esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Obtained Data and Their Discussion
### Micro-Hardness Characteristics  
The microhardness of the synthesized nanocomposites extensively depends on the particle size of theadditive nanoparticles and their concentrations. The increment in the weight percentages of B4C will increase the dislocation interlocking more than for TiC and then improve the microhardness. The microhardness of Al/B4C nanoparticles is higher than that of Al/TiC nanoparticles; hence, the B4C reinforcing nanoparticles create stronger bonds with Al particles than the TiC reinforcing nanoparticles, which then inhibit the movement of the dislocations and in turn increase the microhardness, as shown in Figure 8. Moreover, the microhardness values of Al/B4C and Al/TiC nanocomposites are normally affected by compacting pressure, sintering temperature, and the weight percent of B4C and TiC. The microhardness increases with increasing weight percents of B4C and TiC.  
Figure 6: Show wear rate vs. applied loading.  
Figure 7: Photomicrographs of the grooves for Al/TiC and Al/B4C nanocomposites at (6 N).  
This is due to the higher hardness of each B4C and TiC matrix than the Al matrix. Finally, the micro-hardness test is a valuable route to define the microstructure and mechanical properties of nanocomposites. As shown in Figure 8, the values of micro-hardness for Al/B4C nanocomposites and Al/TiC are 92 (HRC) and 87.4 (HRC), respectively.
## 7 Future Research Directions
### Safety Implications  
Robots can be a safer alternative to manual inspection methods, but they can also introduce new safety hazards. There is a significant gap in the research on assessing the safety implications of introducing autonomous robots to construction sites. As discussed in Section 6.6, there have been some studies on the risk assessment of UAVs in construction [266], but other types of robots pose different types of risks which need to be evaluated as well. While FAA regulates the use of UAVs [247], an overarching legal framework for all types of robots needs to be developed. Future research may assess the perceived risks and safety of other types of robots in order to provide regulatory guidance. Such regulatory guidance does not only provide a framework to mitigate risks from the use of robots, but also provides a framework to evaluate the risk for insurance and liability purposes.
## 2 Challenging Open Issues
### Other Open Issues
#### 2.2.5 Reliability  
Reliability in the data delivery in a network can be measured by Packet Delivery Ratio (PDR) and Bit Error Rate (BER). PDR represents the ratio of the number of packets received by the receiver to the number of packets generated by the sender, while BER represents the ratio of the number of error bits to the number of bits generated by the sender. Reliability is very important in medical applications. An error in data transmission from the sensor node to the sink node could be fatal and could lead to mistreatment. Thus, the routing protocol design must consider how to deliver the data as reliably as possible.
## 3 Results  
### Dataset Setup and Experimental Setup  
We use the data generated in Section 2.2 as the experimental dataset, which consists of the longitudinal pipeline, the transverse pipeline, the underground cavity, and no target, as shown in Table 3. Three-dimensional GPR image data are trimmed to 20 \(\times\) 40 \(\times\) 45 (channel, height, and width). The proportion of the training set and verification set is 4:1.  
In order to evaluate the performance of the proposed CNN model, this paper uses multiple control groups and compares the classification results with those of each control group with the same network structure and training method.  
The first control group is the 2D-CNN. AlexNet has been developed for the general classification of RGB images. Therefore, for 3-dimensional GPR images, the data of multiple acquisition channels can be equivalent to multiple color channels and sent into a 2D-CNN network, similar to processing RGB three-channel color images. The second control group is a CNN using the traditional 3D convolution kernel, denoted as 3D-CNN. The third control group is a CNN using the 3D depth-wise separable convolution block, denoted as DS-3D-CNN. The last group is a CNN with 3D depth-separable convolution blocks with dimension fusion proposed in this paper, denoted as DF-DS-3D-CNN.
## 4 Performance Analysis
### Simulations Results Corresponding to Network Radius
#### 4.4.2 Analysis of \(|\Delta D_{BA12}|\)  
Even as the network radius changes, the BS motion modeling remains unchanged, meaning the impact of BS motion on AUV-BS distance is maintained. Hence, irrespective of the expansion or contraction of the network radii, the observed pattern remains consistent: variability increases as the AUV traverses the overlapping region when the motion directions of BS1 and BS2 align, and this variability increases with increasing BS motion.  
To verify this, we analyzed \(|\Delta D_{BA12}|\) across different network radii. As shown in all figures in Figure 9, the pattern where \(|\Delta D_{BA12}|\) exhibits higher values during the period traversing the overlapping region remains consistent. Moreover, the maximum value of \(|\Delta D_{BA12}|\) (approximately 25 m) is also maintained regardless of the network radii, as illustrated in Figure 9a-d. This indicates that, while the modeling of BS motion results in variability in the x-, y-, and z-axis positions, the BS motion is not affected by changes in the network radius.  
Figure 8: \(D_{BA1F}\) according to time and network radii.  
The statistical analysis of \(|\Delta D_{BA12}|\) allows for more detailed examination of the impact of network radii. As shown in Table 6, an increase in network radii is associated with a decrease in both the mean and standard deviation of \(|\Delta D_{BA12}|\). This trend is attributed to a larger network radius enhancing the proportion of time periods near the start and end of the trajectory of an AUV, where \(|\Delta D_{BA12}|\) is close to \"0\" (indicating an increase in the tail of the \(|\Delta D_{BA12}|\) pattern), thereby reducing the overall mean and standard deviation. This result confirms that the network radius influences statistical measures such as the mean and standard deviation of the variability indicator \(|\Delta D_{BA12}|\), suggesting that an increase in network radius reduces the variability caused by BS motion.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
###### Abstract  
Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process.  
EEG; epilepsy; GARCH models; time series characterization Article
# Performance Analysis of RIS-Assisted SatComs Based on a ZFBF and Co-Phasing Scheme
## 2 System Model  
We present an RIS-assisted SatCom system composed of a multibeam satellite, a single Earth station (ES), \(K\) UEs with single antennas, and \(K\) UAVs each equipped with an RIS consisting of \(N\) reflecting elements. As illustrated in Figure 1, the UAVs operate as mobile reflectors between the satellite and the UEs. The satellite generates \(K\) spot beams with \(K\) feeds; each beam is fed by a single feed, and the satellite's payload is considered to be transparent. The UAVs reflect the incident signals from the satellite using their RISs, and the phases of the incident wave are adjusted by \(N\) reflecting elements. It is assumed that the ES assigns one appropriate RIS to each UE, which means one RIS is allocated per UE, and a dedicated wireless control link is utilized to deliver a control signal from the ES to each RIS for the information of the reflection phase shift. In summary, the system operates as follows: (i) a satellite generating \(K\) beams receives signals from the ES and transmits signals to \(K\) UAVs. Subsequently, (ii) each UAV transmits a signal to each designated user through \(N\) reflecting elements. In this configuration, (iii) the UE receives IBI from the satellite through the RIS and simultaneously receives interference from adjacent RISs, referred to as IRI.
## 4 Results
### Suggestion of Nonlinear Rotational Stiffness
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Statistics**} & \multicolumn{3}{c}{**K Value (kNm/rad)**} & \multicolumn{3}{c}{\(\boldsymbol{\beta}\) **Value (rad)**} \\ \cline{3-8}  & & **K1** & **K2** & **K3** & \(\boldsymbol{\beta}\)**1** & \(\boldsymbol{\beta}\)**2** & \(\boldsymbol{\beta}\)**3** \\ \hline \multirow{3}{*}{New-S} & Average & 26.354 & 6.453 & 2.110 & 0.018 & 0.081 & 0.176 \\  & Std. D & 7.241 & 1.037 & 0.661 & - & - & - \\  & CV & 0.275 & 0.161 & 0.313 & - & - & - \\ \hline \multirow{3}{*}{Reused-1} & Average & 23.594 & 4.350 & 1.402 & 0.022 & 0.096 & 0.149 \\  & Std. D & 2.514 & 1.263 & 0.763 & - & - & - \\  & CV & 0.107 & 0.290 & 0.544 & - & - & - \\ \hline \multirow{3}{*}{Reused-4} & Average & 21.263 & 4.060 & 1.200 & 0.021 & 0.059 & 0.220 \\  & Std. D & 5.114 & 1.236 & 0.399 & - & - & - \\  & CV & 0.241 & 0.304 & 0.332 & - & - & - \\ \hline \multirow{3}{*}{Reused-7} & Average & 17.922 & 3.643 & 1.506 & 0.029 & 0.082 & 0.178 \\  & Std. D & 4.305 & 1.440 & 0.436 & - & - & - \\  & CV & 0.240 & 0.395 & 0.290 & - & - & - \\ \hline \multirow{3}{*}{Proposal} & Average & 22.475 & 4.705 & 1.577 & 0.022 & 0.080 & 0.178 \\  & Std. D & 5.879 & 1.645 & 0.672 & - & - & - \\ \cline{1-1}  & CV & 0.262 & 0.350 & 0.426 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Suggested rotational stiffness parameter values in trilinear model for wedge joints.
## 3 Results
### Case Studies
Loss of frequency selectivity associated with CGL is reflected by the pronounced vertical structures shown in (b), which was due to a broadening of cochlear tuning. Acoustic amplification shown in (c) appeared to enable partial recovery of the /s/- and /t/-clusters, but the cochlear tuning remained broadened. The LAP(ac) was 67.5%. The LAPs of the hearing prosthesis options involving implantation (d, e) were larger, i.e., 82.4% and 74.3%, respectively, which reflects the fact that high-frequency AN firing rates triggered by electrical stimulation are more artificial/blurred than firing rates of unaided hearing or with acoustic amplification in this subject. Figure 6: AN firing rates with regard to fiber index and time. The subject showed moderate CGL. See (**a**) Normal hearing reference. (**b**) Unaided hearing, with hearing loss parameters (I\({}_{\text{f}}\), I\({}_{\text{s}}\), O\({}_{\text{f}}\), O\({}_{\text{s}}\), and N\({}_{\text{fib}}\)). (**c**) Sole acoustical amplification, with amplification parameters (L\({}_{\text{f}}\) and L\({}_{\text{s}}\)). (**d**) Sole electrical stimulation, with insertion depth of the CI. (**e**) Electrical stimulation combined with acoustic amplification, with insertion depth of the CI and amplification parameter L\({}_{\text{f}}\). LAP refers to the cost-adjusted loss of action potentials. Figure 7 shows results for a subject with moderate to severe CS. In particular, an average of 12.8 functional fibers with high spontaneous rates existed for each IHC, whereas synapses releasing into low and middle spontaneous rate fibers were damaged. In (b), the unaided scenario, the predicted LAP was 17.3%. Marginal improvement was predicted for sole acoustic amplification (LAP = 17.2%). For (e), i.e., electrical stimulation combined with acoustic amplification, the LAP was much larger, i.e., 57.7%, and for sole electrical stimulation (d), the LAP was even larger, i.e., 77.2%. Figure 8 summarizes for each of the reported cases the LAPs in a bar chart. The bar charts may reflect recommendation weights of particular hearing prosthesis options. Shorter bars reflect stronger recommendations, and the color of the shortest bar was made distinct. Such bar charts are suggested to be displayed to clinicians by a possible prospective decision support system.
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
## 3 Results and Discussion
Thus, it is possible to say that for an equal computational effort, our proposal presents a better performance or conversely, for achieving a predefined BER, our proposal requires fewer operations and memory locations than Fourier proposals. These reductions lead to a simplified hardware implementation, which can result in less power consumption and lower latency. The trade-off in the hardware implementation is reflected in extra memory locations, due to the need for storing all the \(M^{P}\) possible noiseless channel outputs \(g_{l}^{(k)}|\mathbf{b}_{l}\). Figure 2: Performance of the equalizers [15] and the proposed 5-MPFs with quadrature phase shift keying (QPSK) modulation scheme.
###### Abstract  
In order to improve the azimuth resolution beyond what monostatic synthetic aperture radar (SAR) can achieve in the forward-looking area, an asymmetric configuration bistatic SAR system and its imaging algorithm are proposed in this paper. The transmitter is mounted on a fixed platform in side-looking mode while the receiver moves along a nonlinear trajectory in forward-looking mode. Due to the high velocity and acceleration of the maneuvering platform in both along-track and height direction, the traditional algorithms are no longer applicable. In this paper, a new algorithm based on the high precise 2-D frequency spectrum is proposed, which takes high-order Taylor series expansion terms of the slant range into consideration. The proposed algorithm compensates high-order range-azimuth coupling terms to guarantee the focus accuracy in SAR imaging. The simulation results and error analysis validate the effectiveness of the proposed algorithm and the correctness of our analysis.  
2-D frequency spectrum; method of series reversion (MSR); SAR imaging; bistatic forward-looking SAR (BFL-SAR) +
Footnote †: journal: Sensors
## 3 Processes to Eliminate B-O LID  
Broadly speaking, two approaches can be used to reduce the extent of B-O related degradation throughout cell fabrication and avoid the drawbacks of using alternative silicon materials. Firstly, thermal processes performed at relatively high temperatures in the absence of light (or with low residual illumination intensities from the heating elements). Secondly, annealing processes with the deliberate addition of carrier injection from either illumination or an applied bias performed at lower temperatures. However, there are significant differences in the underlying mechanisms and the extent of degradation they can mitigate.  
A high-level diagrammatic representation of the B-O defect system is shown in Figure 1. Four states are shown, although, in reality, the system is much more complicated. State A represents the recombination-inactive but unstable defect precursor states. State B represents the recombination-active states of the B-O defect. State C represents the regenerated state of the B-O defect stable under subsequent illumination. State D represents the thermally annihilated state of the B-O defect precursor(s), presumably species of boron and oxygen. The transition from state A to B (degradation) represents the reduction of the minority carrier lifetime with exposure to illumination. The reverse reaction from state B to A (defect annihilation) represents the unstable recovery of lifetime with dark annealing. The transition from state B to state C (regeneration) is the stable recovery of lifetime with illuminated annealing at elevated temperatures. Hence this pathway for eliminating B-O related LID requires the formation of the defect for regeneration to take place. The reverse reaction from state C to B (destabilisation) is the de-passivation of the defect from dark annealing or illuminated annealing at excessively high temperatures. It should be noted that in several publications, this reaction is drawn from state C to A, often as a two-step reaction via state B. However, the kinetics can be explained by the destabilisation reaction occurring from state C to B, with an almost immediate annihilation of the defects (B to A) given the much higher reaction rate for defect annihilation [71]. The reaction from state A to state D (precursor annihilation) represents the thermal killing of defect precursors with high-temperature processing. This transition does not appear to require the pre-formation of the defects. The reverse reaction from state D to state A (precursor formation) is the thermal formation of the precursors at intermediate temperatures.  
Figure 1: Simplified diagrammatic representation of the boron-oxygen (B-O) defect system.
# Extraction of Dietary Fibers from Plant-Based Industry Waste: A Comprehensive Review
## 4 Dietary Fibers Extraction
### Cellulose Extraction  
Cellulose is the most abundant natural macromolecule in the world. As tree resources are decreasing, more attention is paid to the search for new natural cellulose resources and green extraction processes [81]. Usually, cellulose extraction is performed using alkali treatment, and it is an effective method for the removal of non-cellulosic components, but results in the production of pollutants [81; 82; 83; 84]. Water is an ecological solvent and can be used for the extraction of soluble dietary fibers. Subcritical water has a higher concentration of hydroxide and H\({}_{3}\)O\({}^{+}\) ions and can act as an acid or base catalyst precursor during hydrolysis. Also, organic compounds are more soluble due to their low dielectric constant and density of subcritical water [81]. Subcritical water can be applied to obtain pure cellulose because during subcritical water treatment hemicellulose gradually hydrolyzes at 180 \({}^{\circ}\)C, and with the increase in temperature levels, the depolymerization and condensation of lignin also occurs [81; 85]. During sesame hulling, high amounts of sesame hulls are produced which are typically used as domestic animal feed or discarded. These hulls contain significant amounts of cellulose and are valuable to the food industry. Subcritical water and alkali pretreatment were performed for the extraction of purified cellulose from the sesame hulls. The results indicated that the extraction yields were similar for both techniques while the crystallinity of cellulose obtained with the subcritical water pretreatment was significantly higher [81]. Rice straw is a by-product of rice production that includes leaf blades and sheaths, stems, and the remains of the panicle, and it is an abundant lignocellulosic material [34]. For the extraction of cellulose from rice straw, ultrasound, combined with the reflux heating method, was applied. In comparison with the alkaline process (29%), a higher yield was obtained with the combined method (37%), also resulting in a more hydrophilic cellulose with lower aggregation tendency. Both cellulose fibers had similar crystallinity, morphogenetic characteristics, thermal behavior, as well as similar performance after incorporation into a polymer matrix. This new combined method was four times faster than the alkaline process and is also an eco-friendlier process that does not require a toxic solvent [34]. Alkaline treatment for cellulose extraction from apple pomace was optimized using response surface methodology. The optimal conditions were 161.54 min for extraction time, 10.23% for the NaOH concentration, and 69.82 \({}^{\circ}\)C for the temperature. Under these conditions, the cellulose yield was 27.96%, the \(\alpha\)-cellulose content was 85.31% and the whiteness
## 3 Results
### Behavioral Results  
The overall mean accuracy (\(\pm\)SEM) in performing the modified, delayed match-to-sample task was 76.00 (\(\pm\)1.65) %. There was a significant main effect relative to the VWM capacity load, with \(F_{(1,31)}\) = 201.31, \(p\) < 0.001, and \(\eta\)p2 = 0.87. The accuracy in performing the task was significantly reduced at low (1) compared to high (4) VWM loads (87.83 vs. 64.17%). There was a significant two-way interaction VWM load x state depression group, with \(F_{(1,\,31)}\) = 4.23, \(p\) < 0.05, and np2 = 0.12. Post hoc pairwise comparisons revealed that this significant interaction was driven by the VWM experimental manipulation rather than the individual differences in state depression. Indeed, in the case of the HSD group, there was a reduction in accuracy comparing low and high VWM loads (89.04 vs. 61.95%, \(p\) < 0.001). A similar VWM load effect was found for LSD individuals (86.61 vs. 66.38%, \(p\) < 0.001). However, the mean accuracy in performing the task did not differ significantly comparing the HSD with LSD group at low (89.04 vs. 86.61%, \(p\) = 0.53) and high (61.95 vs. 66.38%, \(p\) = 0.24) VWM loads. There was also a significant two-way VWM load x distractor interaction to target vertical spatial compatibility, with \(F_{(1,\,31)}\) = 4.27, \(p\) < 0.05, and \(\eta\)p2 = 0.12. However, post hoc pairwise comparisons did not reveal a significant difference when comparing vertically spatially compatible to vertically spatially incompatible distractor-to-memory-probe conditions at both low (87.13 vs. 88.52%, \(p\) = 0.06) and high VWM loads (64.81 vs. 63.52%, \(p\) = 0.19). No other main effects or interactions were statistically significant.  
The overall mean correct response time (\(\pm\)SEM) in performing the voluntary task was 928.39 (\(\pm\)35.86) ms. There was a significant main effect relative to the VWM capacity load, with \(F_{(1,31)}\) = 82.40, \(p\) < 0.001, and \(\eta\)p2 = 0.73, revealing significantly faster responses at low (1) compared to high (4) VWM loads (851.70 vs. 1005.08 ms). There was a significant main effect regarding the distractor in targeting horizontal spatial compatibility, with \(F_{(1,\,31)}\) = 5.27, \(p\) < 0.05, and \(\eta\)p2 = 0.15. The mean correct response time was significantly faster for the same conditions compared to different distractors in targeting horizontal positions (922.41 vs. 934.37 ms), suggesting the presence of a spatial positive priming (SPP) effect. No other main effects or interactions were statistically significant. In particular, there was no significant difference in mean correct responses when comparing the HSD with LSD groups at low (837.99 vs. 865.40 ms, \(p\) = 0.73) and high (976.38 vs. 1033.78 ms, \(p\) = 0.42) VWM loads. The output file of the statistical analysis conducted on behavioral data can be found in the Supplementary Materials.
## 2 Conceptual Model for Coordinated Automatic Generation Control in a Multimachine Smart System
### Synchronous Generators in a Multimachine Smart System  
At the core of this model are multiple synchronous generators, which operate within a sophisticated smart system infrastructure. These generators are connected within a network that demands high adaptability and efficient resource utilization to meet both technical and economic objectives.