esa-sceva/satcom-chunk-collection huggingface dataset:
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
###### Abstract  
To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods.  
Sensor Networks; mobile Sinks; low-latency; balance of energy consumption; redundancy on network coverage +
Footnote †: journal: Sensors
## 4 Verification and Discussion of the Proposed Method
### Comparison of Different Methods  
To verify the effectiveness of the proposed method, the simulated signal with 5 dB of noise was considered on the 2/3 tooth breakage situation. The frequency spectrum is obtained with the spectral subtraction method. Figure 13 shows that the side-band frequency under the strong noise is hardly extracted from the frequency spectrum. However, the spectral subtraction method can enhance the amplitude value of the side-band frequency region more than the gear meshing frequency region. The results are shown in Figure 14. The side-band frequency features are extracted by the EWT method.  
For comparison, the EMD was used to decompose the observed signal into eight IMFs. The added noise signals are complex, so the fault frequency cannot be evaluated directly with the power spectrum. The envelope spectrum of the first IMF contains the primary information of the fault signal. Therefore, the side-band feature frequency can be identified clearly. Then, the LMD is applied to adaptively decompose the added noise signal into several PFs from the high-frequency band to the low one. Several PFs can be obtained with the LMD process. The first PF is selected for further analysis, because it has the biggest correlation coefficient and keeps the most of information from the original signal. Finally, the DWT decomposes the added noise signal to extract the main information of the fault features. The side-band frequency features are extracted from the wavelet correlation of the impulse  
Figure 11: The faulty features with square envelope analysis, (a) no noise, (b) 10 dB, (c) 5 dB, (d) 0 dB, (e) \(-\)5 dB, and (f) \(-\)10 dB of noise.  
Figure 12: The comparison results for different noise level.  
component with the envelope spectrum. The compared faulty features of the different algorithms are shown in Figure 15.  
Figure 14: The segmentation of the frequency spectrum with spectral subtraction.  
Figure 13: The segmentation of the frequency spectrum with 5 dB of noise.  
Figure 15: The fault features with square envelope analysis.  
Table 3 shows the side-band features calculated from the square envelope analysis. The proposed algorithm has significantly better performance for the feature extraction of the side-band information of the faulty gear. The noise energy has been obviously reduced by the proposed method, which is efficient for fault feature extraction.  
Table 4 shows that EMD, LMD, and DWT method can extract the side-band frequency features except noise \(-\)5 dB and \(-\)10 dB. Their performance for extracting the side-band feature reached a limit when the noise exceeded \(-\)5 dB. However, the proposed method can more clearly extract the side-band feature under different background noise, and its amplitude value remains stable. Therefore, the proposed method is feasible for reducing strong noise and effectively extracting the faulty features.
## 4 Experimental Result and Discussion  
### Data and Method  
To prove the feasibility of the AQFCE in practice, it has been fully tested under various conditions in China. According to \"Three-year Action Plan for Winning the Blue Sky Defense War\" issued by the China's State Council in June 2018, the Fenwei Plain has replaced Pearl River Delta to be the key area of air pollution control together with the Jing-jin-ji region and Yangtze River Delta. As representative cities, Beijing, Shanghai and Xi'an (Figure 2), with different environments, industrial structures and pollution features, are selected. Unreasonable industrial and energy structures as well as continuously rapid growth of vehicles have caused huge emissions of local pollution, which is common in the three metropolises. In addition, Beijing and Xi'an are, respectively, located in the northeast and southwest of \"Jing-jin-ji air pollution transmission channel\", so they are often affected by regional pollution transport. Adjacent to sand sources, they also suffer from sand- and dust-storms in spring. Daily and hourly concentration data of six conventional air pollutants from September 2018 to August 2019 are from the Ministry of Ecology and Environment of China ([[http://www.mee.gov.cn](http://www.mee.gov.cn)]([http://www.mee.gov.cn](http://www.mee.gov.cn))). In view of the excessive hourly data, the representative month of every season (October, January, April and July) are used in hourly evaluations. The AQI is the most widely used and recognized air quality evaluation method at present, so comparative analysis with the AQI in practice is necessary for any new method. Therefore, based on the same monitor data, comparative analysis between the AQI and AQFCE is carried out on different spaces (Beijing, Shanghai, Xi'an), time (spring, summer, autumn, winter), tasks (daily report, hourly report) and purposes (air quality level, chief pollutant).
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 7 Pruning the Network
### Pruning in the Deterministic Case  
For the deterministic case in which links are activated in a deterministic way under the activation function \(g(\tau)\), we can identify links that are completely covered by the rest of the network so that their pruning will not have any effect on the AoI.  
To illustrate an example, Figure 4 shows the fourth link, which has an activation function of \(g(2)+g(3)\) being completely covered by the first and second link in the sense that when the source node \(s\) is ready to send a message to destination node \(d\), whatever the time is, we may completely prune the fourth link without affecting the AoI. Equations (26) and (27) represent the reduction formulas.
# Antimicrobial Peptides--Mechanisms of Action, Antimicrobial Effects and Clinical Applications
## 2 Antimicrobial Peptides  
Antimicrobial peptides are composed of a different number of amino acids. This number is usually from 10 to 60 amino acids [13]. Amphipathic properties have a vital role in the activity of AMPs [17]. Thanks to this property, AMPs are easily integrated into the cell membrane or pass through into the cytosol [18]. The mechanisms of action on microorganisms depend on different physicochemical properties: charge, structure, sequence length, peptide concentration, hydrophobicity, and membrane composition [19]. Ribosomally synthesized AMPs that contain only natural amino acids are divided into linear, \(\alpha\)-helical peptides (such as cercopins, magainins, and melittins), peptides labeled with enrichment of one or two amino acids (PR 39 rich in proline-arginine, indolidin), and peptides containing disulfide bonds (e.g., defensins, protegrin) [20]. Additionally, peptides with strong antimicrobial activity synthesized extraribosomally have significant posttranslational modifications, such as lipoproteins (polymyxin, dermaseptin) and lentibiotics containing non-native amino acids [21].
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 3 Retina Vein Occlusion
### Role of OCTA in Retinal Vein Occlusion  
[PERSON] et al. reported on OCTA findings in 25 eyes with RVO and found that OCTA is capable of visualizing peripheral pathology associated with vein occlusions, such as tortuous venous segments, areas of non-perfusion, retinal atrophy, collateral or shunt vessels, neovascularization as well as macula changes such as intraretinal edema and enlargement of the FAZ (Figures 4-6). These OCTA findings have been shown to be consistent with clinical, anatomic, and FA findings [53; 54; 55].  
predominantly located within and course through the DCP. This has helped to further understanding of re-perfusion in chronic stages of RVOs [60].  
OCTA has also been an effective modality for assessing the macular complications of RVO. [PERSON] et al. evaluated 81 eyes with a history of RVO and found good agreement between FA and OCTA scans centered on the macula for capillary non-perfusion and capillary changes such as dilation, pruning, and telangiectasia (Figure 6--blue circle). The group also found good agreement for grading the FAZ for the 3 mm \(\times\) 3 mm OCTA scan [61]. Hence, when used in conjunction with cross-sectional and en face structural OCT, OCTA may be able to provide enough information for the diagnosis of macular edema and ischemia, monitor response to therapy and assess visual prognosis, thus reducing the need for FA [14; 53]. A limitation of OCTA, which is also seen in FA, is that microvascular alterations can be obscured by extensive intraretinal hemorrhages in the acute stage (Figure 4A--yellow arrows, Figure 5A). In addition, in cases where the visual acuity is impaired due to macular ischemia or severe macular edema, fixation may be challenging resulting in poor quality OCTA scan with movement or blink artefacts.
## 3 Results and Discussion
### Aroma Profiling

Table 3: Antioxidant activities (2,2-diphenyl-1-picrylhydrazyl radical (DPPH, % inhibition); ferric reducing antioxidant power (FRAP, (\(\upmu\)M TE g-1 FW); Trolox equivalent antioxidant capacity assay TEAC ABTS %; Ferrozine (% inhibition) in red _Topepo_ fruits grown in unfertilized soil (control, CTR) and soils fertilized with compost from olive wastes (CO), compost from vegetable wastes (CV), and horse dung (HD). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**COMPOUNDS** & **RT** & **CTR** & **CV** & **CO** & **HD** \\ \hline
**ALCOHOL (12)** & & & & & \\
1-pentanol, & & & & & \\
4-methyl- & 6.11 & x & x & x & x \\ guaiacol & 14.62 & nd & x & nd & x \\ cis- 3 nonen- 1-ol & 15.35 & x & x & x & x \\ phenylethyl alcohol & 15.65 & nd & nd & nd & x \\ benzyl alcohol & 17.36 & x & x & x & x \\
1.10-decanediol & 19.15 & x & nd & x & x \\ nonna-3.5-dien-2-ol & 20.11 & x & nd & x & nd \\
4-tert & & 21.33 & x & x & x \\ butylthiophen & 1-decanol. 2-ethyl- & 22.43 & x & x & x \\
1-octanol. 2-butyl- & 23.13 & x & x & x & x \\
1.2-benzenediol. & & & & & \\ O-(4-ethylbenzoyl)-O’- & 23.55 & nd & x & x & x \\ propargyloxycarbonyl- & 1-octanol. 3-butyl- & 31.77 & x & x & x \\
3-hexadecanol & 37.27 & nd & x & x & nd \\
**ALDEHYDES (12)** & & 4.08 & x & x & x \\ hexanal & 4.08 & x & x & x & x \\ benzeneacetaldehyde & 12.91 & x & nd & x & x \\
2-octenal. (E)- & 13.51 & nd & nd & x & x \\
4-nonenal. (E)- & 14.96 & x & nd & nd & x \\
2.6-nonadienal. & & & & & \\ (E.Z)- & 17.27 & nd & x & x & x \\
2-nonenal. (Z)- & 17.49 & x & x & x & x \\ Benzaldehyde. & 18.05 & nd & nd & nd & x \\
4-ethyl- & 18.68 & x & nd & x & x \\ cis-decanal & 18.90 & x & x & x & nd \\
7.11-hexadecanal & 21.14 & x & x & x & x \\
2.4-decadienal. (E.E)- & 22.24 & x & nd & nd & nd \\ tricyclo[7.1.0.0[1.3]] & 23.00 & x & x & x & x \\ decane-2-carbaldehyde & & & & & \\
**KETONES (2)** & & & & & \\
4-nonanone & 14.11 & x & x & x & x \\
2(3H)-furanone. & & & & & \\ dihydro-5-pentyl- & & & & & \\
**ESTERS (9)** & & & & & \\
4-hexen-1-ol. acetate & 5.65 & x & nd & nd & x \\ phenacylidene & 9.69 & x & x & x & x \\ diacetate & 14.86 & x & x & nd & x \\ cinnamillcarbonate & 16.99 & x & nd & nd & nd \\ myrtenyl acetate & 23.80 & x & x & x & x \\ hexanoic acid. hexyl & 25.33 & x & nd & nd & nd \\ ester & & & & & \\
5.8.11.14.17- & & & & & \\ eicosapentaenoic & & & & & \\ acid. methyl ester. & & & & & \\ (all-Z)- & & & & & \\ oxalic acid. allyl & & & & & \\ octadecyl ester & & & & & \\ isopropyl myristate & 37.90 & nd & x & nd & nd \\ \hline \hline \end{tabular}
\end{table} 
Table 4: - Summary of compounds identified in red _Toppo_ fruits grown in unfertilized soil control (CTR) and soils fertilized with compost from olive wastes (CO), compost from vegetable wastes (CV), and horse dung (HD).
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 6 Challenges in Whole-Genome Alignment
### Computational Challenges  
Computational challenges in WGA span from the handling of large and complex datasets to the efficiency of alignment algorithms. The exponential increase in the volume of genomic data due to advancements in sequencing technologies necessitates WGA algorithms that can handle large datasets efficiently and accurately [76].  
Temporal and spatial complexities are significant challenges. As the size of the genomes increases, the time taken for alignment and the memory required for processing also increase exponentially. Even with advancements in algorithmic design and computational power, dealing simultaneously with large genomes or multiple alignments remains a daunting task [77].  
The implementation of parallel and distributed computing strategies can help mitigate these challenges to some extent. However, these approaches also require specialized knowledge and resources and can be associated with significant costs [78].
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 4 Measured Results and Discussion  
The chip manufactured in a 40 nm CMOS process occupies an area of 2.16 mm\({}^{2}\). The layout of ADC_CORE is shown in Figure 14, including the input buffer, SHA, and Stage1-6. The die micrograph is shown in Figure 15, including ADC_CORE, digital correction, and serdes output. Many decoupling capacitors are filled in the spare spaces between different blocks to keep the power supply voltage clean and stable.  
The static performance of DNL and INL is presented in Figure 16. 65,536 sample points are saved to calculate DNL and INL. The measured DNL and INL are \(-\)0.72/+0.68 LSB and \(-\)1.52/+1.51 LSB after calibration, respectively. Figure 17 shows a fast Fourier transform (FFT) plot of the ADC output before and after calibration at the input frequency of 227 MHz and 1 GSps. Figure 18 shows the SFDR and SNR of this ADC versus the frequency of the input signal with and without calibration at 1 GS/s. For example, this ADC can achieve an SNR of 57.38 dB and an SFDR of 78.5 dB with 227 MHz input frequency. The SFDR can be greatly improved when the input frequency is low.  
power consumption in the future. The 10-bit, 80 MS/s pipelined ADC achieves the bit error rate of \(10^{-15}\) errors/sample, which is a factor between \(10^{4}\) to \(10^{6}\) less than in a comparable lookahead pipelined ADC [35]. Our work can achieve the same BER level at higher speed and accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & [35] & [36] & [37] & [38] & [39] & **This Work** \\ \hline Technology & 250 nm & 28 nm & 40 nm & 28 nm & 40 nm & 40 nm \\ \hline Voltage (V) & - & 0.9 & 1.1 & 1.0 & 1 & 1.8 \\ \hline Resolution (bit) & 10 & 12 & 12 & 12 & 12 & 12 \\ \hline Sampling Rate (Gsps) & 0.08 & 1 & 0.5 & 0.25 & 400 & 1 \\ \hline ENOB (bit) * & - & 9.1 & - & - & 9.40 & 9.2 \\ \hline SNDR (dB) * & 58.3 & 56.6 & 64.10 & 61.5 & 58.4 & 57.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of measured ADCs.  
Figure 16: DNL and INL after calibration based on 65,536 sample points. (**a**) DNL after calibration; (b) INL after calibration.  
Figure 17: Fast Fourier transform (fft) before and after calibration versus the input frequency 1 GSps. (**a**) fft plot of this ADC versus frequency of the input signal without calibration; (**b**) fft plot of this ADC versus frequency of the input signal with calibration.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Multi-Robot Task Allocation
The results of the simulation experiment suggested that the execution time increases correspondingly with an increasing number of tasks, followed by a gradual decrease in production efficiency. It would be necessary to add new robots temporarily to ensure productivity, but the production cost would also increase, so the tasks need to be set in advance. Figure 9: Distributed task allocations: (**a**) An entry was stored in task storage with ID numbers. And robots could select ID to work according to the relationship between the robot’s function and the task requirement [85]; (**b**) A task was split into several subtasks. And each robot proposed the largest possible task allocation initially, then gradually decreased its offers based on negotiation mechanism until a deal was done [28]. As shown in Figure 9b, researchers developed market and auction-based approaches for task subdivision and allocation based on the Rubinstein negotiation protocol [28]. The advantage of such a protocol was that the auctioneer robot had to split the task into subtasks during the negotiation. In each round of negotiation, each robot initially started proposing the largest possible task allocation for itself and decreased its offers based on a negotiation of cost functions (discount factors) at each round until the other party indicated acceptance. With distributed task assignment under this protocol, each robot comes with a DDS, and each robot can dynamically adjust to the actual situation to get a suitable task. However, the task allocation result was generally an approximate optimal solution because of the discount factors, such as the area of the mission area, the distance of the robot from the goal, the area beyond the target, and the overlapping work area. Combined with the above task allocation technology, the research progress of agricultural multi-robots in task allocation technology in the past 10 years is summarized in Table 4.  
It can be seen from Table 5 that the current task allocation methods for agricultural multi-robots are mainly centralized, and most of them are implemented in the simulation experiment. The centralized task allocation mainly adopts the integer programming method, which describes the nature of the task allocation problem by establishing the objective function and constraints. Integral programming (IP) and mixed-integer programming (MIP) problems are an important branch in the field of operations research, which includes branch and bound method [89], cutting plane algorithm [90], graph theory method, etc [91]. The idea is to determine the transfer method and transfer relationship from one search point to another, and the result is a unique optimal solution, which is suitable for small scale; when the scale is extended, the computation is considerable and the computation time will be greatly increased [92]. Thus, the computation of the algorithm grows exponentially with
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 6 Conclusions  
According to the presented study, conclusions are drawn as follows.  
Figure 15: Influence of parameters of the random field on the stress reliability: (**a**) Discrete element; (**b**) correlation model; (**c**) correlation length.  
In previous reliability analysis of PSC bridges, the parameters affecting structural reliability are usually simulated as uncorrelated random variables while the spatial distribution of structural parameters (e.g., concrete elastic modulus and distributed load, etc.) is neglected. In this paper, a method was proposed for time-dependent reliability evaluation of PSC bridges, in which the influences of random field can be taken into account. Based on the random field theory and IS method, the proposed method keeps the balance between accuracy and efficiency, as demonstrated through three numerical examples.  
Concrete creep and shrinkage have a significant impact on the reliability index of the PSC box-girder bridge. Especially in the early stage after bridge completion, the reliability index decreases rapidly with time, which is consistent with the time-dependent development of shrinkage, creep, and steel relaxation. Before the reliability index of deflection falls below the target value of 1.5, some maintenance and rehabilitation measures (e.g., jacking back-up tendons) are required to restrain the long-term deflection and ensure the normal use of the bridge. During the whole service period, the reliability index of stress is larger than the target value of 4.2, indicating that the bridge satisfies the bearing capacity requirements.  
The number of discrete elements in the random fields and correlated models has little effect on the deflection and stress reliability index of the PSC box-girder bridge. However, the correlation length has a great influence on the reliability of the structure, and the reliability index decreases with the increasing of the correlation length. It should be noted that the correlation length was used as the assumed values in the reliability analysis in this paper. However, to obtain more precise results for the reliability analysis, the correlation length should be determined by the measured data to describe the random field. Moreover, the local average method of a random field is only applicable to the mesh of a uniform random field, but the discretization of a nonuniform random field deserves further discussion.  
Conceptualization, Z.C. and T.G.; Theoretical analysis and numerical modeling, Z.C.; Funding acquisition, Z.C. and T.G.; Project administration, Z.C. and T.G.; Software, Z.C.; Supervision, T.G. Validation, Z.C. Writing--original draft, Z.C.; Writing--review and editing, Z.C., S.L. and W.L.  
This research was funded by [National Natural Science Foundation of China] grant number [51908191], [Natural Science Foundation of Jiangsu Province, China] grant number [BK20191016].  
The authors declare no conflict of interest.