esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Incisor Position Modification with Torque Control  
Our review studies concluded some differences in the clinical outcomes achieved with different types of self-ligating brackets and conventional brackets [34; 35; 36; 37; 38].  
The conclusions of older studies regarding torque control in passive self-ligating brackets compared to conventional and active self-ligating brackets are varied. In a prospective clinical trial by [PERSON] et al. (2006) [68], it was found that passive self-ligating brackets were equally efficient as conventional brackets in delivering torque to maxillary incisors in both extraction and non-extraction cases. [PERSON] et al. (2008) [69] concluded that active self-ligating brackets were more effective in torque expression than passive self-ligating brackets, while [PERSON] et al. (2012) [70] found that the impact of the active or passive self-ligating mechanism or the ligature on torque control was minimal compared to slot dimensions.  
However, the differences are not always significant, and it appears that the choice of a bracket system may depend on the specific needs of the patient and the treatment goals.  
Further research is needed to fully understand the differences between passive self-ligating and conventional brackets or active self-ligating and to determine the most appropriate use of each system in clinical practice.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Auxiliary Data
#### 3.2.4 Statistical Data  
The Food and Agriculture Organization of the United Nations (FAO) publishes national catch fisheries and aquaculture statistics in their annual Yearbook of Fishery and Aquaculture Statistics [49], which are provided by national ministries and institutions. The Fisheries and Aquaculture Department of the FAO compiles and disseminates time series of fishery data, which are publicly available within its data collections on capture and aqua  
Figure 5: Comparison of coastline datasets for three coastal sections (**A–C**) along the shoreline in Guangdong Province, China. Background image: Median image derived from Sentinel-1 data.  
culture statistics. This FAO FishStat database provides these data, which are structured at a national level, providing a comprehensive overview of this sector. Aquaculture production statistics were harmonized and downloaded from the FAO Fishstat software [1], a global database with national-level production data that are submitted by FAO member countries (see Table 3).
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 4 Results and Discussion
### Evaluating the Proposed Receiver with LEO Satellite Communication
#### 4.3.1 Synchronization Algorithm Numerical Results  
In the following simulations, we consider the worst case scenario of LEO satellite communications, as proposed in Table 2.  
Figure 8 shows the estimation error of the fractional CFO \(\epsilon_{v}=|v-\hat{v}|\) for all possible _SF_s. It can be seen that the estimation of \(v\) is more precise for the lower \(SF\) (i.e., \(SF\in\{7,8,9\}\)) since a DR of 280 Hz/s does not affect the synchronization and the decoding performance of the latter _SF_s. In addition, the highest _SF_s have the lowest bin separations, which make them more sensitive to the fractional offsets and the DR.  
\begin{table}
\begin{tabular}{c c} \hline Carrier frequency \(f_{c}\) (MHz) & 868 \\ Maximum CFO \(\Delta f_{max}\) (kHz) & 50 \\ DR (Hz/s) & 280 \\ Transmitted power \(P_{Tx}\) (dBm) & 14 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 7: Doppler shift and DR evolution from an Eutelsat nano-satellite with a typical altitude of 550 km and given the carrier frequency of 868 MHz.  
In Figure 9, we represent the start of frame error \(\epsilon_{n_{s}}=|\frac{\pi_{n_{s}}+\lambda-(\theta_{s}+\lambda)}{M}|\) as a function of the SNR for each \(SF\). We notice that our time synchronization algorithm has good precision since, for instance, \(e_{n_{s}}=0.017\) (respectively, \(\epsilon_{n_{s}}=0.021\)) at the \(SNR\) sensitivity threshold (the \(SNR\) associated to a BER of \(10^{-5}\) in LoRa communication) [15] of \(SF=7\) (respectively, \(SF=12\)). In [21], we showed that DCSS can maintain good decoding performance for timing errors \(e_{n_{s}}\) of 0.25. Hence, given the time synchronization accuracy of our algorithm and the robustness of DCSS, we expect to have good decoding performance of our receiver.  
Figure 8: Fractional CFO estimation error \(\epsilon_{v}\).  
Figure 9: Beginning time estimation \(e_{n_{s}}\).
## 7 Conclusions  
The purpose of this study was to study the differences in gs changes, An and Tr in winter wheat under warming and water deficit conditions, and to simulate values using a calculation based on the Ball-Berry and Penman-Monteith models, and subsequently fit them. The purpose of this was to provide a clear understanding of photosynthesis in winter wheat under warming conditions (global warming). The results showed that temperature increase and water deficit decreased the gs, An and Tr of winter wheat, and also decreased the fitting effects of the measured values and simulated values. The fitting effect of the R-g treatment was 0.2% lower than that of N-g, and that of the R-d treatment was 1.5% higher than that of N-d. The fitting effect of the R-g treatment was 0.4% higher than that of R-d, and that of the N-g treatment was 2.2% higher than that of N-d. The simulated values of transpiration rate were obtained based on the Penman-Monteith model, and the fitting results with the measured values showed that warming and water deficit reduce the fitting effect of the two values. R-g was 1.2% lower than N-g, and R-d was 8.2% higher than N-d under increasing temperature. R-g was 8.0% higher than R-d, and N-g was 1.6% higher than N-d. It was found that the warming and water deficit conditions reduced the fitting effect of the simulated values. It is concluded that warming and drought stress will reducethe coupling effect of the photosynthetic rate and transpiration rate. This study's results will support the further prediction of winter wheat yield based on photosynthetic capacity and transpiration rate under climate change (global warming).  
Conceptualization, Y.G. and X.W.; methodology, Y.G.; software, Q.L. and J.L.; formal analysis, Q.L. and Y.G.; investigation, Q.L. and Y.F.; data curation, Q.L. and J.L.; writing--original draft preparation, Q.L. and A.K.M.H.; writing--review and editing, H.W. and Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51879267), the Ministry of Finance and Ministry of Agriculture and Rural Affairs of the People's Republic of China (CARS-03-19), and the Agricultural Science and Technology Innovation Program (ASTIP).  
Not applicable.  
Suggested Data Availability Statements are available in section \"MDPI Research Data Policies\" at [[https://www.mdpi.com/ethics](https://www.mdpi.com/ethics)]([https://www.mdpi.com/ethics](https://www.mdpi.com/ethics)) (accessed on 18 February 2023).  
The authors declare no conflict of interest.
## 1 Introduction  
ALE-1 is a micro-satellite jointly developed by Tohoku University and ALE Co., Ltd. (Tokyo, Japan), and it was launched by Japanese Epsilon Launch Vehicle No. 4 on 18 January 2019 as part of the first implementation of JAXA's Innovative Satellite Technology Demonstration Program [1, 2]. The mission of ALE-1 is to demonstrate the technology of artificial shooting star generation, and it is still operational in orbit. ALE-1 required a descent of about 100 km from the initial orbit altitude of about 500 km due to safety aspects in orbit, which was successfully accomplished with a passive drag sail de-orbit device called Separable De-Orbit Mechanism (SDOM). SDOM is a passive de-orbit system that uses atmospheric drag in the low Earth orbit, and it was jointly developed by Nakashima Engineering Works Ltd. and Tohoku University. Although it takes a long time to de-orbit, it is a simple thin-film deployment system that is actuated by mechanical strain energy, and it is expected to be a solution to the space debris problem, which has been a significant concern in recent years [3, 4].  
We have reported on the development and operational progress of SDOM and ALE-1 in various ways. In particular, in 2022, we published a paper on the status of system trouble that occurred in SDOM and its impact on the orbit descent [5]. Two of the four corners of the square-shaped drag sail of SDOM were unexpectedly loosened, resulting in a drag flag. This system trouble seemed to decrease SDOM's performance as a de-orbit mechanism, and we discussed the prediction of a possible significant delay in the orbit descent.  
Despite the trouble, however, SDOM was subsequently able to fulfill its role in orbit descent and revealed that the effect of the trouble was minimal. Moreover, SDOM was successfully separated from the ALE-1 satellite at the end of 2022 as intended, allowing the micro-satellite to stay in the target orbit for a longer time period. In this paper, we review the history and results of the orbital operation of ALE-1 in terms of various aspects and evaluate the descent performance of the SDOM based on the comparisons between orbit propagation simulations and the actual orbit transition record provided in the form of TLE (Two-Line Element) history.
## 5 Performance Evaluation
### Impact of Node as Expression of Energy Consumption  
This section presents a comparison between the energy consumption and the increase in SNs, as well as how much energy saving is achieved when using the proposed methodology. To calculate the amount of kilowatts per hour (kWh) in a continuous-operation day, the following formula was applied  
\[kWh=\frac{V\times I\times 24}{1000}, \tag{8}\]  
where V = 3.3 V, I = 70 mA for the IIoT-ESP8266, while for IIoT-RPi3 I = 350 mA.  
Figure 8 shows the distribution in an industrial warehouse without walls or obstacles for all CNs to establish connections to a centralized router. According to Figure 9a, the proposed algorithm presents greater energy savings as more SNs are added to the IIoT network, compared to a centralized topology. In the centralized or star topology, SNs were placed in a distance where all sensor nodes had the CN connection, while the proposed topology disperses the SNs throughout the industrial plant (Figure 6a).  
Figure 7: QoS and ND impact: (**a**) reliability requirements; and (**b**) node density.  
Therefore, the energy saving was 0.002 kWh with two SNs but it increased to 0.143 kWh with 64 SNs. This is expressed as a percentage of energy savings in Figure 9. Thereby, with a few nodes there was a saving of approximately 5% but with 64 nodes there was a saving of more than 20%.  
Figure 8: IISN distribution in an industrial plant to connect all sensor nodes in a centralized topology.  
Figure 9: Energy onsumption: (**a**) kilowatt consumption per day; and (**b**) energy saving percentage.  
The growth in energy savings is because the IIoT network can grow in a \(3^{n}\) way, and the proposed topology does not impact in a linear way but \(\log_{3}\), which balances energy consumption throughout the IIoT network, as if this topology understood where there is a need for more or less energy and balances it.  
Since the RRDLA [15], DETR [9], and REER [8] algorithms carried out simulations using the network simulator software ns-2 [33], these works did not report their energy consumption data, thus it is only possible to compare these algorithms with the present proposal in terms of reliability requirements or node density (as done in Section 5.2), and it is impossible to compare them in terms of energy efficiency as they are logical and not physical topologies. The present algorithm was simulated with the ns-2 software but was also physically configured in the room shown in Figure 6a.
###### Abstract  
The cultivated land in the black soil of Northeast China (BSNC), due to long-term high-input and high-output utilization, is facing a series of challenges such as soil erosion, compaction, and nutrient loss. However, the existing cultivated land quality evaluation (CLQE) lacks regional specificity, making it difficult to accurately reflect the cultivated land quality (CLQ) characteristics across different areas. Therefore, this study proposes a comprehensive evaluation framework that integrates both cultivated land functionality and degradation risk, establishing an assessment system consisting of 18 indicators to comprehensively evaluate the CLQ in the BSNC from multiple perspectives. The results indicate that the CLQ in the BSNC exhibits a declining trend from north to south, with second- and third-grade land dominating, accounting for 75.68% of the total cultivated land area. The overall cultivated land functionality increases from west to east, with the Liaohe Plain Region (LHP) performing the best. Low-risk cultivated land is primarily concentrated in the Songren Plain Region (SNP) and the Western Sandy Region (WS), covering 38.555% of the total cultivated land area. Additionally, this study finds a trade-off between the primary productivity function and the resource utilization efficiency function across different regions, while a synergistic relationship is observed between resource utilization efficiency and soil nutrient maintenance functions. This research emphasizes the necessity of balancing productivity and ecological protection to achieve the sustainable and efficient use of the BSNC.  
cultivated land quality evaluation; black soil of Northeast China; cultivated land function; degradation risk; trade-off; synergy +
Footnote †: journal: Applied Sciences
## 3 Results
### Inspecting the ANOVA  
The sonography scan's dominant factors were double-checked via the F test. Table 4 lists the confidence levels of the factors contributing to the sonography scan integrity. The dominant factors providing the highest contributions to the quality characteristic of the difference between the theoretical calculation and practical survey of 5.4 and 56.6% were A (angle of probe) and C (frame rate), although other factors provided 11.9% of the total contribution. This implies that some intrinsic factors have some influence on sonography performance. In addition, the data fluctuations related to their variation exceeded those of minor factors by 22.3%, according to \(\mathrm{SS_{total}}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Factor** & **SS** & **DOF** & **Contribution** & **Var** & \(F\) & **Probability** & **Confidence Level** & **Significance \({}^{*}\)** \\ \hline A & 0.0081 & 1 & 5.4\% & 0.0081 & 9.5347 & 0.39\% & 99.61\% & Yes \\ B & 0.0053 & 2 & 3.6\% & 0.0027 & 3.1643 & 5.42\% & 94.58\% & No \\ C & 0.0852 & 2 & 56.6\% & 0.0426 & 50.1001 & 0.00\% & 100.00\% & Yes \\ D & 0.0022 & 2 & 1.5\% & 0.0011 & 1.3147 & 28.11\% & 71.89\% & No \\ E & 0.0011 & 2 & 0.7\% & 0.0006 & 0.6502 & 52.80\% & 47.20\% & No \\ Others & 0.0178 & 8 & 11.9\% & 0.0022 & 2.6245 & 2.25\% & 97.75\% & No \\ Error & 0.0306 & 36 & 20.3\% & 0.0009 & & S = 0.029174761 & \\ Total & 0.1506 & 53 & 100.0\% & & \multicolumn{6}{c}{\({}^{*}\) Note: At least 99\% confidence level} \\ \end{tabular}
\end{table}
Table 4: Each factor’s confidence level was related to the sonography scan protocol effectiveness. Confidence levels exceeding 99% prove the factor’s significance.  
Figure 2: The respective three fish-bone plots of the sonography scan protocol. Apparently, factor C (frame rate) has the dominant contribution among all factors.
## 4 Results
### \(G/G_{max}\)-\(\gamma\) Curves
This is consistent with the findings in the literature [5,26,38]. It should be emphasized that a reduction in \(\gamma_{\rm r}\) is observed for the case of \(\sigma_{\rm c}\) = 900 kPa and \(D_{\rm r}\) = 30%. This is probably caused by the breakage of sand particles during the testing, and more fine contents are found at the end of testing. The influence of determination methods is consistent for all the \(\sigma_{\rm c}\) and \(D_{\rm r}\) cases. In particular, the \(\gamma_{\rm r}\) calculated by the autocorrelation function method is lower compared to the other methods while the hysteresis loop method and arithmetical average method capture almost the same \(\gamma_{\rm r}\) and, on the top of that, of the least square method. The percentage changes (\(\delta\)) in the normalized shear modulus are reported in Figure 12. The results of the least square method and autocorrelation function method are compared against ones of the hysteresis loop method. The results of shear strain less than 0.02% are not presented since the relatively small percentage changes are observed in this strain range. For \(D_{\rm r}\) = 30% and \(\sigma_{\rm c}\) = 50 kPa, the relative difference for the two determination methods increases with increasing shear strain, with the maximum \(\delta\) of about 20% for the autocorrelation function method and about 10% for the least square method. As \(\sigma_{\rm c}\) increases, the relative difference appears to decrease. This follows the same trend observed for the \(G\) shown in Figure 8. For \(\sigma_{\rm c}\) = 900 kPa, the percentage changes at \(\gamma\) = 1% of two determination methods decrease to around 5%. In addition, similar trends are captured for the cases of \(D_{\rm r}\) = 50% and 70%. Compared to \(\sigma_{\rm c}\), \(D_{\rm r}\) is found to be the second factor that affects the uncertainties introduced by different determination methods.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model
### Data Preprocessing
#### 2.2.4 Sample Making  
After the previous stationarity test, difference processing, the pure randomness test and TEC data normalization processing, the experimental samples are prepared next. This paper selects the TEC data from 0:00 on 1 January 2002 to 24:00 on 31 December 2011 in eight Chinese regions, and the total number of observation points at each region is 47476. After the first-order differential processing, it becomes 47475. A segmentation method with a sliding window of 13 + \(p\) (\(p\) indicates the number of samples to be predicted in the future) is used to make the normalized data into samples. The sample making process is shown in Figure 4. \(x_{i}\) is the input of sample \(i\), including 13 observations, as shown by the orange data in Figure 4. \(y_{i}\) is the output of the sample \(i\). When predicting the data within \(t\) hours in the future, \(y_{i}\) contains \(t\) / 2 points, as shown by the blue points in Figure 3 (this paper is  
Figure 3: TEC values after first-order difference.  
predicting TEC data for the next two hours, so \(t=2\) and \(p=1\)). The whole experiment is shown in Figure 5.
## 3 Code Synchronization Algorithm Based on the Segment Correlation  
First, the overall design scheme and block diagram of the code synchronization module are described in this section. Then code acquisition is described in detail, while existing problems of traditional series code acquisition methods are pointed out and corrected, and a novel series code acquisition method based on the segment correlation is implemented. Finally, the code tracking module is described.
## 1 Introduction
The inverter-fed voltage is full of harmonics, which makes the frequency spectrum hazy to understand. Moreover, the drive control algorithms can also have an impact on the amplitude of the harmonics. For example, in the case of direct torque control (DTC) motors, the drive directly influences the current signal carrying all the information about the motor's health [14]. Researchers have tried several different techniques to cope with these problems. The use of Hilbert transforms to extract the signal's envelope, which possesses considerable information regarding the electrical machine's health, can be found in [15]. The authors in [16] used a fractional Fourier transform to recover the faulty frequencies from a non-stationary signal. In [17], the authors used a sliding discrete Fourier transform for the detection of broken rotor bars, while [18; 19; 20; 21] used the wavelet technique to improve the accuracy. These techniques' capability to handle nonstationary signals opens a new paradigm in the field of fault diagnostics [22; 23; 24]. However, the poor time-frequency resolution is still a challenge. This paper presents the following attractive features:  
* For studying the impact of broken bars on different performance parameters, a WFA-based analytical model is developed. This model takes less simulation time to study the effect of low voltage and asymmetry on the machine's performance. It also helps to plot the faulty theoretical patterns used as a benchmark for differentiation among fault and spatial harmonics;
* Unlike most of the papers where a high inertia load or variable transformer is used, an industrial inverter-based low voltage test to extend the motor's transient interval is proposed. It is also described how an industrial inverter can reduce the voltage while maintaining a constant rated frequency. Increasing the transient length of the motor startup current improves the time-frequency resolution of the spectrum;An algorithm to improve the spectrum's legibility is proposed, which helps segregate various frequency patterns in the transient regime;
* The wavelet transform is preferred over the short-time Fourier transform (STFT) to avoid inherited FFT drawbacks. Moreover, a band-stop infinite impulse response (IIR) filter is used to attenuate the fundamental component, which improves the spectrum's legibility;
* It is proposed that the selection of time-frequency regions with a 95% confidence interval (CI) in the form of a contour plot gives a more unambiguous indication of faulty patterns. By adjusting the level of the CI, the spatial and switching frequency-based patterns can be avoided. To the best of the author's knowledge, this technique is not presented in the literature so far.