esa-sceva/satcom-chunk-collection huggingface dataset:
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 5 Conclusions  
The resource constrained nodes of WSNs make high data rate applications a challenging issue. In this paper, we proposed ABORt, a multichannel load balancing routing protocol that uses ACK-based control information dissemination. ABORt does not rely on a synchronization phase for network and routing information exchange. Hence, it avoids time and energy wastage in order to construct routes. It is based on a load balancing technique that helps avoid congestion in the network and a queue overflow avoidance mechanism that prevents packets from being dropped due to accumulation in packet queues. Performance evaluation using a Cooja simulator shows that ABORt is efficient in terms of delivery ratio, queue overflow, end-to-end delay and overhead compared to two single channel routing protocols  
As energy is a scarce resource in WSNs, we plan on adding a duty-cycle approach allowing nodes more time to sleep with ABORt compared to other protocols. Indeed, as results showed that ABORt achieves higher delivery ratios and lower delays, we expect an energy efficient performance when used on top of a duty cycle mechanism. We also plan on enhancing ABORt in order to support node integration during the network operational phase. Adding a node to an already operational multichannel network requires coordination for knowing which channel to use in order for newcomer nodes to communicate with the network.  
This research was conducted with the support of the European Regional Development Fund (FEDER) program of 2014-2020, the region council of Auvergne, and the Digital Trust Chair of the University of Auvergne.  
[PERSON] and [PERSON] conceived and designed ABORt. They also conceived and designed the simulation scenarios and analyzed the results. They both wrote the paper. [PERSON] implemented and executed the simulations.  
Figure 12: Overhead for 5 pkts/s/node.  
Figure 13: Overhead for 10 pkts/s/node.
# Annotated-VocalSet: A Singing Voice Dataset
## 2 Steps to Generate the Dataset
### Combining Extracted Notes with Ground Truth Scores  
After extracting the notes, they needed to be associated with the scores. The scores and lyrics of each file are available in the VocalSet dataset [6]. Therefore, the estimated notes and the scores were automatically combined by a software program created by this paper's authors. For each musical score, an array that held the notes' information, such as name and duration, were created in the code. Then the two lists, estimated notes and scores, were mixed. To mix them, the code iteratively walks through the lists, and when the notes are matched, that is notes on each list were either a rest or an articulated sound, they were combined. Finally, the following columns were added to the files in the \"extended\" directories.  
* _Ground truth Note name_: their format is a capital letter + [#/b] + octave number--for example, C4, D#5, or Bb4.
* _Ground Truth Frequency_: It includes the frequency of the note based on A4 = 440 Hz.
* _Ground Truth MIDI code_: the MIDI note number according to the ground truth note name.
* _Lyric_: if the file comes from an arpeggio, scale, or long-tone, this column includes the sung vowel. Otherwise, the syllable corresponding to the note according to the scripts provided in VocalSet [6] is used in this column.
* _Ground Truth Note duration name_: it includes the name of the note's duration. Its value is one from this list: Whole_note, Half_note, Dotted_Quarter_note, Quarter_note, Dotted_Eighth_note, Eighth_note, Eighth_note_triplet, Sixteenth_note, or Grace_note.
* _Ground Truth note duration_: the BPMs of two series of songs were indicated in VocalSet. The BPM of the songs marked as 'fast' is 330, and those marked'slow' is 60. Therefore, the ground truth notes' duration of these two categories only were annotated in this study.
* _Interval to the previous note_: this shows the number of semitones between the current note and the immediate previous note.
* _Interval to the following note_: this shows the number of semitones between the current note and the note immediately following it.
## 4 Discussion
### Strengths and Weaknesses of the Study  
Although a computer-aided decision system with an AUC of 0.83 (0.82, 0.84) cannot replace human interpretation, this accuracy level may be very useful in assisting radiologists with prompt a diagnosis and treatment. An automated detection system based on our proposed model has the advantages of analyzing the CT image's entire region with equal importance. This reflects in reducing the human errors related to missed readings on the whole region of the 3D image. Furthermore, small fractures are often hardly visible on CT images, and require multiple checks by the radiologists: an automated detection system can also be useful in this context.  
Strengths and Weaknesses in Relation to Other Studies, Discussing Particularly Any Differences in Results  
Although several authors have already investigated AI applications in the orthopedic field, the possibility to detect maxillofacial fractures in 3D images of injured patients using deep learning algorithms has not been explored yet. Even if in other studies we can find better results, for example, in terms of AUC-ROC (0.95 [8]), it must be taken into account the complexity of the region of interest, such as the splanchonceranium and the enormous variability of the fracture types that may be present in this anatomically complex district. It is important to remark that the algorithm should be intended as an aid to the radiologist in recognizing facial fractures, more as a second opinion, rather than an independent one.
# Failure Warning at the End of Service-Life of Lead-Acid Batteries for Backup Applications
###### Abstract  
The prediction of remaining useful life is an important function of battery management systems. Existing research typically focused on factors that determine the quantity of the remaining useful capacity, and are able to determine the remaining useful capacity several years before battery failure to counter hysteresis of variables of lead-acid batteries. These techniques are not suitable at the end of service-life for backup batteries. This paper proposes a linear-superposition-voltage-aging model with three improvements. First, the estimation of the deep-discharge of the proposed voltage model does not require the remaining useful capacity. Second, the internal resistance of the deep-discharge is predicted from the contacting resistance of electrochemical impedance spectroscopy. Third, a morphology correction factor of internal resistance is about to saturate at the end of battery service-life. The model accurately forecasts battery failure at the end of service-life in two groups of accelerated-aging experiments. The proposed method in this paper focuses on the factors that determine quality of remaining useful capacity to counter hysteresis of variables of lead-acid batteries and judge battery failure at the end of service-life.  
state of health; state of charge; electrochemical impedance spectroscopy; electrode utilization coefficient; morphology correction factor; internal resistance; charge-transfer resistance; circuit equivalent model; remaining useful life; end of service-life; battery management system; lead-acid battery
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 4 Experimental Results and Analysis
### Performance Test of Thermoelectric Module  
#### 4.2.1 Output Characteristics under Different Heat Dissipation Modes  
In order to increase the output voltage and power, Twenty thermoelectric plates coated with thermal grease on both sides constitute a TEG module. In the experiment, the peristaltic pump speed is set to 280 r/min, the corresponding flux of water is 1191 mL/min. At the same time, the temperature control system is used to ensure a stable temperature difference. The TEG system is subjected to experiments employing three different cooling methods: natural air cooling, fan cooling, and water cooling. Output characteristics of these cooling modes are compared and analyzed. Under the premise that the temperature of the hot end is 150 \({}^{\circ}\)C and the load resistance is 50 Ohms, and the data is recorded every 10 s. The data are shown in Table 1 below.  
When the water cooling heat dissipation is used for the experiment, the inlet water temperature of the aluminum alloy deflector is 30 \({}^{\circ}\)C, and the outlet water temperature is 45.5 \({}^{\circ}\)C. It can be seen that the output power is basically stable at all times under the three heat dissipation modes. The water cooling heat dissipation mode exhibits the highest  
\begin{table}
\begin{tabular}{c c c c} \hline
**Heat-Dissipating Method** & **Power/W (T = 10 S)** & **Power/W (T = 20 S)** & **Power/W (T = 30 S)** \\ \hline Water cooling & 8.712 & 8.715 & 8.706 \\ \hline Fan cooling & 8.526 & 8.522 & 8.517 \\ \hline natural air cooling & 8.286 & 8.281 & 8.283 \\ \hline \end{tabular}
\end{table}
Table 1: Output data under different heat dissipation modes.  
Figure 11: Diagram of open circuit voltage at different peristaltic pump speed.  
output power, while the natural air cooling heat dissipation mode has the lowest output power. Therefore, under the same external conditions, the effect of water cooling is the best.
## 3 Results and Discussion
### Porous Carbons Characterization
#### 3.1.4 FTIR Analysis  
Figure 2 depicts the 400-4000 cm\({}^{-1}\) infrared spectral region of the carbon samples.  
The FTIR spectrum of organ nut shells [29] showed strong bands attributed to stretching vibrations of the hydroxyl groups (O-H) of phenols/alcohols, strong bands associated with the stretching of carbonyl groups (C=O) and of the C-O stretch vibration of the alcohols or ethers. The profile of the FTIR spectra of the activated carbon samples reveals, as expected, and a considerable reduction in the intensity or even the disappearance of some bands ascribed to several functional groups removed during the high temperatures of the activation process. In the case of the biochar, it is possible to observe a few bands with relevant intensities, most of them related to the aromatization of the carbon matrix during carbonization: bands at 1580 and 1430 cm\({}^{-1}\) assigned to C=C aromatic ring stretch and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ANS a** & **Char** & **ACK** & **CC a** \\ \hline C (wt.\%) & 47.8 & 74.6 & 69.9 & 86.3 \\ H (wt.\%) & 6.54 & 3.44 & 1.74 & 0.47 \\ N (wt.\%) & 0.33 & 0.30 & 0.39 & \textless{}0.20 \\ S (wt.\%) & \textless{}0.03 & \textless{}0.03 & 0.20 & 0.57 \\ O \({}^{\circ}\)b (wt.\%) & 45.2 & 21.4 & 26.9 & 6.76 \\ Ashes (wt.\%) & 0.065 & 0.26 & 0.95 & 5.70 \\ pH\({}_{\text{PZC}}\) & - & - & 7.0 & 9.1 \\ \hline \hline \end{tabular} a
[29]; b Calculated by difference (wt. %): \(100-(\text{C}+\text{H}+\text{N}+\text{S}+\text{Ash})\).  
\end{table}
Table 3: Elemental analysis and ash content of the ANS, Biochar, ACK, and CC samples.  
bands at 750 and 815 cm\({}^{-1}\) associated with the aromatic C-H out-of-plane bend [38]. There are common bands among the activated carbon samples--namely, the band at 1385 cm\({}^{-1}\) attributed to the O-H bend frequency of phenol, the bands at 1622-1633 cm\({}^{-1}\) associated with C=O groups and, also, to C=C aromatic ring stretching vibrations, and the bands at 1132-1180 cm\({}^{-1}\), usually assigned to the C-O stretch vibration of alcohols or ethers.
# Dynamic Response Analysis of RC Frame against Progressive Collapse Based on Orthogonal Test
## 3 Application of Orthogonal Test Method
### Orthogonal Test Method  
#### 3.2.1 Trial Arrangement  
In this paper, the three factors selected to affect the dynamic response of the structure are failure time, initial velocity, and initial displacement. The five levels of failure time factor are 0.05 T, 0.075 T, 0.1 T, 0.125 T, and 0.15 T; the five levels of initial velocity factor are \(-60\) mm/s, \(-30\) mm/s, \(0\) mm/s, \(30\) mm/s, \(60\) mm/s; the five levels of initial displacement factors are \(-20\) mm, \(-10\) mm, \(0\) mm, \(10\) mm, \(20\) mm. The combination of orthogonal table L25(53) arrangement factors were used to analyze the structure's dynamic response. The test schemes under the three working conditions of the bottom side, corner, and inner columns are shown in Tables 3-5. The vertical displacement of the top of the failed column under different conditions of the bottom corner, side, and inner columns is shown in Figures 4-6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Failure Time** & **Initial Velocity** & **Initial Displacement** \\ \hline K1 & 425.9 & 426.8 & 547 \\ K2 & 420.3 & 417.5 & 452.6 \\ K3 & 417.4 & 398.2 & 376.1 \\ K4 & 413 & 423 & 354.3 \\ K5 & 407.8 & 418.9 & 354.4 \\ k1 & 85.18 & 85.36 & 109.4 \\ k2 & 84.06 & 83.5 & 90.52 \\ k3 & 93.48 & 79.64 & 75.22 \\ k4 & 82.6 & 84.6 & 70.86 \\ k5 & 81.56 & 83.78 & 70.88 \\ R & 3.62 & 5.72 & 38.54 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Orthogonal test table when bottom corner column fails.  
Figure 4: Vertical displacement of column top after the failure of bottom corner column.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Failure Time** & **Initial Velocity** & **Initial Displacement** \\ \hline K1 & 618.9 & 615.5 & 787.8 \\ K2 & 617.1 & 614.2 & 681.1 \\ K3 & 618.2 & 587 & 561.3 \\ K4 & 604 & 615.6 & 530.5 \\ K5 & 587.4 & 613.3 & 484.9 \\ k1 & 123.78 & 123.1 & 157.56 \\ k2 & 123.42 & 122.84 & 136.22 \\ k3 & 123.64 & 117.4 & 112.26 \\ k4 & 120.8 & 123.12 & 106.1 \\ k5 & 117.48 & 122.66 & 96.98 \\ R & 6.3 & 5.75 & 60.85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Orthogonal test table when the bottom inner column fails.
## 5 Inertia Estimation Varying \(P_{e}\)  
The results obtained using the proposed methodology demonstrate an accurate performance. However, it was observed that the accuracy of the inertia estimation depended on the increment of the total active power \(P_{E}\) generated by the generation units of the system. In this section is presented a comparison of the results obtained in DIgSILENT PowerFactory with different values of \(P_{E}\) to estimate the inertia at G1. The results were obtained from the experimental validation of Section 4 in the second case study. This procedure used the measurements prior to a disturbance, using different values of \(P_{E}\) during the first oscillation; hence, the inertia could be estimated. Figure 8 shows the behavior of the active power provided by G1 and the inertia estimation when the short-circuit event was applied at the transmission line L1 (Figure 2).  
Figure 8 presents the results of the inertia estimation when \(P_{E}\) was varying. For this case study, the input inertia at G1 was \(H\) = 3.7 s; the first three approximations were \(H\) = 3.57 s, \(H\) = 13.78 s, and \(H\) = 3.13 s; and these results suggest that first approximation had a noticeable accuracy. This behavior was due to the changes of \(P_{E}\) in the system and the other values used to estimate the inertia. However, this does not mean that the inertia estimation cannot be improved; some factors like the second derivate of the angle at the generation bus had an influence on the results, and its value was produced in accordance with the type of event or disturbance presented in the power system.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Simulation Tool** & \(H\) **Input (s)** & \(H\) **Estimated (s)** & **Absolute Error \%** \\ \hline RTDS & 3.7 & 3.8 & 2.7 \\ DIgSILENT PowerFactory & 3.7 & 3.56 & 3.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inertia estimation results at bus 1, Case 3 phase short-circuit.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Simulation Tool** & \(H\) **Input (s)** & \(H\) **Estimated (s)** & **Absolute Error \%** \\ \hline RTDS & 3.7 & 3.79 & 2.43 \\ DIgSILENT PowerFactory & 3.7 & 3.65 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inertia estimation results at bus 1, Case 3 phase short-circuit and PI model.
###### Abstract  
We report on employing in vitro biosynthesized ZnO nanoparticles using _L. pubescens_ shoot methanol extract (50 and 100 mg LP-ZnO NPs) to examine their antimicrobial efficacy against _Pseudomonas aeruginosa_ (ATCC27853), _Staphylococcus aureus_ (ATCC 29213), _A Aspergillus niger_ (ATCC 16404 NA), and _Apergillus terreus_ (TCC 10029). The formation and stability of the investigated ZnO nanoparticles were proven by transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), UV-vis spectroscopy, X-ray diffraction (XRD), and thermal gravimetric analysis (TGA). The ZnO nanoparticles were rod-shaped (width: 10.76-30.93 nm). The nanoparticles in dimethyl sulfoxide (DMSO) outperformed their water counterparts in terms of their zones of inhibition (ZIs) (marginal means of 12.5 and 8.19 mm, respectively) and minimum inhibition concentrations (MICs) (means of 4.40 and 8.54 mg/mL, respectively). The ZI means for _S. aureus_, _P. aeruginosa_, _A. terreus_, and _A. niger_ were 10.50, 6.13, 12.5, and 11.5 mm, respectively. When treating _S. aureus_ and _P. aeruginosa_, the ZI of the 50 mg LP-ZnO NPs in water was better (14 mm), with a lower MIC and lower minimum bacterial/fungicide concentrations (MBC/MFC) (7.22 and 4.88 mg/mL, respectively) than the ZnO and control drugs. The SEM images showed cellular alterations in the surface shapes after the LP-ZnO-NP treatments. Biosynthesisized LP-ZnO NPs could have beneficial antibacterial properties, which could allow for future contributions to the development of new antimicrobial drugs.  
lavender shoots; bioactive compounds; biosynthesis; ZnO nanorod; zone of inhibition _Acticle_ Article
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 4 Discussion
### Physiotherapy Interventions  
Electric stimulation (NMES + FES) did not enhance muscle strength and self-care in people with SMA II and III [48]. This lack of improvement might stem from challenges such as the instability of electrode positioning during sleep and the low stimulation level (10 mAh), which might not be sufficient to recruit partially denervated muscles. However, in the [PERSON] et al. study, electrotherapy (NMES + FES) combined with cycling exercise may be well tolerated in an adolescent with SMA III and may improve motor performance in SMA patients [49]. Given its promising role in neuromuscular conditions, electrotherapy could be considered an adjunct to conventional rehabilitation rather than a standalone intervention.  
Whole-body mechanical vibrations were safe and well-tolerated, with no notable changes in electrolytes and CK levels [60]. However, objective differences in muscular strength, function, and flexibility were not observed, although participants reported positive subjective outcomes. Considering the potential benefits observed in individuals with spinal injury, where whole-body vibrations increase blood flow and activated muscle mass, it raises the question of whether this therapy could be valuable as a complementary treatment in SMA [70].  
Aerobic training sessions using a cycle ergometer, lasting 30 min over multiple weeks, seemed to enhance VO2 max and ultimately improve muscular capacity in people with SMA III, causing no harm [45; 52]. Notably, it did not enhance muscle strength or functional mobility, as it was not the primary focus, nor were motor functions and ADL [52]. However, optimal intensity and dosage of aerobic training remain uncertain in this population, especially considering their susceptibility to fatigue [52]. [PERSON] et al., for example, reported increased fatigue perception following aerobic training in individuals with SMA III [59]. It is worth noting that intermediate grades of SMA, such as type II, have received much attention in the literature.  
Hydrotherapy was primarily studied as an adjunctive treatment to conventional physiotherapy or aerobic training, showing benefits in motor and pulmonary functions, ADL, and quality of life for caregivers and people with SMA [45; 47; 59]. However, studies with more follow-ups and wider samples are needed to investigate the long-term effects of hydrotherapy.  
Finally, progressive resistance/strength training in people with SMA II and III was safe and well-tolerated, with high levels of adherence reported by participants (\(\sim\)90%) [51]. Participants in the [PERSON] et al. study reported a subjective improvement in pain and perceived fatigue [51], and in muscular strength and function. Not only that, but the use of nusinersen therapy, in conjunction with physical therapy, can result in a more favourable motor outcome [55]. However, the role of strength training is still controversial due to the low level of evidence, as also reported by [PERSON] et al. in their review [71].
## 3 Results
### Modelling and Predicting the Reactive Power Using ANFIS Grid Partition  
As previously mentioned, this type of ANFIS architecture is best suited to situations with a reduced number of inputs. The ANFIS architectures for different numbers of inputs (N) and fuzzy sets are shown in Figure 7a-d (FS).  
When analysing the modelling results based on ANFIS grid partition, it is noticeable that in the testing process, there are no significant differences in ANFIS performance. There have been experiments with various architectures. Table 1 shows the results. In Table 1, it is noted that FS represents the number of fuzzy sets and N represents the number of inputs, which is actually the number of previous samples. When the ANFIS parameters are set so that the ANFIS structure has many rules (for example, N > 3, FS > 3), the phase testing results are not any better. On the other hand, overtraining occurs at some point, and ANFIS loses the ability to generalize. This can be shown with N = 4 and FS = 4. It was found that the training time increased excessively. As a result, if a large number of fuzzy sets are selected, this type of architecture is not very suitable for time series prediction.  
Figure 6: How to use previous samples of reactive power when modelling with ANFIS.  
As shown in Figure 7, the grid partition method generates a large number of rules in the rule layer. Many rules are generated when the input space is partitioned. The training time is increased in this way. Line 12 of Table 1 illustrates this. In fact, for a number of previous samples N = 6, these models from Table 1 are the only ones with an acceptable training time. Other models, for example, for N = 5, FS = 4, have been tested, but they failed, necessitating a large amount of training time. The training time for this type of ANFIS based on grid partition models is excessively long due to the large number of neurons that result from the partitions made by the fuzzy sets, resulting in a large number of neurons in layer 3 and 4, and thus a large number of laws. Of course, a larger number of rules leads to smoother modelling in general, but if there are too many rules, the model cannot be used in practice due to the lengthy training period.  
Figure 8 depicts the modelling and prediction results for an ANFIS grid partition architecture (N = 4, FS = 2) chosen from Table 1. Figure 8 depicts the difference between the calculated reactive power and the one obtained by simulation, as well as the error. Figure 9 depicts the differences between measured and predicted reactive power, as well as the test phase error. Figure 10 depicts the variance in the regression coefficients during the training and testing phases.  
Figure 7: Different architectures of ANFIS models using the grid partition method; (**a**) N = 5, FS = 2; (**b**) N = 4, FS = 2; (**c**) N = 3, FS = 2; (**d**) N = 2, FS = 3.