esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Under a low traffic environment, the low-load adaptive contention and sleep approach would work well in terms of reducing medium access latency and making the best use of channel capacity. Hence, during low traffic periods in a WBAN, adoption of such an approach instead of TDMA may yield a beneficial trade-off between energy and delay as well as efficient capacity-utilization [15]. Finally, in time-synchronized and superframe oriented TDMA protocols [3, 16, 17, 18] and in the IEEE 802.15.4, time-slots are assigned using control packets (_i.e._, beacons). Although so far TDMA is the best suited MAC strategy for WBAN, during low-rate traffic periods the overall achievable capacity utilization is drastically decreased with existing TDMA protocols. Moreover, the TDMA approach is mostly limited to perform in a WBAN with single-hop star topology, where extra energy costs are incurred for long range transmissions between the sensors and gateway. Conversely, cluster-based multi-hop communication [4, 19] could serve as a potential solution to overcome the energy related shortcomings of single-hop based TDMA protocols. Note that the multi-hop communication architecture in few recent studies [6, 7, 8] justify the potential use of such communication model for WBAN. Motivated by the observations described above, in this paper we design **A** Traffic **L**oad **A**ware **S**ensor (ATLAS) MAC protocol for collaborative body area sensor networks, more specifically for WBAN. The ATLAS protocol is a beacon-enabled, superframe oriented MAC protocol developed according to the IEEE 802.15.4 standard. This protocol efficiently and adaptively exploits the properties of contention access period (CAP), contention free period (CFP) and inactive period (IP) of the superframe of IEEE 802.15.4, while taking the traffic load-status of the network into account. The contributions of the ATLAS MAC protocol are as follows: (i) A new load adaptive complete medium access solution is introduced for WBAN in which the receiver-driven approach is used for the protocol operation and it provides better energy-efficiency, high capacity-utilization (_i.e._, throughput) and minimal delay; (ii) An innovative load estimation and differentiation technique is proposed that uses the network capacity as the most decisive parameter to justify the congestion (load) status of the network; (iii) The network model consists of a multi-hop communication pattern that is very new in terms of WBAN MAC design; (iv) Finally, the performance of ATLAS is evaluated through extensive simulations in ns-2 [20] and compared in detail with the existing schemes. The rest of the paper is organized as follows. Section 2 discusses relevant literature on existing WBAN MAC protocols. Section 3 introduces the problems with the exiting schemes and the motivation of this study. The preliminary design considerations are described in Sections 4 and 5 presents the detailed protocol operations of ATLAS. Section 6 describes performance evaluations by comparisons with existing schemes, and finally conclusions are offered in Section 7.
## 2 Starch Modification Processes
### Genetic Modifications  
Genetic modification of starch is a technique that involves transgenic technology. Genetic modification can be carried out using traditional plant breeding techniques or biotechnology. This technique uses the enzymes involved in starch biosynthesis [32]. Generally, plants, animals, and organisms, including bacteria and fungi, contain genes for enzymes that engage in starch biosynthesis. These genes can be used to produce genetically modified microorganisms, which produce those enzymes for starch modifications. For example, modified starch with amylose-free, high amylose content, or waxy rice starch consists of 0 to 3% of amylose can be produced via genetic modification processes [33].  
Amylose-free starch has widely been tested as water binders, viscosity builders, and texturizers, mainly in food and other industrial applications. However, this starch is less resistant to shear, acid, and high temperatures than native starch, forming cohesive pastes with extended cooking [32]. High-amylose starch in cereals is produced via a genetic mutation of the gene that encodes the starch-branching enzyme, known as the amylose extender (ae) mutant. This ae mutant lacks the starch branching enzyme, resulting in longer amylose chains. Starch with high amylose content is an essential precursor for developing polymer coatings that are resistant to enzymatic degradation. Moreover, these coatings can also be used in colon cancer targeted drug delivery systems [34].  
[PERSON] et al. [35] investigated the effect of breeding techniques in transgene-free progeny cassava plants. The authors reported that the potential use of genetically modified starch varieties in food applications. Furthermore, [PERSON] et al. [36] prepared genetically modified potatoes with high cellulose, amylose, and less pectin. Moreover, [PERSON] et al. [37] explored the effect of the transgenic technique on the bioavailable vitamin B6 in cassava starch.
## 3 Results and Discussion
### VOC Production Assay  
The mode of action of VOCs in inhibiting _B. cinerea_ was explored using the mouth-to-mouth assay. The results indicate that the VOCs produced by the yeasts inhibited the growth of _B. cinerea_, with inhibition levels varying among yeast strains (Figure 7). Notably, the _B. cinerea_ strain PPRI 30807 exhibited higher susceptibility (mean inhibition of 73%) to yeast VOCs than strain B05.10 (mean inhibition of 38%). This pattern differed from the dual-culture assay, where the inhibition of PPRI 30807 was less pronounced, suggesting that VOCs could be a primary mode of action against this particular strain.  
In the VOC assay trial, _P. kluyveri_ (Y64) demonstrated the highest inhibition, achieving 60%, 76%, and 100% inhibition against _B. cinerea_ B05.10, IWBT-FF1, and PPRI 30807, respectively (Figure 7). Unlike in the diffusible metabolite assay (Figure 5) where _P. kluyveri_ (Y64) exhibited lower inhibition, its stronger performance in the VOC assay points toward VOC production as its primary antagonistic mechanism. This observation aligns with findings by [PERSON] et al. (2018) who reported _P. kluyveri_ as an effective inhibitor of _B. cinerea_ growth under in vitro conditions, emphasising its reliance on VOCs for mould suppression.  
Other yeasts also demonstrated notable VOC-based inhibition. _Sulhomyces pyralidae_ (Y63) achieved inhibition rates of 32%, 55%, and 82% against strains B05.10, IWBT-FF1, and PPRI 30807, respectively, while _M. guilliermondii_ Y88 exhibited 29%, 47%, and 79% inhibition (Figure 7). Previous studies by [PERSON] et al. (2018) and [PERSON] et al. (2018) also reported the effective VOC-mediated inhibition of _B. cinerea_ by _S. pyralidae_ and _M. guilliermondii_, supporting the current findings. _Zygoascus hellenicus_ (Y89) showed moderate inhibition, with rates of 44%, 38%, and 58% against the three _B. cinerea_ strains, respectively (Figure 7), further illustrating the potential of VOC production as a biological control strategy against mould proliferation.
# Aerodynamic Modeling and Performance Analysis of Variable-Speed Coaxial Helicopter
## 4 Comparison of Required Power of Variable Speed and Variable Total Distance  
After proving the accuracy of the algorithm, this paper compares the required power of variable-speed coaxial helicopter and variable collective-pitch coaxial helicopter at different forward flight speeds, as shown in Figure 4. Among them, it is specified that the power of the variable-speed curve and variable total distance curve is the same in hovering. That is, the following are specified: the consistency of the rotor diameter, hovering rotor speed and hovering blade angle.  
As is demonstrated in Figure 4, the two curves intersect when the forward flight speed is 15.4 m/s. Before the intersection point, the required power for the variable-speed coaxial helicopter is lower than that of the variable-pitch coaxial helicopter. When the power difference between the two is the largest, the required power of a variable-speed coaxial helicopter is 89% that of a variable total-pitch coaxial helicopter. After the intersection point, the required power of a variable-speed coaxial helicopter is higher than that of a variable-pitch coaxial helicopter, and the difference increases gradually.  
It is worth noting that for the forward flight speed at the intersection of the two curves, the rotor speed with variable speed and variable total pitch corresponds to the hovering condition. It can be inferred that when the forward flight speed of the variable-speed coaxial helicopter increases, due to the characteristic that the rotor speed first decreases and then increases, the rotor type resistance first decreases and then increases, and the rotor efficiency first increases and then decreases, so that the variable-speed coaxial helicopter has a lower power demand near the cruise speed point than the variable-pitch coaxial helicopter, which improves the cruise flight time effectively, while the variable-pitch coaxial helicopter has lower required power at high speed.  
Since this property is inherent in a helicopter, it can be considered that the above characteristics are available for variable-speed coaxial helicopters and variable collective-pitch coaxial helicopters with the same rotor diameter, hovering rotor speed and hovering blade angle.  
Figure 4: The variation in required power to the forward speed between variable rotor speed coaxial helicopter and variable collective-pitch coaxial helicopter.
## 4 Experimental Evaluation
### Results and Analyses
#### 4.2.2 Graph Embedding Methods: Best Values of \(d\)  
Now, we apply ATP, BoostNE, DeepWalk, DWNS, graphGAN, Line, NERD, NetMF, and node2 vec to our five datasets to obtain the low-dimensional representation vectors of the nodes. Then, to compute the similarity between two nodes in a dataset, we apply Cosine to their corresponding vectors. In order to _carefully_ analyze the impact of the number of dimensions \(d\) in similarity computation of nodes, we set \(d\) to different values as 64, 128, 256, and 512. For _each_ possible combination of methods, datasets, and \(d\) values (e.g., ATP with the BlogCatalog dataset when \(d=64\)), we perform the experiment _five_ times and select the _best_ accuracy obtained among these five different executions as the _final_ accuracy for that combination. More specifically, we conducted \(900\) (\(\equiv 5\;\times\;9\;\times\;5\;\times\;4\)) different experiments. Finally, similar to the strategy taken in Section 4.2.1, in the case of each embedding method with a dataset, we find out the _best_ value of \(d\) for which the embedding method shows its _highest_ accuracy in similarity computation of nodes.  
Figures 4 and 5 illustrate the accuracy of embedding methods with different values of \(d\). The former figure shows the results with the BlogCatalog, Cora, and DBLP datasets, while the latter one shows the results with the TREC and Wikipedia datasets; as an example, BoostNE shows its highest accuracy when \(d\) is set as 256 and 128 with the BlogCatalog and TREC datasets, respectively. In these figures, we do not represent the precision metric due to the same reason as in Figure 3; the values of this metric with BlogCatalog, Cora, DBLP, TREC, and Wikipedia datasets for different values of \(d\) are represented in Tables 1-5 in Appendix A, respectively. As already noted in Section 4.1, we cannot apply graphGAN to the Cora and TREC datasets due to their large sizes. Table 3 summarizes the best value of \(d\) for all embedding methods with our datasets. Note that, hereafter, when we compare the effectiveness of embedding methods with those of similarity measures for a dataset, we consider the effectiveness of embedding methods _on their best values of \(d\)_ with that dataset; as an example, in the case of DeepWalk with the BlogCatalog dataset, we consider its effectiveness based on \(d=128\) according to Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **JacSim** & **JPRank** & **SimRank*** & **SimRank** \\ \hline BlogCatalog & 1 & 1 & 2 & 2 \\ Cora & 5 & 4 & 5 & 5 \\ DBLP & 3 & 3 & 6 & 3 \\ TREC & 2 & 2 & 2 & 1 \\ Wikipedia & 5 & 5 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best iterations of all similarity measures.  
Figure 4: Accuracy of embedding methods with BlogCatalog, Cora, and DBLP datasets for different values of \(d\).
# Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations
## 4 Near-Real-Time Global Plasma Bubble Monitoring
### Equatorial and Low-Latitude Ionospheric GNSS L-Band S4 Scintillation
However, the pattern may be more identical in higher solar activity conditions where bubbles often grow to higher altitudes. Moreover, there is also good agreement in the S4 pattern in Figure 7 with the seasonal and longitudinal patterns of irregularity distributions reported by [PERSON] et al. [115] by using F3/C measurements. Figure 7: The seasonal and annual variation of the initiation and the duration of the F7/C2 S4 EPB detections during August 2019 to February 2022.
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 5 Conclusions  
In this paper, we propose an all-to-one network PMFN for LFSR. To make full use of complementary information between the reference view and auxiliary views, we design a PFFB with three densely connected DMFMs. This block can implicitly deal with disparities among different views. Additionally, in this block, low-level and high-level features are incorporated to improve the SR performance. Moreover, we design an FEB to further refine the HR residual map to improve the SR reference view. Experimental results have demonstrated the superiority of our PMFN over other state-of-the-art methods.  
In future work, an adaptive receptive field network can be investigated to improve the accuracy of generated texture details. This network should adapt to different disparities, and have generalization in synthetic and real-world datasets.  
Conceptualization, W.Z.; methodology, W.Z., W.K., H.S. and Z.X.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z., W.K., H.S. and Z.X.; Funding acquisition, W.K., H.S. and Z.X. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ang** & **Dataset** & **Scale** & **PSNR** & **SSIM** & **Scale** & **PSNR** & **SSIM** \\ \hline \multirow{2}{*}{\(3\times 3\)} & STFgantry & \multirow{2}{*}{\(2\times\)} & 38.76 & 0.992 & \multirow{2}{*}{\(\times\)4} & 30.67 & 0.946 \\  & HCInew & & 36.57 & 0.975 & & 30.66 & 0.914 \\ \hline \multirow{2}{*}{\(5\times 5\)} & STFgantry & \multirow{2}{*}{\(2\times\)} & 39.33 & 0.993 & \multirow{2}{*}{\(\times\)4} & 30.97 & 0.947 \\  & HCInew & & 36.92 & 0.976 & & 30.93 & 0.917 \\ \hline \multirow{2}{*}{\(7\times 7\)} & STFgantry & \multirow{2}{*}{\(2\times\)} & **39.68** & **0.994** & \multirow{2}{*}{\(\times\)4} & **31.12** & **0.951** \\  & HCInew & & **37.07** & **0.978** & & **30.99** & **0.919** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative results of different angular resolutions on the STFgantry and HCInew using our PMFN for \(2\times\) and \(4\times\) LFSR.  
Figure 8: Different structures of the FFB. FFB1 uses a straight forward connection, FFB2 uses a dense connection without introducing input features, FFB3 uses a dense connection without multi-scale features, and FFB is the structure used in our network.  
Funding: This study was partially supported by the National Key R&D Program of China (2018 YFB2101100), the National Natural Science Foundation of China (61872025), the Science and Technology Development Fund, Macau SAR (0001/2018/AFJ) and the Open Fund of the State Key Laboratory of Software Development Environment (SKLSDE-2021 ZX-03).
## 5 Experimental Analysis
### Algorithm Performance
#### 5.2.3 Analysis of Algorithm Stability  
Figure 11 below assesses the stability performance of the algorithm across multiple iterations. The left box plot illustrates the distribution of the final objective function values corresponding to various weight combinations over 10 independent runs. It is evident from the figure that the configuration prioritizing demand coverage not only exhibits the lowest median, but also demonstrates a reduced box height, signifying that its results are both stable and of a high quality. In contrast, the configuration that emphasizes delivery time presents a higher box, indicating considerable fluctuations in its results. The bar chart on the right, which displays the mean and standard deviation, further corroborates that the configuration focusing on demand coverage has the smallest standard deviation, thereby confirming its stability advantage. Conversely, the configuration prioritizing delivery time reveals a larger standard deviation, suggesting that the quality of the solution is significantly influenced by initial values or other stochastic factors. In conjunction with the practical analysis, it is observed that, during the delivery process, ensuring a high delivery success rate often necessitates a more rigorous evaluation of effective delivery solutions. Consequently, solutions with a substantial demand coverage are more likely to be favored.
# Random Frequency Division Multiplexing
## 4 Rfdm-Mimo  
Let us consider the RFDM scheme in a multi-antenna scenario; Figure 2 shows the RFDM-MIMO scheme with the number of transmitting antennas \(N_{l}\) and receiving antennas \(N_{r}\). Modulated signal \(\mathbf{s}\) through \(\mathsf{S}/\mathsf{P}\) and RFDM modulation is divided into \(N_{l}\) segments, and \(\mathsf{CP}\) is added to each segment. After the transmit filter, signal \(x_{j}\) is obtained and transmitted through the channel at the \(j\)-th antenna, \(j=1,\ldots,N_{l}\). After discarding the \(\mathsf{CP}\), the received signal \(\overline{\mathbf{y}}\) can be rewritten as:  
\[\overline{\mathbf{y}}=\overline{\mathbf{H}}\overline{\mathbf{x}}+\overline{\mathbf{v}}, \tag{10}\]  
where \(\overline{\mathbf{y}}=[\mathbf{y}_{1}^{T},\ldots,\mathbf{y}_{N_{l}}^{T}]^{T}\in\mathbb{C}^ {NN_{l}\times 1}\), \(\overline{\mathbf{x}}=[\mathbf{x}_{1}^{T},\ldots,\mathbf{x}_{N_{l}}^{T}]^{T}\in\mathbb{C}^ {NN_{l}\times 1}\), \(\overline{\mathbf{H}}=[\mathbf{H}_{1}^{T},\ldots,\mathbf{H}_{N_{l}}^{T}]^{T}\in\mathbb{C} ^{NN_{l}\times NN_{l}}\), \(\overline{\mathbf{v}}=[\mathbf{v}_{1}^{T},\ldots,\mathbf{v}_{N_{l}}^{T}]^{T}\in\mathbb{C} ^{NN_{l}\times 1}\).  
At the receiver, the complete RFDM-MIMO detector is like this: (1) Estimate \(\hat{\mathbf{\hat{x}}}\) according to the channel information:  
\[\hat{\mathbf{\pi}}=\overline{\mathbf{H}}^{-1}\overline{\mathbf{y}}=\overline{\mathbf{x}}+ \overline{\mathbf{H}}^{-1}\overline{\mathbf{v}}. \tag{11}\]  
(2) Reconstruct the original signal \(\hat{\mathbf{s}}\) based on the pre-trained DNN, as shown in Figure 2.
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 2 The Methodology of the Work
### Theoretical Background
#### 2.1.4 Equations for \(R_{mp}\) and \(Ra_{mp}\) Quantities  
The new \(R_{mp}\) and \(Ra_{mp}\) parameters integrate/numerically integrate the complex influence of a set of selected cutting parameters on the mechanical state in the cut as well as on the topographic state of the final surface after the cut. In the case of \(R_{mp}\), this is the ratio of the currently selected cutting parameters to those calculated or graphically constructed (see below) for the neutral plane at the depth level \(a_{p0}\). These parameters \(f_{z0}\), \(v_{c0}\), \(a_{p0}\), \(Ra_{0}\) and others are actually material parameters. This means that \(R_{mp}\) and \(Ra_{mp}\) are actually ratios between the values of the given currently selected and the material parameters. Therefore, in fact, the derived quantities \(R_{mp}\) and \(Ra_{mp}\) can accurately reflect the instantaneous state of the mechanical tool-material interaction at contact, Equations (38)-(41):  
\[R_{mp}=\left(\frac{f_{z}}{f_{z0}}\cdot\frac{v_{c}}{v_{c0}}\cdot\frac{a_{p}}{a _{p0}}\right) \tag{38}\]  
\[R_{mp0}=\left(\frac{f_{z0}}{f_{z0}}\cdot\frac{v_{c0}}{v_{c0}}\cdot\frac{a_{p0 }}{a_{p0}}\right) \tag{39}\]  
\[R_{mp}=k_{mse}\cdot Ra_{0}\cdot\left(\frac{f_{z}}{f_{z0}}\cdot\frac{v_{c}}{v_{ c0}}\cdot\frac{a_{p}}{a_{p0}}\right) \tag{40}\]  
\[R_{mp}=\frac{R_{mpX}}{R_{mpX_{0}}}=\frac{v_{c}\cdot f\cdot a_{p}}{v_{c0}\cdot f _{0}\cdot a_{p0}}=\frac{R_{mpV}}{R_{mpV_{0}}} \tag{41}\]  
where \(R_{mpX}\) are the selected working parameters, \(R_{mpX_{0}}\) are the calculated material parameters on the neutral plane, \(R_{mpV}\) is the cut volume from the selected values and \(R_{mpV_{0}}\) is the cut volume from the calculated material values on the neutral plane. The stress-strain diagrams of the tool-material contact are shown in Figures 3 and 4.
# Partial Pre-Emphasis for Pluggable 400 G Short-Reach Coherent Systems
## 4 Experimental Setup and DSP
### Results and Discussion  
Although by reasonably reducing the peaking of the partial pre-emphasis filter, the SNR of the signal from the AWG is increased as discussed before, the reliance on the Rx equalizer to compensate for the remainder of the Tx frequency response and the Rx frequency response results in increased noise enhancement, especially for low-cost coherent systems with extremely bandlimited Tx. This could result in overall performance penalty that is experimentally characterized in this section.
## 2 A Proposed ASPC Approach for OCRs in a DG-Integrated Distribution Network
### Protection Coordination Algorithms
#### 2.4.3 Genetic Algorithm  
The application of genetic algorithm (GA) to the ASPC approach of OCRs in the DG-based DN is shown in Figure 8. The algorithm selects the optimal TDS setting and \(A\), \(B\) and \(C\) coefficients of protection characteristic curve. In general, the GA works in the process of biological selection, crossover, mutation and combination [98; 99]. Survival of the fittest among all feasible solutions in the binary form of genes (or string structures) is considered in the GA. For each generation, a new set of potential solutions is created by the fittest of the previous set of solutions. The control variables are represented in the string structures. The GA randomly initializes the solutions, then, the fitness evaluation of each solution is performed. The fitness function is the same as the objective function \(Z\) in Equation (2) of Section 2.2. The solution has the higher fitness which can be probably selected for a new generation; this procedure is also called the biological selection or reproduction. Crossover and mutation steps are applied to get an available range of feasible solutions. When the new generation is obtained, the algorithm will repeatedly work until the termination condition is satisfied. In Figure 8, the counter of generation, \(G\), is initially set to 1. The parameter \(I\) is defined as the counter of population, which will stop until getting the population size.  
Figure 7: A flow chart of hybrid PSO-GSA applied to the proposed ASPC approach of OCRs.