esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 2 Background
### Utilizing OSS-Fuzz Project  
OSS-Fuzz is a collection of more than 1000 open-source-based fuzzing projects initiated by Google. In numerous academic studies, OSS-Fuzz serves as an experimental baseline for evaluating the efficacy of coverage feedback-based fuzzing. Researchers can utilize OSS-Fuzz to conduct experiments on a variety of open-source fuzzing against real-world software (mostly libraries). In this paper, we utilize libFuzzer stubs in OSS-Fuzz that have actually found real-world bugs to justify our claims in research experiments.
# Current-Sensing Topology with Multi Resistors in Parallel and Its Protection Circuit
## 2 Materials and Methods
### Rectified Mean Magnitude Detection
#### 2.2.2 Dynamic Characteristics of Rectified Mean Detection
In order to evaluate the accuracy of ripple estimation Equations (40), (43) and (44), the relative error of ripple estimation is defined as:  
\[\gamma=\frac{{{V_{RA}}^{\prime}}-{V_{RA}}}{{V_{RA}}}\times 100\% \tag{45}\]  
Figure 12 shows the relative estimation errors for the three waveforms. As shown in Figure 12, for both sine and triangular waves, the maximum relative errors of estimated ripple amplitude are less than 1%. For periodic pulse waves, the maximum relative errors depend on duty cycle and all are less than 6%. The estimated ripple amplitude would be used for determination of the hysteresis ratio in a comparison circuit. As shown in the results section, the hysteresis ratios must be higher than the ripple ratio to eliminate protection hiccups. The hysteresis ratios could be set as 1.06 times the calculated value to ignore the ripple estimation error. In Equations (40), (43) and (44), the ripple amplitudes are all inversely proportional to the normalized frequency. That means the ripple amplitude is at a maximum while \(\Omega=10\). It should be mentioned that the parameter \(A\) in Equations (40), (43) and (44) means the peak value of input waveforms, but the hysteresis voltage is calculated based on the rectified mean value of these waveforms. So the transformation between the peak value  
Figure 12: The relative error of ripple estimation equations for input as: (**a**) Sine. (**b**) Triangular. (**c**) Periodic pulse. and the rectified mean value according to Table 1 is necessary. Define ripple ratio \(R_{RP}\) as the ratio of estimated ripple amplitude \(V_{RA}{}^{\prime}\) to rectified mean value \(X_{RMS}\). The ripple ratio can be calculated with Equation (46) for sine, triangular and periodic pulse waveforms, respectively, from top to bottom. \[R_{RP}=\left\{ \begin{array}{c}\frac{0.33}{\Omega}\\ \frac{0.394}{\Omega}\\ \frac{0.524}{D\Omega}\left(-2.985D^{2}+2.985D+0.00075\right)\end{array} \right. \tag{46}\]  
Define hysteresis ratio \(\delta_{H}\) as the ratio of half the comparator hysteresis voltage to the initial threshold voltage. For these waveforms, if the hysteresis ratio of the comparison circuit is set higher than any ripple ratio calculated via Equation (46) at \(\Omega=10\), the repeated switching issue could be eliminated.
## 4 Conclusions  
The range-frequency domain interference structure in the shadow zone caused by the ocean bottom of the high-sound speed sediment was analyzed. We discovered that the interference structure could be depicted by a single SB (BS) grazing angle combined with the interferometric structure, the reflection coefficient of the layered ocean bottom, if the depth of launch and reception makes the grazing angles of the sea surface-bottom reflection (SB) and sea bottom-surface reflection (BS) the same. In this regard, we provided a preliminary explanation by the ray theory.  
Then, we proposed to invert geoacoustic parameters by the range-frequency domain interference fringes in the shadow zone caused by the ocean bottom of the high-sound speed sediment. Compared to the interference period of the ocean bottom reflection coefficient, a more concentrated inversion result of the sound speed and thickness of the sedimentary layer could be obtained by the matching of the spatial position of the interference fringes. At the same time, we proposed a fringe extraction method based on the maximum between-class variance method. The results of this paper showed that the error of the extraction method had small error and met inversion requirements.  
In the simulation environment, we linearly inverted the sound speed and thickness of the sediment by the matching of interference fringe spatial position combined with the grid search method, and compared them with the multi-dimensional inversion results of the genetic algorithm. Both the grid search method and the genetic algorithm inversion results showed that the inversion results of the sound speed and thickness of the sediment by matching the spatial position of the interference fringes were accurate, and the error was small. Focusing on the inversion of the sound speed and thickness of the sediment, the cost function proposed in this paper combined with the grid search method could quickly obtain a comparatively accurate (compared to the multi-dimensional optimization process) of the sound speed and thickness of the sediment. The future research for us is to verify our findings by real experimental data.  
Conceptualization, Z.L.; methodology, Z.L.; software, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, C.H. and M.Z.; funding acquisition, C.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 12004414).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
# Quasi-Uniform Density Non-Solid Infill Strategy for Axisymmetric Non-Planar Additive Manufacturing
## 3 Results and Discussion
### Comparison of Initial Seed Methods  
Although the barrel-based domains mainly relied on one initial seed approach each (EBNS for \(\Omega_{1}\), ERE for \(\Omega_{2}\)), the hourglass domain \(\Omega_{3}\) served to compare the three methods in a controlled manner: Euclidean blue noise sampling, Euclidean repulsion energy, and geodesic blue noise sampling, aiming to distribute \(N=204\) points. In all trials, 50 iterations of GRE optimization were performed after the initial seed. Regarding runtimes, the procedure utilizing EBNS elapsed 2425.2 [s], the ERE-based procedure elapsed 3407.9 [s], and the GBNS-based procedure elapsed 6123.8 [s]. On the other hand, the final standard deviations after GRE were \(\sigma^{opt}_{\Omega_{3}EENS}=0.340\) [mm], \(\sigma^{opt}_{\Omega_{3}ERE}=0.284\) [mm], and \(\sigma^{opt}_{\Omega_{3}GENS}=0.278\) [mm]. These results equip the user with the possibility of deciding with a more accurate result at the cost of runtime in the form of GBNS, a compromise solution in the form of ERE, or a fast solution at the expense of accuracy in the form of EBNS. Figure 9 further exemplifies this, showcasing the initial seed and final distributions for all three subcases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Case** & **Mean [mm]** & \multicolumn{2}{c}{**Standard**} \\  & & **Deviation [mm]** \\ \hline \(\Omega_{1}\) & EBNS initial seed & 0.740 & 0.106 \\  & GRE optimization & 0.848 & 0.030 \\ \hline \(\Omega_{2}\) & ERE initial seed & 0.937 & 0.560 \\  & GRE optimization & 1.693 & 0.159 \\ \hline \(\Omega_{3}\) & GRNS initial seed & 3.839 & 0.500 \\  & GRE optimization & 4.384 & 0.278 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean and standard deviation values of the distance with the two nearest neighbors for all three case studies presented.  
Figure 8: Distance with the \(2n=2\) nearest neighbors: (**a**) \(\Omega_{1}\), with EBNS initial seed; (**b**) \(\Omega_{2}\), with ERE initial seed; and (**c**) \(\Omega_{3}\), with GBNS initial seed.  
Figure 9: **Top row**: initial seed distributions in \(\Omega_{3}\) for all three algorithms; **bottom row**: their corresponding GRE distributions.  
These runtime results are valuable in the context of manufacturing offline process set-up. One of the goals of this offline process setup is to asynchronize the offline setup tasks from the operation proper, leaving no machine idle time. This allows for significant build cycle time savings and efficient manufacturing job shop planning. This asynchronization can be achieved through the present methodology, as the trajectory planning algorithm runtimes are in the order of magnitude of the build cycle time of a layer, especially in robot-based NPAM [58].
# Iron-Rich Magnetic Coal Fly Ash Particles Induce Apoptosis in Human Bronchial Cells
## 3 Results
### Physicochemical Characterisation of Size-Separated CFA  
On the basis of FE-SEM morphological characteristics (Figure 1) and the EDX spectra, we could identify the Svalbard CFA as fly ash (Si, Al, and O), ferrospheres (Fe, Al, and O), soot particles (carbon), mineral particles (S, O, Ca, Cl, and Na), and unidentified particles [13]. The FE-SEM imaging of the PM\({}_{2.5}\) (Figure 1A-C) fraction confirmed that the low air flow had separated out the smallest particles from the bulk CFA samples, with the majority of the particles being in the PM\({}_{2.5}\) range. The morphology did not show uniform CFA spheres throughout the sample, and the spheres present did not have smooth surfaces. Small PM\({}_{2.5}\) shards of glass were also found distributed throughout the PM\({}_{2.5}\) CFA, as well as the mineral gypsum (CaSO\({}_{4}\)) that was seen as clumps of feather-like crystals. The PM\({}_{10}\) CFA (Figure 1D-F) contained many different types of particle morphologies. Some particles were larger than the PM\({}_{10}\) size range, but the majority of the separation was within the threshold. There were four basic morphologies discovered in the Svalbard PM\({}_{10}\) fraction (Figure 1D). The bulk CFA (Figure 1G) samples consisted of a mixture of large shards of glass and large CFA spheres. The majority of these particles were larger than the PM\({}_{10}\) threshold, and therefore the bulk CFA was not used in any geochemical or biochemical analysis. Magnetite (Fe\({}_{3}\)O\({}_{4}\)), a magnetic iron oxide, was obtained to act as a positive control for the bioreactivity experiments. The SEM analysis confirmed that the magnetite was crushed (via a small geological pestle and mortar) from macroscopic crystals into PM\({}_{10}\)-sized particles (Figure 1H).  
Micro-characterisation of the smallest components of the CFA fractions was performed using HRTEM (Figure 2). The images revealed the crystalline structure of the carbon particles and showed that the iron-bearing ferrospheres were usually smaller than 2.5 \(\upmu\)m and that they were not crystalline. The calculated measurement of the carbon d-spacing between the lattices at the highest level of magnification was confirmed to be 3.4 A (Figure 2B). The large \"fibrous\" particles seen in the SEM images (Figure 1F) of the PM\({}_{10}\) CFA were also seen in the TEM (Figure 2C).  
Figure 1: Field emission scanning electron microscope (FE-SEM) micrographs of size-separated coal fly ash (CFA) particles collected from Svalbard. (**A**) Particulate matter (PM\({}_{2.5}\) CFA; (**B**) PM\({}_{2.5}\) CFA containing gypsum crystals; (**C**) PM\({}_{2.5}\) CFA with large glass structures; (**D**) PM\({}_{10}\) CFA containing many morphologies; (**E**) PM\({}_{10}\) CFA sphere; (**F**) PM\({}_{10}\) CFA with fibrous morphology; (**G**) bulk CFA; (**H**) crushed magnetite.
## 2 Methods
The neural networks used in the ENN and the SVMs were trained on half of the dataset, and then tested on the remaining half to give an indication of classification performance on unseen data. DFA was carried out using SPSS and calls classified to genus and species. The SVM classifier was trained and tested using Matlab. The kernel functions of the support vector machines used a radial basis function with a gamma parameter ranging from 0.005 to 0.03 in steps of 0.005, from 0.03 to 0.1 in steps of 0.01, and from 0.1 to 0.5 in steps of 0.1. For each value of gamma, the support vector machine was reinitialised 20-times to increase the chance of obtaining an optimal classifier. A support vector machine was trained for every target case in the dataset (each genus, or species) to classify an instance as either belonging to that case, or not. All classifiers were then combined and categorised each echolocation call as either belonging to a specific class (genus or species), or not. A call was classified correctly only if a single support vector machine classified it, and that classification was correct. Neural networks were trained and tested using Matlab. Neural networks used a sigmoidal activation function and varied in learning rate, momentum, number of hidden layers, and number of neurons per hidden layer used (Table 1). Because overtraining can lead to a decrease in accuracy [38], each neural network configuration recorded its best epoch and associated weights. Once 2500 epochs had been completed, the weights of the best epoch were saved to file. The best epoch was identified as the one that produced the highest minimum accuracy over all targets, and then the highest average accuracy over all targets. When all permutations of architectures were exhausted, the top 50 performing neural networks were refined by retraining with randomly initialised weights 30 more times, as the values of the initial weights affect the accuracy of the neural network [39]. Performance was measured by sorting the networks first by highest minimum classification rate, and then highest average classification rate. Two types of ENNs were generated. The first utilized the top performing 21 neural networks (of the 50 retrained networks) trained to classify calls to species. The second type used a hierarchy of neural networks [21]. The first layer of the hierarchy used an ensemble of the top 21 performing neural networks (of the 50 retrained networks trained to genus level) trained to classify calls only to genus. Within each genus, ensembles of 21 neural networks (of the 50 retrained networks trained for each species) were trained to classify calls only from species within a particular genus. The performance of the SVMs and ENNs were ranked first in order of minimum classification rate, and then by average classification rate. This prevented a classifier that performed poorly on one or two genera or species to be selected over one that performed slightly worse on average, but better in general.
## 4 Improved Algorithms
### I-RRT Algorithm
#### 4.3.5 Removing Redundant Nodes  
Due to the randomness of the classic RRT algorithm, the path may oscillate. Redundant nodes are removed to process the path from the starting point to the target point generated by the random tree [28]. Starting from the first node \(\mathsf{q}_{\text{init}}\), the subsequent path nodes are connected in turn, the second node is ignored, and the third node is connected. If the object does not collide with the obstacle, the second node on the path is deleted. If there is a collision, then the node is retained, the parent node of the collision point is used as the new evaluation node, and the above steps are repeated until the target point is reached. The final series of reserved nodes is saved into Path 2 and connected to obtain the path after removing redundant nodes, as shown in Figure 6. The collision here refers to the collision detection of the path mentioned in Section 4.3.1. Figure 7 shows the path diagram generated by the I-RRT algorithm. Algorithm 1 shows the pseudocode of the I-RRT algorithm.  
```
1.initialization
2.S\(\blacklozenge\)q\({}_{\text{init}}\);
3.while true do
4.q(rand)=random() or end();
5 ifq(rand) = random()
6.q(nearest)\(\blacklozenge\)Triage_nearest_list_index(node_list, q(rand));
7.dist1\(\blacklozenge\)Nearest distance(q(nearest),obstacles);
8.ifdist1 > expandDis
9.q(new)\(\blacklozenge\)Extend(expandDis,q(nearest), Direction Angle)
10.else
11.q(new)=virtual(Extend(expandDis,q(nearest),DirectionAngle)
12.dist2\(\blacklozenge\)Nearest_distance(q(new), obstacles)
13.ifdist1 > dist2
14.q(new)\(\blacklozenge\)Extend(expanddis,q(nearest),Direction Angle)
15.else
16.q(new)\(\blacklozenge\)Extend(expandDis,q(nearest),Direction Angle)
17.if collision_check(q(new))
18.Tree.add(q(new))
19.ifdist(q(new),q(end))< expandDis
20.returnGoal
21.else
22.continue
23.returnpath
24.path2\(\blacklozenge\)remove_redundant_nodes(path)
25.returnGraph
```  
**Algorithm 1**I-RRT
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 3 Results
Since most of the changes are marginal and difficult to be observed with the naked eye (except in very few occasions), a statistical analysis is performed both for the amplitude (Figure 8d) and phase (Figure 8h) of the reflection coefficients, and four statistical descriptors are computed: mean value, standard deviation, kurtosis and skewness. It is worth noting that most of the changes observed occur in the standard deviation and/or in the kurtosis of the observable (amplitude or phase), and this is very clear in some selected cases in the histograms, especially as a function of the rms wave height, and its damping. Figure 7: Zoom of LHCP reflected signal over calm water. Also, as a multi-path quality check, a density plot overlaid with a scatter plot with the uncalibrated direct signal in the x-axis (dBFs: dB full scale), and the calibrated reflectivity in the y-axis (dB) is presented in the b plots. In an ideal scenario the direct signal power should be constant (i.e., multi-path free), the scatter plot should be a vertical line showing the variability of the received power when the direct signal is constant, but actually, it is not completely. As said, these figures are a sanity check of the quality of the data. For example, in Figure 8b, the error induced by the direct signal in the reflected signal powers is estimated to be (robustfit of the scatter plots, Figure 8b) \(\sim\)1.18 dBFs/dBFs at RL, and  
Figure 8: Clean roughened water surface without capillary waves analysis. See text for description. -0.15 dBFs/dBFs at RR, which are negligible as compared to the variations of the received power (red and blue lines fluctuations wrt. green line, Figure 8a). The density plot of the phase (scatter plot not overlaid on top of it for clarity), Figure 8f must be interpreted as the correlation of the reflection coefficients at both polarizations which is linked to the instantaneous variations of the scattering coefficients at RL and RR polarizations. Ten different case studies are presented: (1) Clean Flat and Roughened Water Surface With/Without Capillary Waves, (2) Clean Water-Variation with Incidence Angle, (3) Bottles and Fixed Net, (4) Plastic Bottles, (5) Marine Litter (Nets), (6) Marine Litter (Food Wraps and Bags), (7) Marine Litter (Caps and Lids), (8) Straws, (9) Pellets, and (10) Styrofoam. These results are representative results for each case study, as in most cases, the experiment was repeated for consistency. In the following sections, the observables for a clean water surface, with/without waves/capillary waves are presented as the reference cases. Then, the results for the eight types of marine plastics are presented. Finally, Section 4 summarizes and discusses the average results for the different realizations performed.
## 4 Performance Evaluation
### Simulation Results and Discussion
#### 4.2.5 Performance Comparison and Analysis of Latency
In comparison, better latency performance is observed in fair PA than in fixed PA as the transmitted power increases.
# Sensory Texture and Mastication Physics of Multi-Phase Meat Products
## 4 Results and Discussion
### Sensory  
To get insights on how different amounts of BLS are sensorially perceived, 19 panelists were asked to rate the samples according to the attributes of hardness, fibrousness, juiciness, energy for breakdown, and overall acceptance on a scale from 0 to 10 (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Batter Like Substance**} & \multicolumn{2}{c}{**0\%**} & \multicolumn{2}{c}{**10\%**} & \multicolumn{2}{c}{**40\%**} & \multicolumn{2}{c}{**100\%**} \\ \cline{2-9}  & **Mean** & **S.D.** & **Mean** & **S.D.** & **Mean** & **S.D.** & **Mean** & **S.D.** \\ \hline Hardness & 4.24 \({}^{\text{a}}\) & 2.05 & 4.29 \({}^{\text{a}}\) & 1.57 & 4.62 \({}^{\text{a}}\) & 1.40 & 3.35 \({}^{\text{a}}\) & 2.41 \\ Fibrousness & 5.89 \({}^{\text{a}}\) & 1.26 & 5.90 \({}^{\text{a}}\) & 2.10 & 5.14 \({}^{\text{a}}\) & 1.55 & 2.51 \({}^{\text{b}}\) & 1.67 \\ Juiciness & 5.21 \({}^{\text{a}}\) & 1.60 & 4.65 \({}^{\text{a}}\) & 1.67 & 4.33 \({}^{\text{a}}\) & 1.53 & 2.90 \({}^{\text{b}}\) & 1.61 \\ Energy & 5.46 \({}^{\text{a}}\) & 1.93 & 5.23 \({}^{\text{a}}\) & 1.74 & 4.55 \({}^{\text{a}}\) & 1.85 & 2.89 \({}^{\text{b}}\) & 1.53 \\ Overall & 5.71 \({}^{\text{a}}\) & 1.75 & 5.17 \({}^{\text{a}}\) & 1.50 & 4.53 \({}^{\text{ab}}\) & 1.90 & 3.23 \({}^{\text{b}}\) & 1.78 \\ \hline \hline \end{tabular}  
* mean = mean value (\(n=19\)); S.D. = standard deviation; Superscript letters indicate significant differences according to a Tukey test (\(p<0.05\)).  
\end{table}
Table 2: Sensory scores for selected attributes on a scale from 0 to 10, with 5 as reference value. 0 = worst, 10 = best.  
Figure 3: Mechanistic illustration of the meat fibers (brown elongated structures) interacting with the binding BLS (purple matter). Captions in the upper left corner correspond to the amount of BLS.  
Figure 2: Hardness (N) and total work (J) of the first compression cycle from samples containing 0–100% BLS as determined by a two-cycle compression test to 75% deformation. Superscript letters (a,b) next to datapoints indicate significant differences according to a Tukey test (\(p<0.05\)).  
Four of the five tested sensory attributes are negatively responding to an increase of BLS. This goes along the previous found results (Figures 2 and 3) and was as before mentioned already described in the literature as effect of decreasing structured particles [5]. Only the hardness showed a different tendency, which was similar to the effect seen in the two-cycle compression test. Samples with 0-40% provoked the same perception with a slight tendency of higher hardness scores toward 40%. Plain batter was subjectively rated to be softer than the 40% sample; however not significantly on a level of \(p<0.05\). Subjective energy for mastication, juiciness, and fibrousness of the sample with 100% BLS showed significantly lower values (\(p<0.05\)) compared to samples containing a structured phase.