esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 4 Results and Discussion
### Discussion  
From the testing procedure, it was observed that most of the flaw signals were extracted properly. However, there were some reconstructed signals that failed to extract the flaw signals properly from the original signal. When the amplitude of the target signal is relatively small, the neural network could not extract the flaw signal as expected. Instead, the flaw signal as well as the background signal from the weld interface and the geometry are also denoised by the denoising autoencoder. Additionally, some loss of amplitude is witnessed. Figure 11 shows some examples that did not extract the flaw signal as expected.  
Both signals (a) and (b) from Figure 10 share some characteristics in common. Firstly, the flaw signals have relatively low amplitude when observed in the mixed signal. Moreover, the peak amplitudes of denoised signals are low compared to that of signals in Figure 9.  
There could be several reasons that the autoencoder used in this study did not perform well on signals with small peaks. One reason could be the shortage of samples or training data. The autoencoder learns the features of signals, and most of the signals are acquired from the peak amplitude. This means that the amount of data with particularly low amplitude is not enough for the neural network to learn. Another reason could be the pattern of signals. Most of the training data had a similarity when extracting the flaw  
Figure 11: The examples of signals that are not extracted properly: (**a**) A-scan mixed crack signal data and extracted crack signal from flaw #150; (**b**) A-scan mixed slag inclusion signal data and extracted slag inclusion signal from flaw #892.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & **Training** & **Testing** \\ \hline Number of data (\#) & 2463 & 1000 \\ Average of maximum & 0.6729 & 0.6281 \\ amplitudes & & \\ Average difference of data & 0.02764 & 0.03282 \\ points & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Point-by-point comparison of extracted flaw data, training dataset, and testing dataset must.  
signal. Most of the background signals have a smaller amplitude compared to that of the flaw signal. This characteristic of the training data would mislead the DAE to consider that all signals with small amplitude are extra signals to be deleted. Lastly, the original data were not regularized. Some of the original A-scan data had very low amplitude. By regularizing the signals with the flaw signal's amplitude, the DAE used in this study would have learned much better.
## 4 Propagation Characteristics and Phenomena of LMS Communication
### Reflection  
The reflection is the phenomenon in which the signal is reflected from the ground in the direction of the satellite, and it depends on the elevation angle of the satellite as seen from the mobile satellite terminal. The magnitude of the ground-reflected wave is proportional to the terrain roughness factor, and its effect can be reduced by terminal antenna directivity [19; 24].
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 5 Our Privacy-Preserving Swarm Learning
### System Initialization
#### 5.1.3. Registration  
To participate in swarm learning, \(\textbf{MC}_{k}\in\textbf{MCs}\) need to register to smart contract (SC) published on the blockchain, which specifies the entire deep learning task. Each \(\textbf{MC}_{k}\) constructs a zero-knowledge proof \(\textit{proof}_{sk_{k}}\) of its private key \(sk_{k}\). The \(\textit{proof}_{sk_{k}}\) is defined as \(<c,r>\), where \(v\in_{R}\mathbb{Z}_{q}\), \(t=g^{v}\), \(c=H(g,pk_{k}^{psu},t)\), and \(r=(v-c\cdot sk_{k})\ mod\ q\). Then, \(\textbf{MC}_{k}\) submits \"_Registration_\" transaction \(<pk_{k}^{psu},\textit{proof}_{sk_{k}}>\) to the blockchain. And then, each \(\textbf{MC}_{k}\) on the blockchain verifies \(\textit{proof}_{sk_{k}}\), i.e., \(\textit{proof}_{sk_{k}}\stackrel{{?}}{{=}}H(g,pk_{k}^{psu},g^{v}( pk_{k}^{psu})^{c})\) to prevent an adversary from registering itself with \(pk_{k}^{psu}\) copied from an honest \(\textbf{MC}_{l}\). If the verification is passed, \(\textbf{MC}_{k}\) is registered as a member of the learning group and stores its \(pk_{k}^{psu}\) to the blockchain.  
And in order to protect public broadcast channels, we can construct the symmetric encryption key \(\textit{key}_{kl}=(pk_{k}^{psu})^{sk_{l}}=(pk_{l}^{psu})^{sk_{k}}\) and use the symmetric key encryption algorithm \(\textit{Enc}_{key_{kl}}(\cdot)\) to encrypt the traffic data between \(\textbf{MC}_{k}\) and \(\textbf{MC}_{l}\).
## 2 Characteristics of Surface Potential Distribution for DC Grounding Electrode
### Distribution of Current in the Feeder Rods of a DC Grounding Electrode
The current distribution of all feeder rods in the DC grounding electrode under the above two conditions is analyzed. According to Figure 3, it can be seen that under normal circumstances, when the grounding electrode is not broken, all the feeder rods (assumed to be \(N\)) are approximately divided into the total injection current value \(I\), and the average value of the distribution  
Figure 3: Current distribution of DC grounding electrode under normal operating conditions. (**a**) Simulation result; (**b**) The current distribution result of each feed rod. Figure 2: DC grounding electrode feeder rod numbering diagram. current of all the feeder rods is 3.4567 A. In Figure 4, when a feeder rod is broken in the local area, there is no current injection in the broken feeder rod. At this time, the remaining (\(N-1\)) feed rods are approximately divided into the total injection current value I. At this time, the average value of the distribution current of the feed rod under all normal operation is 3.4642 A, which is 0.0075 A higher than that without fracture. In Figure 5, when three feeder rods are broken in the local area, there is no current injection in the three broken feeder rods. At this time, the remaining (\(N-3\)) feeder rods are approximately divided into the total injection current value \(I\). At this time, the average distribution current of the feed rods under all normal operation is 3.4801 A, which is 0.0234\(-\)0.0236 A higher than that without fracture. In summary, when the feeder rod of the DC grounding electrode is partially broken, the distributed current of the remaining part of the feeder rod will increase slightly, but it is still approximately evenly distributed as a whole.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 2 System Model
### Last Hop Selection  
To assess the impact of the last hop selection, in this paper, we consider three strategies: (i) first opportunity (FO), (ii) prescribed quality (PQ), and (iii) best quality (BQ). The first strategy implies that a bridge to the BS is established as soon as possible, i.e., if after a  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Scenario** & **Propagation Path** & **Model** & **Losses** \\ \hline Single-lane & LoS & FSPL & 0 dB \\  & Vehicle–body block. & FSPL + height-dependent & 30–50 dB \\  & Under-vehicle prop. & FSPL + \(\phi d^{-\psi}\), where \(\phi\) and \(\psi\) \\  & & are as in Table I in [23] & \\  & Front/rear reflection & FSPL + reflection loss & \(\sim\)25 dB \\ \hline Multi-lane & Sym. side reflection & FSPL + reflection loss & \(\sim\)3 dB \\  & Assym. side reflection & FSPL + angle-dependent loss & 16–20 dB \\  & Front reflection & FSPL + angle-dependent loss & 24–42 dB \\  & Rear reflection & FSPL + angle-dependent loss & 15–30 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Propagation losses, \(L_{B}\).  
few relays there is a LoS path to the BS at the distance \(d<d_{L}^{max}\), the system attempts to initiate a connection. Though the FO strategy does not allow for any control over the quality of the channel, it provides the shortest latency, as the number of hops is always the fewest possible.  
The most flexible strategy is the PQ, as it enables selecting the minimum required MCS for the connection among all the hops within a bridge. Considering the last hop, it allows for completion of the bridge if only the hop channel quality is good enough, i.e., it meets the prescribed data rate. By varying the required MCS, it is possible to improve the data rate at the cost of bridge availability. The last strategy is \(\mathrm{BQ}\) which is aimed at improving the quality of the last hop as much as possible. This means that the system starts scanning the quality of the channel to the BS and attempts to find more relays until the degradation of quality is observed. This allows for further improvement of the data rate but requires more hops along with more sophisticated bridge signaling.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods
### Networking Protocol and Hardware Selection
#### 2.1.3 System on Chip and Supporting Hardware  
Vast amounts of similar development boards are available on the market and comparison of suitable boards can be seen in Table 3. Devices produced by Mbed were ruled out because there was a lack of good quality documentation and information for their microcontroller boards at the time of writing. Available documentation and the potential for manufacturing custom integrated sensor boards in the future were the driving factors behind the choice of microcontroller board. The BCM2835, the core chip of the Raspberry Pi was difficult to purchase in quantities smaller than 100; however, a system including the ATWINC1500 chip would not have this issue and could easily be translated to a custom printed circuit board (PCB). This would significantly reduce the costs with minimum added design time. The combination of all these factors meant that the Arduino MKR1000 offered the best trade-off between cost and development time whilst also future proofing the manufacture of the device as a commercial product.
## 4 Experimental Verification
### Identification of Axle Information  
A known calibration vehicle is first driven through the steel beam model from different transverse positions by a motor traction to identify the axle's weight. The calibration vehicle was a two-axle vehicle with a gross weight of 50.8 kg and front and rear axles of 26.2 kg and 24.6 kg, respectively. The center position of the vehicle acting on the beam model is defined as x = 0 cm, and a total of 47 transverse positions (x = \(-\)13.5-12.0 cm) were tested; FAD sensors and axle weight sensors collected corresponding strain responses. Figure 12 shows part of the strain response when the calibration vehicle passes on the steel beam model from a transverse position of x = \(-\)1.5 cm.  
It can be seen that FAD sensors monitor two distinct peak points, which correspond to the two axles of the calibrated vehicle. Based on the proposed method, the wheelbase and the vehicle's speed can be estimated. Table 8 shows part of the identification results.  
As observed from the above table, the identified speed varies in a range of 12.26-12.47 cm/s, and the mean speed is 12.38 cm/s. The identified wheelbase range is 37.68-38.25 cm, and the mean of identified error is 0.41%, which indicates that the identified speed and wheelbase have good stability and accuracy.
# Chemical Diversity of Mediterranean Seagrasses Volatilome
## 3 Results
### Volatilome Classification  
Detected compounds could be classified according to their biosynthetic pathways: fatty acid derivatives (FADs) (in relative abundance: _Z. marina_: 95.8%, _Z. noltei_: 85.3%, _C. nodosa_: 81.5%, and _P. oceanica_: 35.5%), terpenoids (_P. oceanica_: 15.9%, _Z. noltei_: 10.3%, _C. nodosa_: 6.6%, and _Z. marina_: 3.8%), and sulfur-containing compound (_P. oceanica_: 44.7%, _C. nodosa_: 11.0%, and _Z. noltei_: 3.2%, absent in _Z. marina_). A few compounds belonged to other biosynthetic pathways: benzenoids and furanoids as well as some unknown compounds (Supplementary Figure S1). _C. nodosa_ mainly emitted alkanes (38.8%), esters (32.8%), sulfur compounds (11%), alkenes (6.9%), aldehydes (5.7%), ketones (3.2%), ethers (<1%), alcohols (<1%), and some unknown compounds too (<1%) (Figure 3). _P. oceanica_ mainly produced sulfur compounds (44.7%), aldehydes (15.9%), alkanes (14.0%), ketones (12.3%), esters (4.9%), ethers (3.4%), alkenes (2.0%), alcohols (<1%), and some unknown volatiles (<1%). _Z. marina_ mainly released alkanes (79.4%), esters (11.0%), aldehydes (2.9%), ketones (2.7%), and, in low amounts (<2%), alkenes, ethers, alcohols, phenolic compounds, and unknown metabolites. _Z. noltei_ mainly emitted alkanes (72.6%), aldehydes (6.7%), esters (6.4%), ketones (6.0%), sulfur compounds (3.2%), and, in low amounts (<2%), ethers, alkenes, alcohols, carboxylic acids, phenolic compounds, and unknown compounds. The major compounds per species in relative abundance were isopropyl myristate for _C. nodosa_ (25.6%) (produced 10 to 37 times more than in the other three species), DMS for _P. oceanica_ (39.3%) (produced 1.5 times more than in C. _nodosa_ and 2 times more than in _Z. noltei_, while _Z. marina_ produces no DMS), pentadecane for _Z. marina_ (42.9%), and heptadecane for _Z. noltei_ (46%) (both produced by the four species) (Supplementary Table S1).
# Mobile Robot Path Planning Considering Obstacle Gap Features
## 2 Path Planning Method with Clearance Characteristics  
### Global Path Planning Based on Gap Map  
The A* algorithm is a deterministic search method based on traversal. The original A* algorithm is mainly used to search for the shortest path in global path planning problems. And it has been considered a typical algorithm to solve the global path planning problem due to its simple and intuitive search process. The A* algorithm makes a comparison based on the heuristic function values f(n) of the eight adjacent grids of the current grid to determine the next path grid, as shown in Figure 1a. S represents the start point, and T represents the target point. However, a random grid will be selected as the next path grid when there are multiple minimum values. So, the optimal path cannot be guaranteed by the result of the A* algorithm.  
There are two disadvantages to the original A* algorithm:  
* The computing efficiency is seriously affected by too many search nodes;
* The continuity of movement is affected by redundant colinear nodes and turning points.  
In response to the above problems, the following improvements are proposed in this study:* A phenomenon in which the slow diffusion part of the search deviates from the path from the current semi-closed area to the next half-closed area is observed by visualizing the search process of the A* algorithm. And this path can be defined as the obstacle gap, deviating from this path is inconsistent with the experience of human experts.
* Based on the concept of obstacle gap, the search heuristic function, the search node direction, and the final path selection of the A* algorithm are comprehensively optimized.  
The 1/6 threshold for obstacle gap identification balances robot maneuverability and environmental constraints. This ratio ensures detected gaps exceed 1.5\(\times\) the robot's width (with a safety margin) in standard 10 m \(\times\) 10 m operational environments. For typical service robots (0.4-0.6 m width), this corresponds to a minimum navigable gap of 1.67 m (1/6 of 10 m), validated through simulations showing \(>\)90% traversal success while minimizing false detections. The nodes (G) are automatically tagged during map initialization (Figure 1b) to prioritize physically feasible narrow paths.
## 5 Results and Analysis
### Performance of the Sensorless BLDCM Drive Control
This result overperforms state-of-the-art efficiencies in similar BLDC motor applications, limited to 6.9% and 15.8% for single- and two-boost open-loop regeneration methods, respectively [8]: 19.45% (two-boost, closed-loop regeneration method [43]), and 23.44% (improved two-boost, closed-loop regeneration method [44]). Finally, Figure 20a,b present a comparative graphical analysis of the sensorless methods, focusing on the error in position estimation obtained through experimental testing. The commutation error achieved by the proposed back-EMF observer with improved QPLL is compared with the conventional back-EMF observer and the zero-crossing point (ZCP) detection-based method [42]. They were also tested under analogous experimental conditions. Based on the experimentally obtained position error tracking in Figure 20a,b, it is evident that the implemented back-EMF QPLL observer-based position estimation outperforms conventional approaches. The position error is finely brought to near-zero in steady-state conditions by integrating the improved QPLL with the back-EMF observer. Even during transients, such as changes in speed, the position error remains minimal with  
Figure 19: Energy regeneration of the BLDCM drive. Time per division: 10 ms.  
Figure 18: Dynamic performance of the BLDCM drive: sensorless position estimation with speed control. Time per division: 200 ms.  
the implemented back-EMF QPLL observer-based sensorless control, compared to the other mentioned methods. The experimental results demonstrate the efficacy of the QPLL-based back-EMF observer in achieving accurate rotor position estimation across a wide speed range. Its low-speed performance, where traditional methods typically struggle as developed in Section 1, should be emphasized. At low speeds (e.g., below 300 rpm), the QPLL effectively mitigates the challenge of low back-EMF amplitude by dynamically adjusting its loop gain, ensuring stable and precise position estimation without introducing excessive noise or delay. This is evident from the near-zero commutation error observed during both steady-state and transient conditions. At higher speeds, the proposed method maintains consistent performance, with negligible deviation in the estimated position and speed, highlighting its robustness. Furthermore, the system exhibits rapid dynamic response to sudden changes in speed, as evidenced by the minimal transient duration and overshoot in experimental waveforms. The findings underscore the superior dynamic adaptability of the QPLL-based observer, making it a practical solution for sensorless control in solar-powered BLDCM drives.
## 3 Results and Discussion
### Consumer Evaluation
#### 3.1.2 German Consumers  
Similar to Irish consumers, there was no significant (\(p>0.05\)) difference in liking by German consumers for all three butters for sensory attributes, or for overall liking (Hedonics Table 1). In agreement with Irish consumers, German consumers rated the color of both FS-GRSS and FS-CLVR butter significantly (\(p<0.017\)) more intense than FS-TMR butter (Intensity Scale Evaluation Table 1) and showed a significant (\(p<0.05\)) higher score for 'not yellow enough' for FS-TMR butter (JAR Evaluation Table 1). German consumers also found the salt intensity of FS-GRSS and FS-CLVR butters, significantly (\(p<0.017\)) higher than FS-TMR butter, even though identical levels of salt were added to each batch. Additionally, there was a significant (\(p<0.017\)) difference in the perception of firmness intensity, with FS-TMR butter perceived as firmer than FS-GRSS and FS-CLVR butters. Although FA analysis was not undertaken in this study, butters from an identical experimental trial [22], and other studies [33; 34], have characterized butter produced from pasture diets higher in unsaturated FAs, therefore the pasture-derived butter is likely to be softer, due to a lower melting point. The higher salty intensity perception of FS-GRSS and FS-CLVR butter as perceived by the German consumers may also relate to the softer texture of these butters and their behavior in the mouth--more rapid melting compared to FS-TMR butter (Figure 1). This would also appear to be confirmed by the JAR Evaluation of texture for FS TMR butter, which was deemed significantly (\(p<0.05\)) higher for'much too firm'. [PERSON] and [PERSON] (2019) [35] found that margarine with the lowest score for hardness was perceived as the saltiest, despite having identical salt contents.
## 4 Conclusions  
Zinc electrodeposition in both pure aluminum and the aluminum alloy 6061 has been successfully carried out using citrate-based electrolyte solutions, with and without the addition of EDTA. This technique has significantly improved the performance of Al-air batteries by depositing Zn on pure aluminum and the aluminum alloy 6061 with a citrate-based deposition electrolyte solution. Incorporation of EDTA as a chelating agent into the citrate-based deposition electrolyte solution further enhances the performance of the Al-air battery. This improvement is attributed to the facilitated formation of uniform and coherent Zn coatings on the metal surfaces, a benefit not observed in the absence of EDTA.  
Specifically, an Al-air battery that comprises an aluminum alloy 6061 anode with Zn electrodeposition, enhanced by the addition of EDTA, paired with a Carbon@MnO\({}_{2}\) cathode and a 3.5% NaCl solution as an electrolyte, has shown exceptional performance. This configuration achieved a specific discharge capacity of 414.561 mAh.g\({}^{-1}\) and a specific discharge energy of 255.077 mAh.g\({}^{-1}\), maintaining a stable voltage of 0.55 V for a period of 207 h. This showcases the significant impact of EDTA in promoting electrochemical efficiency and battery longevity.  
Conceptualization, R.B.S. and S.N.; methodology, H.S.; software, S.S.; validation, R.B.S., W.W., and H.S.; formal analysis, S.N.; investigation, S.S.; resources, R.B.S.; data curation, S.N.; writing--original draft preparation, S.N.; writing--review and editing, R.B.S.; visualization, R.B.S.; supervision, H.S.; project administration, W.W.; funding acquisition, R.B.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to express their gratitude to PT. PLN (Persero) and the Sepuluh November Institute of Technology for the completion of this research.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SEM & Scanning electron microscope \\ EDX & Energy dispersive X-ray spectroscopy \\ CV & Cyclic voltammetry \\ ORR & Oxygen reduction reaction \\ EDTA & Ethylenediaminetetraacetic acid \\ PVDF & polyvinylidene fluoride \\ NMP & N-methyl-2-pyrrolidone \\ OCP & Open circuit potential \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Anode Sample**} & **Discharge Capacity** & **Discharge Energy** \\  & **(mAh.g\({}^{-1}\))** & **(mWh.g\({}^{-1}\))** \\ \hline Pure Al & 0.267 & 0.11 \\ Zn-pure Al & 104.55 & 60.017 \\ Zn (EDTA)-pure Al & 144.501 & 80.143 \\ AA 6061 & 94.892 & 54.466 \\ Zn-AA 6061 & 247.039 & 137.109 \\ Zn (EDTA)-AA 6061 & 414.561 & 255.077 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Discharge capacity and energy of Al–air battery using \(1\times 1\) cm\({}^{2}\) varied anodes.