optimization considering resource allocation [21], beamforming [22], cooperative communication [23], and full-duplex communication [24]. These topics have been investigated in a flat network structure where the wireless device directly transmits data to the HAP, while hierarchical networks like WSNs have not been considered.  
A few studies have addressed hierarchical structures in WPCNs. For instance, cluster-based cooperation in a WPCN was proposed in [25], where an HAP with multiple antennas exploited energy beamforming to focus more transferred power to the CH considering the performance to be limited by the high energy consumption of the CH. Then, joint optimization of the energy beamforming design, transmit times of all of the nodes, and transmission power of the CH was performed to maximize the minimum data rate achievable among all of the nodes (i.e., max-min throughput). In [26], a WPSN consisting of one HAP, a near cluster, and the corresponding far cluster was considered and a cluster cooperation concept was proposed. If the sensors in the near cluster do not have their own information to transmit, acting as relays, they can help the sensors in the far cluster forward information to the HAP in an amplify-and-forward manner. In [27], a WPSN was divided into several layers and the exact border of each layer was obtained in order to alleviate the doubly near-far effect, and the energy broadcasted by the HAP and radius of each layer were optimized jointly. Furthermore, a multi-hop WPCN based on user-cooperative multi-hop transmission was considered in [28] to improve the throughput fairness, and the max-min throughput was optimized by resource allocation (i.e., transmission power and time). In a WPCN environment, these previous studies considered the WET from the HAP without using SWIPT technology.
# Effect of Magnetic and Electrical Fields on Yield, Shelf Life and Quality of Fruits
## 2 Effect of Magnetic Fields and Electrical Fields on the Growth, Development, and Yield of Fruit
### Effect on Germination

Table 1: _Cont._the fruits, as well as an increase in the harvest of tomatoes per unit area [42; 43; 58]. [PERSON] et al. (2016) showed that MF pre-sowing treatment of melon seed enhanced germination by 14.6%, root and shoot lengths by 36.4 and 22.8%, vigour indices I and II by 40.6 and 28.8%, seedling fresh weight by 9.6%, and dry mass by 12.9%, leaf area by 50.0%, alpha amylase by 80.0%, protease by 92.5%, catalase by 36.5%, and chlorophyll \"a\" and \"b\" content by 50.4 and 80.9% when compared to control treatments [48]. Fruit yield per plant was also significantly influenced by magnetic treatment, with increases of 19.4-28.5% when compared with controls [42; 66; 67]. [PERSON] and [PERSON] (2000) confirmed higher mean fruit weight of tomatoes, as well as overall higher yields, in experiments with magnetically treated seeds of tomatoes [27]. Similar effects have been reported on strawberry yield parameters by [PERSON] and [PERSON] (2004) [68]. Treatment with a magnetic flux density (MFD) from 0.1 mT to 0.3 mT made sprouts grow faster, increased stem height and chlorophyll content, shortened flowering time, and resulted in heavier fruit weight than control treatments [50]. A study by [PERSON] (2012) reported that treatment with an MFD of 0.33 T increased plant height from 4.18 cm to 5.25 cm at four weeks of age [69]. Chlorophyll content is closely related to plant health, with higher chlorophyll content being associated with better plant health [70]. [PERSON] (2020) and [PERSON] et al. (2017) showed that the exposure of plants to a magnetic field increased the chlorophyll levels with low magnetic flux density and short duration of exposure, respectively [51; 57]. Given that chloroplasts have paramagnetic properties, the influence of magnetic field on plants increases its inner energy, which is distributed among the atoms causing accelerated metabolism [71]. The MF affected various plant characteristics, such as germination of seeds, root growth, rate of seedling growth, reproduction growth rate of the meristem cells, and quantity of chlorophyll [72]. The researchers have shown that the magnetic field changed the characteristics of the cell membrane, causing some changes in cell metabolism. The magnetic field affected various plant properties, such as gene expression, protein biosynthesis, and enzyme activities, and affected various plant functions in either plant organs or tissues [73; 74]. The mechanisms of action are represented in Figure 1.  
Figure 1: Magnetic field affecting the morphophysiological properties on a fruit plant with possible mechanisms.
# Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm
## 5 Conclusions  
In this paper, a novel and unified multi-objective optimal routing scheme for FANETs and E-VANETs (EnFVs) is proposed, which considers reliability, data rate, and residual energy as routing metrics. The proposed solution reduced the solution space for optimization algorithms and formulated an analytical optimal solution that offers faster optimization. Exhaustive simulation using a New York City traffic trace demonstrated that the proposed scheme achieved a 90% packet delivery ratio with longer route connectivity (3 \(\sim\) 5 s) and shorter hop distance, thus outperforming three existing state-of-the-art solutions.  
Conceptualization, M.A. and M.Z.; methodology, M.A.; software, M.A.; validation, M.A.; formal analysis, M.A.; investigation, M.A. and M.Z.; resources, M.Z.; data curation, M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A.; visualization, M.A.; supervision, M.Z.; project administration, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported and funded by the Saudi Arabian Cultural Mission (SACM). We acknowledge the constructive teachings by [PERSON] for evolutionary and genetics algorithm.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAVs & Unmanned aerial vehicles \\ EV & Electric vehicles \\ FANETs & Flying ad hoc networks \\ VANETs & Vehicular ad hoc networks \\ SUMO & Simulation for urban mobility \\ OSM & OpenStreetMap \\ EnFVs & electric and flying vehicles \\ \end{tabular}
## 4 Numerical Simulations and Discussion
### Realization of SAR Imaging  
In this section, two groups of simulation experiments are presented to verify the effectiveness of the proposed method. One investigates the SAR imaging of ideal point targets and the other considers the practical measured data. The simulation scenario is stated as Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Center Frequency & 9 GHz \\ Bandwidth & 1 GHz \\ Frequency Resolution & 0.01 GHz \\ Angle Aperture & 5\({}^{\circ}\) \\ Angle Resolution & 0.05\({}^{\circ}\) \\ Sampling Rate & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters setting for spotlight SAR.  
These two simulations demonstrate the imaging quality of the proposed method. For the low SNR, the imaging result of SVA shows severe ambiguity owing to the noise instability of this procedure. In comparison, the proposed algorithm performs much better, and the two matching pursuit algorithms also show good results. For the highest SNR, the imaging quality of all algorithms approach to the same.  
(2) Different number of scatterers  
In this simulation scenario, the SNR is fixed at 5 dB. All the scatterers are gathered around three target clumps and such scatter centers are distributed along the diagonal line of the scene.  
At the situation of scatterer number being 30, the SAR imaging realizations are shown in Figure 4. When the number of scatterers is set to 150, the SAR imaging realizations are shown in Figure 5.  
Figure 4: SAR imaging simulation for ideal point scatterer model (30 scatterers).  
Figure 5: SAR imaging simulation for ideal point scatterer model (150 scatterers).  
From the two groups of simulations above, it is indicated that for the small number of scatterers, all the methods are able to reconstruct target scene well. Compared with SVA, the sparse-reconstruction-based methods obtain more explicit images. It is worth noting that simulation results of the proposed method contain few noisy points. As the scatterer number increases, the scatterers imaging results appear fuzzy. Under the same scenario, the reconstructed scene of the proposed method achieve the most accurate images.
## 2 Methods
### Robust Inclination and Azimuth Scanning  
Firstly, the method of estimating the vector inclination of 3D seismic data based on a multi-window is used to estimate the apparent inclination of the seismic data volume along the Inline and Cross-line [42; 43; 44].  
Figure 1: Correlation seismic profiles. (**a**) Seismic profile with noise; (**b**) Dip scan stack pre-processed data profile.  
The quadratic surface is fitted by the similarity value of the pair of adjacent apparent inclination angles \(\left(\theta_{x},\theta_{y}\right)\), and the form is:  
\[\left(\theta_{x},\theta_{y}\right)=c_{\text{s}}(\theta_{x},\theta_{y})=a_{1} \theta_{x}^{2}+a_{2}\theta_{x}\theta_{y}+a_{3}\theta_{y}^{2}+a_{4}\theta_{x}+a _{5}\theta_{y}+a_{6} \tag{1}\]  
where \(c_{\text{s}}\) is the similarity, \(\theta_{x}\) is the apparent inline along the inline, and \(\theta_{y}\) is the apparent inline along the crossline.  
Solve the coefficient \(a_{j}\) using the method of least squares. The following equation is then used to obtain an estimate of the dip angle.  
\[\left\{\begin{array}{rl}\frac{\partial c_{\text{s}}\left(\theta_{x},\theta_ {y}\right)}{\partial\theta_{x}}&=2a_{1}\hat{\theta}_{x}+a_{2}\hat{\theta}_{y} +a_{4}=0\\ \frac{\partial c_{\text{s}}\left(\theta_{x},\theta_{y}\right)}{\partial\theta _{y}}&=a_{2}\hat{\theta}_{x}+2a_{3}\hat{\theta}_{y}+a_{5}=0\end{array}\right. \tag{2}\]  
In this formula \(\left(\hat{\theta}_{x},\hat{\theta}_{y}\right)\) is the apparent inline angle pair corresponding to the maximum value of the interpolated similar surface \(c_{s}\left(\theta_{x},\theta_{y}\right)\).  
Even if this similarity surface is interpolated, the analysis window may inadvertently span a fault that divides the reflective layer into two regions with different inclination angles. In order to obtain an improved vector inclination estimated value, the multi-analysis window structure is used [45]. In addition to the central window, a set of non-central and overlapping analysis windows are scanned. All windows contain analysis points of interest (Figure 2). Then the amplitude variance of the \(J\) seismic trace falling in the \(i\)-th window is calculated:  
\[\var\pi=\frac{1}{J-1}\sum_{j=1}^{J}\left(u_{ji}-\left\langle u_{i}\right\rangle \right)^{2} \tag{3}\]  
where \(\left\langle u_{i}\right\rangle\) represents the average value of \(u_{ji}\) in the \(i\)-th analysis window. The window with the smallest variance will be assumed to be the best representative of the continuous reflective layer.
## 3 Results and Discussion  
### Appearance of Thin Film  
SEM images and elemental analysis of the fabric samples before and after admicellar polymerization were obtained. Figure 1(a,b) shows fibers in the untreated cotton fabric samples, the surface of fibers is smooth with striations evident in places. It is devoid of polymer aggregates and any other agglomerations over the surface.  
Figure 2(a,b) shows fibers of treated cotton fabric with PA1 after modification by admicellar polymerization. Compared to untreated samples shown in Figure 1, striations are not visible while a coarse, bumpy appearance indicates formation of fluoropolymer on the fiber surface. Figure 2(c,d) shows fibers in the treated cotton fabric with PA2. In this case, striations are also not visible and fluoropolymer coating can be seen on the surface of fibers. With the longer chain fluorocarbon of PA2, the coating appears to be more evenly spread as a uniform layer. It is evident in both cases that the coatings are thin.  
Figure 1: Untreated fibers of interlock knit cotton.  
Figure 2: Interlock knit cotton fibers treated with PA1 (a,b) and PA2(c,d).  
The EDS spectrum of an untreated control sample (Figure 3a) does not exhibit a significant fluorine peak with the resulting elemental analysis for carbon, nitrogen, oxygen and fluorine indicating a fluorine content of only 0.33 atomic%. Carbon and oxygen, the main constituents of the cellulose substrate, give strong peaks. In treated fabrics (Figure 3(b,c)), distinct peaks are observed for fluorine along with those for carbon and oxygen. Fluorine content in PA1 was 6.89% while in PA2 was 10.53%, consistent with the longer fluorocarbon chains of the latter. The levels of carbon and oxygen observed reflect contributions from penetration of the electron beam through the film and/or regions where the fiber surface was not covered.  
Figure 3: Energy dispersive spectroscopy (EDS) spectrum of: **(a)** untreated fiber (b) treated fiber with PA1 (c) treated fiber with PA2.  
Figure 2: _Cont._
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 5 Results and Analysis
### Comparison with Previous Work  
The performance comparisons between the proposed 16 elements frequency RMS antenna and other designs are given in Table 5.  
It can be observed from this table that previous work did not specify how the dimensions of the metasurface were calculated.  
Figure 10: Retrieved effective constitutive parameters of the simulated results (**a**) Effective permeability (**b**) Effective permittivity.
# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 3 Proposed Methods
Finally, we can obtain the edge detection result \(B^{\prime}_{b}(x,y)\) by Equation (17). \[I_{q}(x,y)=\sum_{m=0}^{x}\sum_{n=0}^{y}B(m,n)\] (14) \[I_{b}(x,y)=I_{q}(x-1,y)+I_{q}(x,y-1)-I_{q}(x-1,y-1)+B(x,y)\] (15) \[S(x,y)=I_{q}(x+1,y+1)-I_{q}(x-1,y+1)-I_{q}(x+1,y-1)+I_{q}(x-1,y-1)\] (16) \[B^{\prime}_{b}(x,y)=\left\{ \begin{array}{ll}0,&\text{if }S(x,y)\leq 2\text{ or }S(x,y)\geq 7\\ 1,&\text{otherwise}\end{array} \right.\] (17)
* Output: Finally, according to the binary image edge detection result and the 3 MSB plane fused edge detection result, we combine these two results to obtain the ultimate edge detection result. As Equation (18) shows, the ultimate edge detection result \(U(x,y)\) is obtained by taking the union of the edge detection result of the binary image \(B^{\prime}_{b}(x,y)\) with the edge detection result of the 3 MSB planes \(B^{\prime}_{t}(x,y)\). \[U(x,y)=\left\{ \begin{array}{ll}1,&\text{if }B^{\prime}_{b}(x,y)=1\text{ or }B^{\prime}_{t}(x,y)=1\\ 0,&\text{otherwise}\end{array} \right.\] (18)  
In the proposed CIED method, we utilize 3 MSB planes to extract edge information. The 3 \(\times\) 3 neighborhood processing is performed for each plane to obtain the edge detection results of each plane, and then the results of the 3 planes are compensated and fused to obtain the fused edge detection result. To further improve the clarity and the continuity of the edge line segments, we also employ the 3 \(\times\) 3 neighborhood to process the binary image converted from the grayscale image and thereby obtain the edge detection results. The final edge detection output is derived by merging the two edge detection results. With these steps, CIED is able to effectively extract the edges of medical images. In contrast to other edge detection approaches, the CIED process is founded on the bit plane, which solely comprises 0 or 1. The bit plane has a smaller amount of data and simpler arithmetic logic than dealing with grayscale values from 0 to 255. Consequently, the CIED not only improves computational performance but also reduces computational complexity.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation
### Description of The Scenarios
#### 7.1.1 Smart Office
For the evaluation, the simulation duration was set to \(T=\) 28,000 s (the duration of an average working day), and a parameter, \(\omega\), was defined to set the frequency at which messages were generated during the simulation. As an example, if \(\omega=60\) s, messages were created and sent to their destination every minute. Table 1 shows the different values of this parameter used in order to perform independent executions and compare the obtained results as a function of it. Figure 6: Smart office scenario.
# Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
## 5 Discussion and Conclusions  
The existing research on opportunistic routing mainly ignores three issues: (i) GPS information is inapplicable in marine applications; (ii) candidate selection algorithms only consider the forwarding advancements at the current hop, ignoring the influence of the position distribution of candidate nodes; and (iii) to mitigate the hidden terminal problem, the candidate set size is restricted. To solve the above problems and design green routing protocol, in this paper, we propose a path diversity improved strategy in candidate selection procedures. It can provide diverse paths for packets forwarding by taking the distribution of candidate nodes into account, mitigating the impact of routing void problem and improving packet forwarding efficiency. Based on the EEPA metric, we propose two effective location-free routing protocols, PICS and PRCS, for UWSNs. PICS uses two-hop neighbor information to make routing decisions and PRCS only needs one-hop neighbor information. Both PRCS and PICS protocols have good performance; in particular, PRCS protocol has better performance in terms of PDR and average energy consumption, while PICS protocol achieves lower end-to-end delay performance and better PDR performance when the number of network nodes is large.  
Based on the simulation above, we find the following. (i) The candidate location distribution and candidate set size are important to improve the packet delivery ratio. A larger candidate set size could bring more nodes and an effective candidate location distribution could supply diverse paths for forwarding. (ii) More candidate nodes will increase the end to end delay. On the contrary, fewer candidate nodes cannot always satisfy the packets delivery ratio. It is worth considering how to obtain a higher packets delivery ratio and lower end to end delay with fewer candidate nodes. Overall, the PRCS protocol presented in this paper is an effective solution. (iii) Neighbor discovery and information updating seriously affect network performance in sender-based OR protocols, especially in energy consumption. Our future work will focus on duty-cycled based opportunistic routing protocols to improve energy efficiency.  
[PERSON], [PERSON], and [PERSON] conceived the routing strategy; [PERSON] and [PERSON] conceived the candidates selection model; and [PERSON] performed the simulation and wrote the paper.  
This research was funded by National Natural Science Foundation of China, grant number (61571367, 61571365, 61771401, 61671386, and 61401364) and the National Key Research and Development Program of China, grant number (2016 YFC1400200).  
The authors declare no conflict of interest.