esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Emerging Area or Potential Opportunity in Other Applications  
In the literature review, more than 18 papers on RFID applications in nonmanufacturing industries were collected, and four other clusters of research themes were discovered (Table 2). These clusters, listed in order of descending frequency, represent RFID applications in construction material management, beehive model experiments, erratic tracking experiments, and aquaculture. Because few studies have been published in these fields, not all the papers were present in the main path. Additionally, papers conducting literature reviews in these fields only reported the development progress of specific fields. Although these papers contributed and added value to these fields, the present study only considered influential papers and research fields with many publications (Table 2).  
The literature review revealed that in construction material management, RFID applications have provided support in material planning, procurement, storage, processing, distribution, land use, construction monitoring, and progress reporting, thereby enhancing construction efficiency and preventing over-expenditure and construction delays. In beehive model experiments, RFID has been used to obtain a four-dimensional behavioral model of bees entering and exiting a beehive. In papers discussing the mutual interactions between wave activity and rocky coasts, RFID tags and localization systems have been employed to accurately measure the displacement of ocean sediments. The fewest papers have been published on RFID applications in aquaculture because the use of RFID in feeding management is currently a novel application.  
RFID technology can be used to automatically determine the health and vitality of livestock, after which their feeding can be altered to achieve the optimal allocation of feed resources. In other clusters, a few papers have been published on RFID applications in feeding management. The trend analysis revealed that in research on electronic antennas and tag technologies, RFID applications are no longer the overall development trend; RFID has been widely applied in various new applications.
## 1 Introduction
Thus, the derivative MF filters are removed, and the timing error is just estimated by the on-time MF filter banks without using early- and late-MF filter banks. Then, the MF filter banks can be reduced to 1/3 of the original EL-based method. To prove the stability of the proposed synchronizer, we plot the S-curve through theoretical and numerical analysis. Three commonly used multi-\(h\) CPM schemes, quaternary CPMs with \(h=\{\frac{4}{16},\frac{5}{16}\}\), \(\{\frac{5}{16},\frac{6}{6}\}\), and \(\{\frac{9}{16},\frac{10}{16}\}\), are considered to demonstrate the performance of the LPA-based synchronization method. We implement the overall receiver for the above three multi-\(h\) CPM schemes with LPA-based synchronization and MLSD on a Xilinx Kintex-7 field-programmable gate array (FPGA) platform. The tested bit error rate (BER) results show that the proposed synchronizer's performance has no loss compared with the conventional EL methods, and the slice and dedicated resource (DSPs and block RAMs) utilization are reduced by about 27% and 67%, respectively. The main contributions of this work are briefly summarized as follows:  
* Using LPA, we rederive the ML estimation of phase and timing error signal for multi-\(h\) CPM, which reduces the complexity of the synchronizer. We modify the LPA-based timing error detector to reduce the complexity further with the sign of the detected symbol. It is friendly to being implemented on FPGA;
* We provide an analytical expression for the S-curve of the proposed error signal detector and analyze its stability through the S-curve;
* We provide an architecture of the receiver with the LPA-based synchronizer and implement it for the three promising multi-\(h\) CPM schemes on an FPGA platform. The verification results demonstrate a better tradeoff between complexity and performance than the conventional EL-based method. The structure of this paper is as follows. The signal model and the derivation of the traditional synchronization algorithm and the LPA synchronization algorithm are presented in Section 2. To further reduce the LPA error signal detector, we continue to simplify the detector with the polarization of the error signal. The S-curve is derived to determine the stability of the proposed synchronization algorithm in Section 2. In Section 3, we provide the implementation details based on the receiver's diagram with the LPA-based synchronization and compare the complexity between the LPA-based algorithm and the conventional EL-based algorithm. Finally, Section 4 illustrates the onboard BER test with the low-complexity receiver for the three multi-\(h\) CPM schemes to demonstrate the performance of the proposed synchronization algorithm.
# A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)
## 4 Materials and Methods--Implementation of the EVRPD Model
### Dedicated Genetic Algorithm
#### 4.2.5 Genetic Operators  
Traditional genetic operators were used in the DGA--tournament selection, one-point crossover, and mutation. For tournament selection, individuals from the current population were randomly selected into groups of two (tournament size equal to two). The number of groups for the selection must be equal to the size of the population. The selection operator chose one individual from each group (the algorithm worked on a fixed number of individuals in the population). For the individual to be placed in a mating pool, its objective function value must be lower than that of the other individuals in the group. In the next step, mating pairs were randomly selected from the mating pool to undergo a one-point crossover in accordance with the set crossover probability (a constant moderate level of the crossover rate was adopted in this study). In this case, the crossover involved the exchange of fragments between parent individuals at a randomly generated crossover point. The new individuals (two new individuals are produced for each pair of parents) satisfied the problem assumptions. The new individuals were subject to uniform mutation according to the set moderate level of mutation probability. The practical implementation of the mutation operator in the presented problem consisted of the random selection of a parcel and then the random generation of a drone number for the parcel.  
The genetic algorithm terminated when it reached the number of iterations set by the program's user, or an additional stop criterion was defined by the lack of improvement in the quality of the best individual in the population in the next 50 generations.  
In this study, the population size was selected empirically with respect to the dimensions of the problem. However, some assumptions were made. The population could not be less than the number of customers (c\(\in\)C). However, the number of generations was not less than twice the number of customers. For smaller data instances, these values were greater than these thresholds, and for smaller ones, they were close to them. The remaining values of the parameters of the genetic algorithm were also experimentally determined, inspired by the research described in [53]. Thus, the crossover probability was set at 0.5 and the mutation probability at 0.1.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 4 Constrained Stochastic Game
### Optimization Formulation
The expected transmission power of IoT device \(i\) can be obtained from \(\overline{A_{i}}=\sum\limits_{S_{i}}\sum\limits_{A_{i}}A_{i}\phi_{i,\pi_{-i}} (S_{i},A_{i})\). After receiving the expected transmission power from other IoTdevices, each IoT device calculates the aggregated data rate of IoT devices and solves the LP problem to obtain the optimal policy \(\pi_{i}^{*}\) (lines 6-7 in Algorithm 1). The algorithm is finished when the policies of all IoT devices converge. ```
1:Initialize the policy \(\pi_{i}\) for \(\forall i\)
2:Share the channel gain among IoT devices
3:repeat
4:for All IoT devices \(i\)do
5:Transmit the expected transmission power \(\overline{P_{i}}\) to other IoT devices
6:Calculate the aggregated data rate of IoT devices
7:Solve the LP problem to obtain the optimal policy \(\pi_{i}^{*}\)
8:endfor
9:until Convergence of the policies for all IoT devices
```  
**Algorithm 1**Best response dynamics-based algorithm. Because the complexity of solving the LP problem is not high (e.g., \(O((|S_{i}||A_{i}|)^{3})\)[29] for the [PERSON]'s algorithm which is one of the representative LP solvers) and the stationary policies can be achieved with few iterations (see Section 5.1), the proposed algorithm can be realized without a huge overhead.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments
### Three-Line Segment Model
#### 5.1.1 Case 1  
If the fault \(F_{1}\) occurs at \(t~{}=~{}0\) in the first section (overhead segment) of dc-link as shown in Figure 4, then the arrival time of TW at terminal \(T_{1}\) is given by:  
\[t_{\text{1--F1}}=\frac{x_{F1}}{v_{1}} \tag{9}\]  
where \(v_{1}\) is the propagation velocity of traveling wave. The arrival time of traveling wave at terminal \(T_{2}\) is given by:  
\[t_{\text{2--F1}}=\frac{L_{1}-x_{F1}}{v_{1}}+\frac{L_{2}}{v_{2}}+\frac{L_{3}}{ v_{3}} \tag{10}\]  
where \(v_{2}\) and \(v_{3}\) are the propagation velocities of traveling wave in segments \(L_{2}\) and \(L_{3}\), respectively. And the difference between arrival times at terminal \(T_{1}\) and \(T_{2}\) is given by:  
\[\Delta t_{\text{12--F1}}=t_{\text{1--F1}}-t_{\text{2--F1}}\]  
\[\Delta t_{\text{12--F1}}=\frac{2\ x_{F1}}{v_{1}}-\frac{L_{1}}{v_{1}}-\frac{L_{ 2}}{v_{2}}-\frac{L_{3}}{v_{3}} \tag{11}\]  
The distance from the fault point to the start of segment \(L_{1}\) is given by:  
\[x_{F1}=\left(\Delta t_{\text{12--F1}}+\frac{L_{1}}{v_{1}}+\frac{L_{2}}{v_{2}}+ \frac{L_{3}}{v_{3}}\right)\times\frac{v_{1}}{2} \tag{12}\]  
Figure 4: The direct current (dc)-link model with two overhead lines and one cable segment.
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 3 System Architecture for Non-Contact High Voltage Measurement
### Integration of the High Voltage Measurement Module with the Online PD Monitoring System
An example of the operation of such a two-stage filtration is shown in Figure 10. In the case of a highly noisy signal or when the signal-to-noise ratio (SNR) is low, the filtration efficiency can be increased by applying a wavelet de-noising function on the first stage of the sequential filter. By default, using the _sym8_ wavelet, a five-level wavelet decomposition of the signal is performed, and then at each of these levels, the signal is de-noised using the soft-thresholding technique introduced by [PERSON] and [PERSON] [34; 35].
Sensors_**2010**, _10_, 4521-4540; doi:10.3390/s100504521  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks  
**[PERSON] 1,2-*, [PERSON] 3,4, [PERSON] 4 and [PERSON] 1**  
1 School of Communication and Control Engineering, Jinangnan University, Wuxi, 214122, China; E-Mail: [EMAIL_ADDRESS]  
2 College of Electrical Engineering, Guizhou University, Guiyang, 550003, China  
3 Department of Electrical and Electronic Engineering, Imperial College London, London, UK; E-Mail: [EMAIL_ADDRESS]  
4 Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China; E-Mail: [PERSON]ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] or [EMAIL_ADDRESS].  
_Received: 4 March 2010; in revised form: 14 April 2010 / Accepted: 16 April 2010 / Published: 4 May 2010_  
**Abstract:** For monitoring burst events in a kind of reactive wireless sensor networks (WSNs), a multipath routing protocol (MRP) based on dynamic clustering and ant colony optimization (ACO) is proposed. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length) were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH) is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.  
**Keywords:** wireless sensor networks (WSNs); clustering; multipath; ant colony optimization (ACO)
## 3 Results
### Group Characterization of Bacterial Secretions  
In order to confirm if the Pa strain produced biosurfactant, we identified its secretions with FTIR and HPLC-MS. FTIR displayed the absorption band of -OH stretch at 3406 cm\({}^{-1}\). Those at 2925 and 2860 cm\({}^{-1}\) were caused by the C-H expansion vibration. The carbonyl and carboxyl expansion vibration at 1737 and 1645 cm\({}^{-1}\) indicated the existence of lipids, and peaks at 1457 and 1385 cm\({}^{-1}\) suggested the bending of C-H (-CH\({}_{3}\) and -CH\({}_{2}\)). The absorption peak at 1173 cm\({}^{-1}\) was the stretching vibration peak of C-O (Figure S5). The above estimators suggested that the secretions of the Pa strain were composed of glycolipids.  
These compounds had their peaks at 11.01 and 27.35 min, indicating that their molecular weights were 527 and 334, respectively. This indicated that these two compounds were single rhamnpid  
Figure 5: Differences in surface tension (**a**), hydrophobicity (**b**), and emulsifiability (**c**) between the two strains, Kp and Pa.  
Rha-C12:1-C10 and Rha-C10 [65] (Figures 6 and 7a,b). The molecular weights of the compounds observed at 13.56 and 16 min were 553 and 555, respectively, indicating that these two compounds were double rhamnolipid Rha-Rha-C10:1 and Rha-Rha-C10, respectively (Figures 6 and 7c,d). The peak at 27.35 min represented Rha-C10 (namely 2-O-\(\alpha\)-L-rhamyl-\(\beta\)-hydroxydecyl acid; molecular weight: 334), one of the most common structures in rhamnolipids (Figure 6). Among these homologues, the mono-rhamnolipid structure Rha-C12:1-C10 with the RT of 11.01 min had the highest content.  
These results suggested that the Pa strain might produce biosurfactant, which was identified as rhamnolipids with the highest content of mono-rhamnolipid structure. This biosurfactant enhances the surface hydrophobicity of the strain and improved the emulsification of the solution. Meanwhile, there was no evidence showing that the Kp strain would produce any biosurfactant.  
Figure 6: HPLC-MS analysis of Pa secretion.
## 2 Statement of the Research Problem and Measurement Tools
Figure 4: Design of single-thread constant-temperature hot-wire anemometer sensor for determining air flow velocity: 1—sensor base; 2—ceramic insert; 3—conductive holder; 4—thread (sensitive element); and 5—vertical diffuser. The total number of measurement points was 43. Instantaneous values of air flow velocity \(w_{x}\) were determined at each point using the measurement system described above. Measurements were carried out sequentially in the stationary air flow mode. The average flow velocity \(w\) was calculated as the mathematical expectation of the function \(w_{x}=f\left(\tau\right)\) for all 43 measuring points. The initial flow velocity \(w_{0}\) in the diffuser was calculated by determining the arithmetic mean of the velocities in the first control section (three measuring points at a height of \(h=50\) mm). Spectral analysis of the functions \(w_{x}=f\left(\tau\right)\) was carried out to clarify the gas-dynamic characteristics of the flow in the diffuser using the Scilab version 6.1.1 (France) program. Discrete Fourier transform was used. Each discrete frequency \(f_{n}\) was calculated using the following formula:  
\[f_{n}=\frac{n}{N\Delta t}\, \tag{1}\]  
Figure 5: Installation locations for constant-temperature hot-wire anemometer sensors in flat vertical diffuser (43 measuring points). where \(N\) is the number of points in the studied data series; \(\Delta t\) is the time interval between two consecutive points in the data series; and \(n\) is the index that denotes the discrete frequency number (\(n\) = 0, 1, 2, \(\ldots\), \(N-1\)). The spectral component \(X_{n}\) for each frequency was calculated using the following formula:  
\[X_{n}=\ \sum_{q\ =\ 0}^{N-1}x_{q}\ \exp\biggl{(}-j\ \frac{2\pi nq}{N}\biggr{)}\, \tag{2}\]  
where \(q\) is the number of the point in the data series (\(q\) = 0, 1, 2, \(\ldots\), \(N-1\)); \(x_{q}\) is the point of the data series; and \(j\) is the imaginary unit. The first half of the spectrum was considered, which corresponded to positive frequencies (\(n\leq N/2\)). The spectral component (2) was normalized by the following formula to obtain the signal amplitude (\(A_{w}\)):  
\[A_{w}=\ \frac{2}{N}X_{n}. \tag{3}\]  
The assessment of the pulsating component of the air flow was carried out by determining the turbulence intensity TI for each measuring point. TI was calculated as the ratio of the mean square pulsating component of the velocity to the average velocity of the flow under study. The method for calculating TI is described in detail in the article [36]. Thus, an experimental stand was created to study the gas-dynamic characteristics of a stationary air flow in a vertical, flat, conical diffuser. A measuring system with the required accuracy and speed was selected to obtain information about the gas-dynamic features of flows in the system under consideration. This design, including geometric dimensions and physical processes, is characteristic of flow gasifiers for wood waste.
## 4 Experimental Validation  
### Dataset Description  
Figure 9 displays the four-point bending test from which the dataset used here was collected. Our proposed method is validated on AE data acquired by AE sensors at the sampling frequency of 5 MHz. The duration of each degradation cycle is set at 1 s. For each four-point bending test, a total of eight sensors are positioned on the RC beam to collect data in the run-to-failure process.  
Twenty-four run-to-failure process signals are acquired during three four-point bending tests to validate the proposed approach. Details about the dataset are listed in Table 1. The data is divided into two equal parts for training (signals from sensors 1-4) and test sets (signals from sensors 5-8) formation. Figure 10 shows the degradation process of three test run-to-failure sensor signals (i.e., sensor 5), one from each of the concrete beams, in terms of RMS, kurtosis values, and AE hits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Bending Test** & **Concrete Beam** & **No. Sensors** (Run-to-Fail** & **Signal Length** (s) \\ \hline Training dataset & 1 & A & 4 & 600 \\  & 2 & B & 4 & 650 \\  & 3 & C & 4 & 620 \\  & 1 & A & 4 & 600 \\ Test dataset & 2 & B & 4 & 650 \\  & 3 & C & 4 & 620 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifics of the experimental dataset.  
Figure 9: Pictogram of the four-point bending test.
# Effects of Melatonin Treatment of Postharvest Pear Fruit on Aromatic Volatile Biosynthesis
## 2 Results
### Effects of Melatonin on Expression of Ester-related Genes  
q-PCR was used to analyze the expression of the main enzyme encoding genes for aroma synthesis. We found that MT downregulated _PbLOX2_ IAT in the two varieties, and downregulated _PbLOX1_ in 'Abbe Fetel' IAT and at PRC. In 'Korla', _PbHDL_ was upregulated by MT IAT, while in 'Abbe Fetel', MT upregulated _PbHDL_ at PRC. In 'Abbe Fetel', MT upregulated _PbADH1_, _PbADH2_, _PbADH3_, and _PbAAT_ IAT. However, this difference was not significant in 'Korla', which also proved that the increased activity of AAT and ADH was regulated by the encoding enzyme genes (Figure 5).  
Figure 4: Effect of melatonin on LOX, HPL, ADH, and AAT activity. Data are means \(\pm\) SE of three replicates. * \(p<0.05\), ** \(p<0.01\).  
Figure 5: Effect of melatonin on relative expression of ester-related gene. Levels of gene expression at the first sampling point under control conditions was normalized as 1.0. Data are means \(\pm\) SD of three replicates. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\).
###### Abstract  
Free-space optical communication has emerged as a pivotal technology for space-to-ground downlinks; however, signal degradation caused by atmospheric turbulence continues to pose a significant challenge. In this study, a model for the polarization transmission characteristics of a Gaussian-Shell model (GSM) beam in downlink was established, and conditions sufficient for maintaining the polarization transmission characteristics were derived. The impact of the source spatial coherence on the performance of optical communication systems using circular polarization shift keying (CPoISK) modulation was investigated. Additionally, models for the probability density distribution and scintillation index of the optical intensity under atmospheric turbulence were developed along with a bit error rate model for the optical communication system. The effects of the laser spatial coherence on these models were also analyzed. The results indicate that the optimal performance in the turbulent downlink is achieved with fully coherent light, where the GSM-beam-based CPoISK-modulated system demonstrates a reduction of 1.51 dB in the required power compared to that of an on-off keying system. The implications of this study suggest that optimizing spatial coherence could significantly enhance the reliability of space-to-ground communication systems under atmospheric disturbances.  
G 2025 12 643 1
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 4 Experimental Results
### Results and Discussion  
#### 4.3.1 Sky Detection  
With the validation dataset, we compare the detection accuracy between using [PERSON]'s method [32] and the Mask R-CNN method. The detection accuracy is shown in Table 1. The Mask R-CNN achieved 91.67% accuracy. It has much better performance than [PERSON]'s method. From this result, we can see the the Mask R-CNN method is very powerful to separate the desired object from the natural background.